Journal papers
ABSTRACT
Background: With the advance in single-cell RNA sequencing (scRNA-seq) technol‑ogy, deriving inherent biological system information from expression profles at a single-cell resolution has become possible. It has been known that network modeling by estimating the associations between genes could better reveal dynamic changes in biological systems. However, accurately constructing a single-cell network (SCN) to capture the network architecture of each cell and further explore cell-to-cell heterogeneity remains challenging.
Results: We introduce SINUM, a method for constructing the SInglecell Network Using Mutual information, which estimates mutual information between any two genes from scRNA-seq data to determine whether they are dependent or independent in a specifc cell. Experiments on various scRNA-seq datasets with diferent cell numbers based on eight performance indexes (e.g., adjusted rand index and F-measure index) validated the accuracy and robustness of SINUM in cell type identifcation, superior to the state-of-the-art SCN inference method. Additionally, the SINUM SCNs exhibit high overlap with the human interactome and possess the scale-free property.
Conclusions: SINUM presents a view of biological systems at the network level
to detect cell-type marker genes/gene pairs and investigate time-dependent changes in gene associations during embryo development. Codes for SINUM are freely available at https://github.com/SysMednet/SINUM.
Keywords: Single-cell analysis, Network inference, Mutual information, Gene expression, Single-cell network, Single-cell clustering
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ABSTRACT
The management of chronic myelogenous leukemia (CML) has seen significant progress with the introduction of tyrosine kinase inhibitors (TKIs), particularly Imatinib. However, a notable proportion of CML patients develop resistance to Imatinib, often due to the persistence of leukemia stem cells and resistance mechanisms independent of BCR::ABL1 This study investigates the roles of IL6R, IL7R, and MYC in Imatinib resistance by employing CRISPR/Cas9 for gene editing and the Non-Invasive Apoptosis Detection Sensor version 2 (NIADS v2) for apoptosis assessment. The results indicate that Imatinib-resistant K562 cells (K562-IR) predominantly express IL6R, IL7R, and MYC, with IL6R and MYC playing crucial roles in cell survival and sensitivity to Imatinib. Conversely, IL7R does not significantly impact cytotoxicity, either alone or in combination with Imatinib. Further genetic editing experiments confirm the protective functions of IL6R and MYC in K562-IR cells, suggesting their potential as therapeutic targets for overcoming Imatinib resistance in CML. This study contributes to understanding the mechanisms of Imatinib resistance in CML, proposing IL6R and MYC as pivotal targets for therapeutic strategies. Moreover, the utilization of NIADS v2 enhances our capability to analyze apoptosis and drug responses, contributing to a deeper understanding of CML pathogenesis and treatment options.
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ABSTRACT
Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics. Here, we propose a gene set correlation enrichment analysis method, Gscore, based on an expression dataset-derived coexpression network to examine whether a differentially expressed gene (DEG) list (or each of its DEGs) is associated with a known gene set. Gscore is better able to identify target pathways in 89 human disease expression datasets than eight other state-of-the-art methods and offers insight into how disease-wide and pathway-wide associations reflect clinical outcomes. When applied to RNA-seq data from COVID-19-related cells and patient samples, Gscore provided a means for studying how DEGs are implicated in COVID-19-related pathways. In summary, Gscore offers a powerful analytical approach for annotating individual DEGs, DEG lists, and genome-wide expression profiles based on existing biological knowledge.
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ABSTRACT
Head and neck squamous cell carcinoma (HNSC) exhibits genetic heterogeneity in etiologies, tumor sites, and biological processes, which significantly impact therapeutic strategies and prognosis. While the influence of human papillomavirus on clinical outcomes is established, the molecular subtypes determining additional treatment options for HNSC remain unclear and inconsistent. This study aims to identify distinct HNSC molecular subtypes to enhance diagnosis and prognosis accuracy. In this study, we collected three HNSC microarrays (n = 306) from the Gene Expression Omnibus (GEO), and HNSC RNA-Seq data (n = 566) from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes (DEGs) and validate our results. Two scoring methods, representative score (RS) and perturbative score (PS), were developed for DEGs to summarize their possible activation functions and influence in tumorigenesis. Based on the RS and PS scoring, we selected candidate genes to cluster TCGA samples for the identification of molecular subtypes in HNSC. We have identified 289 up-regulated DEGs and selected 88 genes (called HNSC88) using the RS and PS scoring methods. Based on HNSC88 and TCGA samples, we determined three HNSC subtypes, including one HPV-associated subtype, and two HPV-negative subtypes. One of the HPV-negative subtypes showed a relationship to smoking behavior, while the other exhibited high expression in tumor immune response. The Kaplan–Meier method was used to compare overall survival among the three subtypes. The HPV-associated subtype showed a better prognosis compared to the other two HPV-negative subtypes (log rank, p = 0.0092 and 0.0001; hazard ratio, 1.36 and 1.39). Additionally, within the HPV-negative group, the smoking-related subgroup exhibited worse prognosis compared to the subgroup with high expression in immune response (log rank, p = 0.039; hazard ratio, 1.53). The HNSC88 not only enables the identification of HPV-associated subtypes, but also proposes two potential HPV-negative subtypes with distinct prognoses and molecular signatures. This study provides valuable strategies for summarizing the roles and influences of genes in tumorigenesis for identifying molecular signatures and subtypes of HNSC.
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ABSTRACT
Analyzing multiple networks is important to understand relevant features among different networks. Although many studies have been conducted for that purpose, not much attention has been paid to the analysis of attractors (i.e., steady states) in multiple networks. Therefore, we study common attractors and similar attractors in multiple networks to uncover hidden similarities and differences among networks using Boolean networks (BNs), where BNs have been used as a mathematical model of genetic networks and neural networks. We define three problems on detecting common attractors and similar attractors, and theoretically analyze the expected number of such objects for random BNs, where we assume that given networks have the same set of nodes (i.e., genes). We also present four methods for solving these problems. Computational experiments on randomly generated BNs are performed to demonstrate the efficiency of our proposed methods. In addition, experiments on a practical biological system, a BN model of the TGF- β signaling pathway, are performed. The result suggests that common attractors and similar attractors are useful for exploring tumor heterogeneity and homogeneity in eight cancers.
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ABSTRACT
As a type of immunogenic cell death, ferroptosis participates in the creation of immunoactive tumor microenvironments. However, knowledge of spatial location of tumor cells with ferroptosis signature in tumor environments and the role of ferroptotic stress in inducing the expression of immune-related molecules in cancer cells is limited. Here the spatial association of the transcriptomic signatures is demonstrated for ferroptosis and inflammation/immune activation located in the invasive front of head and neck squamous cell carcinoma (HNSCC). The association between ferroptosis signature and inflammation/immune activation is more prominent in HPV-negative HNSCC compared to HPV-positive ones. Ferroptotic stress induces PD-L1 expression through reactive oxygen species (ROS)-elicited NF-κB signaling pathway and calcium influx. Priming murine HNSCC with the ferroptosis inducer sensitizes tumors to anti-PD-L1 antibody treatment. A positive correlation between the ferroptosis signature and the active immune cell profile is shown in the HNSCC samples. This study reveals a subgroup of ferroptotic HNSCC with immune-active signatures and indicates the potential of priming HNSCC with ferroptosis inducers to increase the antitumor efficacy of immune checkpoint inhibitors.
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ABSTRACT
Erinacines derived from Hericium erinaceus have been shown to possess various health benefits including neuroprotective effect against neurodegenerative diseases, yet the underlying mechanism remains unknown. Here we found that erinacine S enhances neurite outgrowth in a cell autonomous fashion. It promotes post-injury axon regeneration of PNS neurons and enhances regeneration on inhibitory substrates of CNS neurons. Using RNA-seq and bioinformatic analyses, erinacine S was found to cause the accumulation of neurosteroids in neurons. ELISA and neurosteroidogenesis inhibitor assays were performed to validate this effect. This research uncovers a previously unknown effect of erinacine S on raising the level of neurosteroids.
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ABSTRACT
Recently, extracting inherent biological system information (e.g. cellular networks) from genome-wide expression profiles for developing personalized diagnostic and therapeutic strategies has become increasingly important. However, accurately constructing single-sample networks (SINs) to capture individual characteristics and heterogeneity in disease remains challenging. Here, we propose a sample-specific-weighted correlation network (SWEET) method to model SINs by integrating the genome-wide sample-to-sample correlation (i.e. sample weights) with the differential network between perturbed and aggregate networks. For a group of samples, the genome-wide sample weights can be assessed without prior knowledge of intrinsic subpopulations to address the network edge number bias caused by sample size differences. Compared with the state-of-the-art SIN inference methods, the SWEET SINs in 16 cancers more likely fit the scale-free property, display higher overlap with the human interactomes and perform better in identifying three types of cancer-related genes. Moreover, integrating SWEET SINs with a network proximity measure facilitates characterizing individual features and therapy in diseases, such as somatic mutation, mut-driver and essential genes. Biological experiments further validated two candidate repurposable drugs, albendazole for head and neck squamous cell carcinoma (HNSCC) and lung adenocarcinoma (LUAD) and encorafenib for HNSCC. By applying SWEET, we also identified two possible LUAD subtypes that exhibit distinct clinical features and molecular mechanisms. Overall, the SWEET method complements current SIN inference and analysis methods and presents a view of biological systems at the network level to offer numerous clues for further investigation and clinical translation in network medicine and precision medicine.
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ABSTRACT
Squamous and anaplastic thyroid cancers are the most aggressive and life‐threatening cancer types in humans, with the involvement of lymph nodes in 59% of cases and distant metastases in 26% of cases of all thyroid cancers. The median survival of squamous thyroid cancer patients is < 8 months and therefore is of high clinical concern. Here, we show that both VEGFC and VEGFR2/KDR are overexpressed in thyroid cancers, indicating that VEGF/VEGFR signaling plays a carcinogenic role in thyroid cancer development. Using CRISPR/Cas9, we established a KDR knockout (KO) SW579 squamous thyroid cancer cell line that exhibited dramatically decreased colony formation and invasion abilities (30% and 60% reduction, respectively) when compared to scrambled control cells. To validate the potential of KDR as a therapeutic target for thyroid cancers, we used the KDR RTK inhibitor sunitinib. Protein analysis and live/dead assay were performed to demonstrate that sunitinib significantly inhibited cell growth signal transduction and induced cell apoptosis of SW579 cells. These results suggest that selective targeting of KDR may have potential for development into novel anti‐cancer therapies to suppress VEGF/VEGFR‐mediated cancer development in patients with clinical advanced thyroid cancer.
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ABSTRACT
Thyroid cancer is the most common endocrine malignancy. Patients with well-differentiated thyroid cancers, such as papillary and follicular cancers, have a favorable prognosis. However, poorly differentiated thyroid cancers, such as medullary, squamous and anaplastic advanced thyroid cancers, are very aggressive and insensitive to radioiodine treatment. Thus, novel therapies that attenuate metastasis are urgently needed. We found that both PDGFC and PDGFRA are predominantly expressed in thyroid cancers and that the survival rate is significantly lower in patients with high PDGFRA expression. This finding indicates the important role of PDGF/PDGFR signaling in thyroid cancer development. Next, we established a SW579 squamous thyroid cancer cell line with 95.6% PDGFRA gene insertion and deletions (indels) through CRISPR/Cas9. Protein and invasion analysis showed a dramatic loss in EMT marker expression and metastatic ability. Furthermore, xenograft tumors derived from PDGFRA gene-edited SW579 cells exhibited a minor decrease in tumor growth. However, distant lung metastasis was completely abolished upon PDGFRA gene editing, implying that PDGFRA could be an effective target to inhibit distant metastasis in advanced thyroid cancers. To translate this finding to the clinic, we used the most relevant multikinase inhibitor, imatinib, to inhibit PDGFRA signaling. The results showed that imatinib significantly suppressed cell growth, induced cell cycle arrest and cell death in SW579 cells. Our developed noninvasive apoptosis detection sensor (NIADS) indicated that imatinib induced cell apoptosis through caspase-3 activation. In conclusion, we believe that developing a specific and selective targeted therapy for PDGFRA would effectively suppress PDGFRA-mediated cancer aggressiveness in advanced thyroid cancers.
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ABSTRACT
Background
Recently, many computational methods have been proposed to predict cancer genes. One typical kind of method is to find the differentially expressed genes between tumour and normal samples. However, there are also some genes, for example, ‘dark’ genes, that play important roles at the network level but are difficult to find by traditional differential gene expression analysis. In addition, network controllability methods, such as the minimum feedback vertex set (MFVS) method, have been used frequently in cancer gene prediction. However, the weights of vertices (or genes) are ignored in the traditional MFVS methods, leading to difficulty in finding the optimal solution because of the existence of many possible MFVSs.
Results
Here, we introduce a novel method, called weighted MFVS (WMFVS), which integrates the gene differential expression value with MFVS to select the maximum-weighted MFVS from all possible MFVSs in a protein interaction network. Our experimental results show that WMFVS achieves better performance than using traditional bio-data or network-data analyses alone.
Conclusion
This method balances the advantage of differential gene expression analyses and network analyses, improves the low accuracy of differential gene expression analyses and decreases the instability of pure network analyses. Furthermore, WMFVS can be easily applied to various kinds of networks, providing a useful framework for data analysis and prediction.
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ABSTRACT
Background
Human dicer is an enzyme that cleaves pre-miRNAs into miRNAs. Several models have been developed to predict human dicer cleavage sites, including PHDCleav and LBSizeCleav. Given an input sequence, these models can predict whether the sequence contains a cleavage site. However, these models only consider each sequence independently and lack interpretability. Therefore, it is necessary to develop an accurate and explainable predictor, which employs relations between different sequences, to enhance the understanding of the mechanism by which human dicer cleaves pre-miRNA.
Results
In this study, we develop an accurate and explainable predictor for human dicer cleavage site – ReCGBM. We design relational features and class features as inputs to a lightGBM model. Computational experiments show that ReCGBM achieves the best performance compared to the existing methods. Further, we find that features in close proximity to the center of pre-miRNA are more important and make a significant contribution to the performance improvement of the developed method.
Conclusions
The results of this study show that ReCGBM is an interpretable and accurate predictor. Besides, the analyses of feature importance show that it might be of particular interest to consider more informative features close to the center of the pre-miRNA in future predictors.
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ABSTRACT
Chronic myelogenous leukemia (CML) is the most common type of leukemia in adults, and more than 90% of CML patients harbor the abnormal Philadelphia chromosome (Ph) that encodes the BCR-ABL oncoprotein. Although the ABL kinase inhibitor (imatinib) has proven to be very effective in achieving high remission rates and improving prognosis, up to 33% of CML patients still cannot achieve an optimal response. Here, we used CRISPR/Cas9 to specifically target the BCR–ABL junction region in K562 cells, resulting in the inhibition of cancer cell growth and oncogenesis. Due to the variety of BCR–ABL junctions in CML patients, we utilized gene editing of the human ABL gene for clinical applications. Using the ABL gene-edited virus in K562 cells, we detected 41.2% indels in ABL sgRNA_2-infected cells. The ABL-edited cells reveled significant suppression of BCR-ABL protein expression and downstream signals, inhibiting cell growth and increasing cell apoptosis. Next, we introduced the ABL gene-edited virus into a systemic K562 leukemia xenograft mouse model, and bioluminescence imaging of the mice showed a significant reduction in the leukemia cell population in ABL-targeted mice, compared to the scramble sgRNA virus-injected mice. In CML cells from clinical samples, infection with the ABL gene-edited virus resulted in more than 30.9% indels and significant cancer cell death. Notably, no off-target effects or bone marrow cell suppression was found using the ABL gene-edited virus, ensuring both user safety and treatment efficacy. This study demonstrated the critical role of the ABL gene in maintaining CML cell survival and tumorigenicity in vitro and in vivo. ABL gene editing-based therapy might provide a potential strategy for imatinib-insensitive or resistant CML patients.
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ABSTRACT
Although many studies have shown the association between smoking and the increased incidence and adverse prognosis of head and neck squamous cell carcinoma (HNSCC), the mechanisms and pharmaceutical targets involved remain unclear. Here, we integrated gene expression signatures, genetic alterations, and survival analyses to identify prognostic indicators and therapeutic targets for smoking HNSCC patients, and we discovered that the FDA-approved drug varenicline inhibits the target for cancer cell migration/invasion. We first identified 18 smoking-related and prognostic genes for HNSCC by using RNA-Seq and clinical follow-up data. One of these genes, CHRNB4 (neuronal acetylcholine receptor subunit beta-4), increased the risk of death by approximately threefold in CHRNB4-high expression smokers compared to CHRNB4-low expression smokers (log rank, p = 0.00042; hazard ratio, 2.82; 95% CI, 1.55–5.14), former smokers, and non-smokers. Furthermore, we examined the functional enrichment of co-regulated genes of CHRNB4 and its 246 frequently occurring copy number alterations (CNAs). We found that these genes were involved in promoting angiogenesis, resisting cell death, and sustaining proliferation, and contributed to much worse outcomes for CHRNB4-high patients. Finally, we performed CHRNB4 gene editing and drug inhibition assays, and the results validate these observations. In summary, our study suggests that CHRNB4 is a prognostic indicator for smoking HNSCC patients and provides a potential new therapeutic drug to prevent recurrence or distant metastasis.
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ABSTRACT
Alterations in membrane proteins (MPs) and their regulated pathways have been established as cancer hallmarks and extensively targeted in clinical applications. However, the analysis of MP-interacting proteins and downstream pathways across human malignancies remains challenging.
Here, we present a systematically integrated method to generate a resource of
cancer membrane protein-regulated networks (CaMPNets), containing 63,746 highconfidence protein–protein interactions (PPIs) for 1962 MPs, using expression profiles from 5922 tumors with overall survival outcomes across 15 human cancers. Comprehensive analysis of CaMPNets links MP partner communities and regulated pathways to provide MPbased gene sets for identifying prognostic biomarkers and druggable targets.
For example, we identify CHRNA9 with 12 PPIs (e.g., ERBB2) can be a therapeutic target and find its antimetastasis agent, bupropion, for treatment in nicotine-induced breast cancer. This resource is a study to systematically integrate MP interactions, genomics, and clinical outcomes for helping illuminate cancer-wide atlas and prognostic landscapes in tumor homo/ heterogeneity.
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ABSTRACT
Cancer subtype identification is an unmet need in precision diagnosis. Recently, evolutionary conservation has been indicated to contain informative signatures for functional significance in cancers. However, the importance of evolutionary conservation in distinguishing cancer subtypes remains largely unclear. Here, we identified the evolutionarily conserved genes (i.e. core genes) and observed that they are primarily involved in cellular pathways relevant to cell growth and metabolisms. By using these core genes, we developed two novel strategies, namely a feature-based strategy (FES) and an image-based strategy (IMS) by integrating their evolutionary and genomic profiles with the deep learning algorithm. In comparison with the FES using the random set and the strategy using the PAM50 classifier, the core gene set-based FES achieved a higher accuracy for identifying breast cancer subtypes. The IMS and FES using the core gene set yielded better performances than the other strategies, in terms of classifying both breast cancer subtypes and multiple cancer types. Moreover, the IMS is reproducible even using different gene expression data (i.e. RNA-seq and microarray). Comprehensive analysis of eight cancer types demonstrates that our evolutionary conservation-based models represent a valid and helpful approach for identifying cancer subtypes and the core gene set offers distinguishable clues of cancer subtypes.
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ABSTRACT
Prostate cancer (PCa) is the second leading cause of cancer death among men worldwide. About 70% of PCa patients were diagnosed at later stage, and metastasis has been observed. Additionally, the cure rate of PCa closely relies on the early diagnosis with biomarkers. The identification of biomarkers for diagnosis and prognosis is an urgent clinical issue for PCa. Here, we developed a novel scoring strategy, including cluster score (CS) and predicting score (PS), to identify 29 PCa genes (called PCa29) for early diagnostic biomarkers from two datasets in Gene Expression Omnibus. The result indicates that PCa29 can discriminate between normal and tumor tissues and are specific for prostate cancer. To validate PCa29, we found that 97% of PCa29 were consistently significant with these gene expressions in The Cancer Genome Atlas; furthermore, ∼∼70% of PCa29 are consensus to the protein expression in The Human Protein Atlas. Finally, we examined 10 genes in PCa29 on three PCa cell lines by real-time quantitative polymerase chain reaction. The experimental results show that the trend of the differential PCa29 expression is consistent with the analyzed results from our novel scoring method. We believe that our method is useful and PCa29 are potential biomarkers that provide the clues to develop targeting therapy for PCa.
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ABSTRACT
Since imatinib (Glivec or Gleevec) has been used to target the BCR-ABL fusion protein, chronic myeloid leukemia (CML) has become a manageable chronic disease with long-term survival. However, 15%–20% of CML patients ultimately develop resistance to imatinib and then progress to an accelerated phase and eventually to a blast crisis, limiting treatment options and resulting in a poor survival rate. Thus, we investigated whether histone deacetylase inhibitors (HDACis) could be used as a potential anticancer therapy for imatinib-resistant CML (IR-CML) patients. By applying a noninvasive apoptosis detection sensor (NIADS), we found that panobinostat significantly enhanced cell apoptosis in K562 cells. A further investigation showed that panobinostat induced apoptosis in both K562 and imatinib-resistant K562 (IR-K562) cells mainly via H3 and H4 histone acetylation, whereas panobinostat targeted cancer stem cells (CSCs) in IR-K562 cells. Using CRISPR/Cas9 genomic editing, we found that HDAC1 and HDAC2 knockout cells significantly induced cell apoptosis, indicating that the regulation of HDAC1 and HDAC2 is extremely important in maintaining K562 cell survival. All information in this study indicates that regulating HDAC activity provides therapeutic benefits against CML and IR-CML in the clinic.
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ABSTRACT
Anaplastic thyroid carcinoma (ATC) and squamous thyroid carcinoma (STC) are both rare and advanced thyroid malignancies with a very poor prognosis and an average median survival time of 5 months and less than 20% of affected patients are alive 1 year after diagnosis. The clinical management of both ATC and STC is very similar because they are not particularly responsive to radiotherapy and chemotherapy. This inspired us to explore a novel and effective clinically approved therapy for ATC treatment. Histone deacetylase inhibitor (HDACi) drugs are recently FDA-approved drug for malignancies, especially for blood cell cancers. Therefore, we investigated whether an HDACi drug acts as an effective anticancer drug for advanced thyroid cancers. Cell viability analysis of panobinostat treatment demonstrated a significant IC50 of 0.075 µM on SW579 STC cells. In addition, panobinostat exposure activated histone acetylation and triggered cell death mainly through cell cycle arrest and apoptosis-related protein activation. Using CRISPR/Cas9 to knock out HDAC1 and HDAC2 genes in SW579 cells, we observed that the histone acetylation level and cell cycle arrest were enhanced without any impact on cell growth. Furthermore, HDAC1 and HDAC2 double knockout (KO) cells showed dramatic cell apoptosis activation compared to HDAC1 and HDAC2 individual KO cells. This suggests expressional and biofunctional compensation between HDAC1 and HDAC2 on SW579 cells. This study provides strong evidence that panobinostat can potentially be used in the clinic of advanced thyroid cancer patients.
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ABSTRACT
Breast cancer is the most common malignancy in women and the second leading cause of cancer death in women. Triple negative breast cancer (TNBC) subtype is a breast cancer subset without ER (estrogen receptor), PR (progesterone receptor) and HER2 (human epidermal growth factor receptor 2) expression, limiting treatment options and presenting a poorer survival rate. Thus, we investigated whether histone deacetylation inhibitor (HDACi) could be used as potential anti-cancer therapy on breast cancer cells. In this study, we found TNBC and HER2-enriched breast cancers are extremely sensitive to Panobinostat, Belinostat of HDACi via experiments of cell viability assay, apoptotic marker identification and flow cytometry measurement. On the other hand, we developed a bioluminescence-based live cell non-invasive apoptosis detection sensor (NIADS) detection system to evaluate the quantitative and kinetic analyses of apoptotic cell death by HDAC treatment on breast cancer cells. In addition, the use of HDACi may also contribute a synergic anti-cancer effect with co-treatment of chemotherapeutic agent such as doxorubicin on TNBC cells (MDA-MB-231), but not in breast normal epithelia cells (MCF-10A), providing therapeutic benefits against breast tumor in the clinic.
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ABSTRACT
Anaplastic carcinoma of the thyroid (ATC), also called undifferentiated thyroid cancer, is the least common but most aggressive and deadly thyroid gland malignancy of all thyroid cancers. The aim of this study is to explore essential biomarker and use CRISPR/Cas9 with lentivirus delivery to establish a gene-target therapeutic platform in ATC cells. At the beginning, the gene expression datasets from 1036 cancers from CCLE and 8215 tumors from TCGA were collected and analyzed, showing EGFR is predominantly overexpressed in thyroid cancers than other type of cancers (P = 0.017 in CCLE and P = 0.001 in TCGA). Using CRISPR/Cas9 genomic edit system, ATC cells with EGFR sgRNA lentivirus transfection obtained great disruptions on gene and protein expression, resulting in cell cycle arrest, cell growth inhibition, and most importantly metastasis turn-off ability. In addition, the FDA-approved TKI of afatinib for EGFR targeting also illustrates great anticancer activity on cancer cell death occurrence, cell growth inhibition, and cell cycle arrest in SW579 cells, an EGFR expressing human ATC cell line. Furthermore, off-target effect of using EGFR sgRNAs was measured and found no genomic editing can be detected in off-target candidate gene. To conclude, this study provides potential ATC therapeutic strategies for current and future clinical needs, which may be possible in increasing the survival rate of ATC patients by translational medicine.
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ABSTRACT
Triple-negative breast cancer (TNBC) subtype is associated with poor prognosis and a high risk of recurrence-related death in women. Despite the aggressiveness of TNBCs, targeted TNBC therapy is not yet available in the clinic. To overcome this challenge, we generated highly metastatic TNBC cells (LM) derived from metastasized lung cells via a serial spontaneous pulmonary metastasis animal model to identify targetable molecules for attenuating the progression of TNBC metastasis. Gene analysis of primary tumor (P), first-round (1LM) and second-round (2LM) metastasized lung cells revealed that mesenchymal-related genes were significantly expressed in LM cells, especially in 2LM cells. Interestingly, α9-nAChR gene expression was also dramatically induced in LM cells, confirming our previous finding that α9-nAChR plays important roles in receptor-mediated carcinogenic signals in human breast cancer development. Using α9-nAChR as a biomarker, we transfected 2LM cells with CRISPR/Cas9 lentivirus targeting the α9-nAChR genomic region (2LM-α9-nAChR-null), showing that mesenchymal markers and the migration and invasion abilities of 2LM cells were significantly attenuated in 2LM-α9-nAChR-null cells both in vitro and in vivo. In addition, the high efficiency of editing the α9-nAChR gene using a CRISPR/Cas9 lentivirus was demonstrated by gene sequencing, genomic indel frequency and protein expression analyses. Collectively, these results confirmed those of our previous study that advanced-stage breast tumors are associated with substantially higher levels of α9-nAChR gene expression, indicating that α9-nAChR expression is essential for mediating TNBC metastasis during cancer development and may potentially act as a biomarker for targeted therapy in clinical investigations.
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ABSTRACT
Background
As cancer metastasis is the deadliest aspect of cancer, causing 90% of human deaths, evaluating the molecular mechanisms underlying this process is the major interest to those in the drug development field. Both therapeutic target identification and proof-of-concept experimentation in anti-cancer drug development require appropriate animal models, such as xenograft tumor transplantation in transgenic and knockout mice. In the progression of cancer metastasis, circulating tumor cells (CTCs) are the most critical factor in determining the prognosis of cancer patients. Several studies have demonstrated that measuring CTC-specific markers in a clinical setting (e.g., flow cytometry) can provide a current status of cancer development in patients. However, this useful technique has rarely been applied in the real-time monitoring of CTCs in preclinical animal models.
Results
In this manuscript, we established a quick and reliable method for measuring CTCs in a preclinical animal mode. The key to this technique is the use of specific human and mouse GUS primers on DNA/RNA of mouse peripheral blood under an absolute qPCR system. First, the high sensitivity of cancer cell detection on IVIS was presented by measuring the luciferase carried MDA-MB-231 cells from 5 to 5×1011 cell numbers with great correlation (R2 = 0.999). Next, the MDA-MB-231 cell numbers injected by tail vein and their IVIS radiance signals were strongly corrected with qPCR-calculated copy numbers (R2 > 0.99). Furthermore, by applying an orthotropic implantation animal model, we successfully distinguished xenograft tumor-bearing mice and control mice with a significant difference (p < 0.001), whereas IVIS Spectrum-CT 3D–visualization showed that blood of mice with lung metastasis contained more than twice the CTC numbers than ordinary tumor-bearing mice. We demonstrated a positive correlation between lung metastasis status and CTC numbers in peripheral mouse blood.
Conclusion
Collectively, the techniques developed for this study resulted in the integration of CTC assessments into preclinical models both in vivo and ex vivo, which will facilitate translational targeted therapy in clinical practice.
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ABSTRACT
A module is a group of closely related proteins that act in concert to perform specific biological functions through protein–protein interactions (PPIs) that occur in time and space. However, the underlying module organization and variance remain unclear. In this study, we collected module templates to infer respective module families, including 58,041 homologous modules in 1,678 species, and PPI families using searches of complete genomic database. We then derived PPI evolution scores and interface evolution scores to describe the module elements, including core and ring components. Functions of core components were highly correlated with those of essential genes. In comparison with ring components, core proteins/PPIs were conserved across multiple species. Subsequently, protein/module variance of PPI networks confirmed that core components form dynamic network hubs and play key roles in various biological functions. Based on the analyses of gene essentiality, module variance, and gene co-expression, we summarize the observations of module organization and variance as follows: 1) a module consists of core and ring components; 2) core components perform major biological functions and collaborate with ring components to execute certain functions in some cases; 3) core components are more conserved and essential during organizational changes in different biological states or conditions.
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ABSTRACT
Background
One of the crucial steps toward understanding the biological functions of a cellular system is to investigate protein–protein interaction (PPI) networks. As an increasing number of reliable PPIs become available, there is a growing need for discovering PPIs to reconstruct PPI networks of interesting organisms. Some interolog-based methods and homologous PPI families have been proposed for predicting PPIs from the known PPIs of source organisms.
Results
Here, we propose a multiple-strategy scoring method to identify reliable PPIs for reconstructing the mouse PPI network from two well-known organisms: human and fly. We firstly identified the PPI candidates of target organisms based on homologous PPIs, sharing significant sequence similarities (joint E-value ≤ 1 × 10−40), from source organisms using generalized interolog mapping. These PPI candidates were evaluated by our multiple-strategy scoring method, combining sequence similarities, normalized ranks, and conservation scores across multiple organisms. According to 106,825 PPI candidates in yeast derived from human and fly, our scoring method can achieve high prediction accuracy and outperform generalized interolog mapping. Experiment results show that our multiple-strategy score can avoid the influence of the protein family size and length to significantly improve PPI prediction accuracy and reflect the biological functions. In addition, the top-ranked and conserved PPIs are often orthologous/essential interactions and share the functional similarity. Based on these reliable predicted PPIs, we reconstructed a comprehensive mouse PPI network, which is a scale-free network and can reflect the biological functions and high connectivity of 292 KEGG modules, including 216 pathways and 76 structural complexes.
Conclusions
Experimental results show that our scoring method can improve the predicting accuracy based on the normalized rank and evolutionary conservation from multiple organisms. Our predicted PPIs share similar biological processes and cellular components, and the reconstructed genome-wide PPI network can reflect network topology and modularity. We believe that our method is useful for inferring reliable PPIs and reconstructing a comprehensive PPI network of an interesting organism.
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ABSTRACT
Background
The protein-protein interaction (PPI) is one of the most important features to understand biological processes. For a PPI, the physical domain-domain interaction (DDI) plays the key role for biology functions. In the post-genomic era, to rapidly identify homologous PPIs for analyzing the contact residue pairs of their interfaces within DDIs on a genomic scale is essential to determine PPI networks and the PPI interface evolution across multiple species.
Results
In this study, we proposed “pair P osition S pecific S coring M atrix (pair PSSM)” to identify homologous PPIs. The pair PSSM can successfully distinguish the true protein complexes from unreasonable protein pairs with about 90% accuracy. For the test set including 1,122 representative heterodimers and 2,708,746 non-interacting protein pairs, the mean average precision and mean false positive rate of pair PSSM were 0.42 and 0.31, respectively. Moreover, we applied pair PSSM to identify ~450,000 homologous PPIs with their interacting domains and residues in seven common organisms (e.g. Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Escherichia coli).
Conclusions
Our pair PSSM is able to provide statistical significance of residue pairs using evolutionary profiles and a scoring system for inferring homologous PPIs. According to our best knowledge, the pair PSSM is the first method for searching homologous PPIs across multiple species using pair position specific scoring matrix and a 3D dimer as the template to map interacting domain pairs of these PPIs. We believe that pair PSSM is able to provide valuable insights for the PPI evolution and networks across multiple species.
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ABSTRACT
Background
To discover a compound inhibiting multiple proteins (i.e. polypharmacological targets) is a new paradigm for the complex diseases (e.g. cancers and diabetes). In general, the polypharmacological proteins often share similar local binding environments and motifs. As the exponential growth of the number of protein structures, to find the similar structural binding motifs (pharma-motifs) is an emergency task for drug discovery (e.g. side effects and new uses for old drugs) and protein functions.
Results
We have developed a Space-Related Pharmamotifs (called SRPmotif) method to recognize the binding motifs by searching against protein structure database. SRPmotif is able to recognize conserved binding environments containing spatially discontinuous pharma-motifs which are often short conserved peptides with specific physico-chemical properties for protein functions. Among 356 pharma-motifs, 56.5% interacting residues are highly conserved. Experimental results indicate that 81.1% and 92.7% polypharmacological targets of each protein-ligand complex are annotated with same biological process (BP) and molecular function (MF) terms, respectively, based on Gene Ontology (GO). Our experimental results show that the identified pharma-motifs often consist of key residues in functional (active) sites and play the key roles for protein functions. The SRPmotif is available at http://gemdock.life.nctu.edu.tw/SRP/.
Conclusions
SRPmotif is able to identify similar pharma-interfaces and pharma-motifs sharing similar binding environments for polypharmacological targets by rapidly searching against the protein structure database. Pharma-motifs describe the conservations of binding environments for drug discovery and protein functions. Additionally, these pharma-motifs provide the clues for discovering new sequence-based motifs to predict protein functions from protein sequence databases. We believe that SRPmotif is useful for elucidating protein functions and drug.
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ABSTRACT
A module is a fundamental unit forming with highly connected proteins and performs a certain kind of biological functions. Modules and module-module interaction (MMI) network are essential for understanding cellular processes and functions. The MoNetFamily web server can identify the modules, homologous modules (called module family) and MMI networks across multiple species for the query protein(s). This server first finds module candidates of the query by using BLASTP to search the module template database (1785 experimental and 1252 structural templates). MoNetFamily then infers the homologous modules of the selected module candidate using protein-protein interaction (PPI) families. According to homologous modules and PPIs, we statistically calculated MMIs and MMI networks across multiple species. For each module candidate, MoNetFamily identifies its neighboring modules and their MMIs in module networks of Homo sapiens, Mus musculus and Danio rerio. Finally, MoNetFamily shows the conserved proteins, PPI profiles and functional annotations of the module family. Our results indicate that the server can be useful for MMI network (e.g. 1818 modules and 9678 MMIs in H. sapiens) visualizations and query annotations using module families and neighboring modules. We believe that the server is able to provide valuable insights to determine homologous modules and MMI networks across multiple species for studying module evolution and cellular processes. The MoNetFamily sever is available at http://monetfamily.life.nctu.edu.tw.
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ABSTRACT
The proteins in a cell often assemble into complexes to carry out their functions and play an essential role of biological processes. The PCFamily server identifies template-based homologous protein complexes [called protein complex family (PCF)] and infers functional modules of the query proteins. This server first finds homologous structure complexes of the query using BLASTP to search the structural template database (11 263 complexes). PCFamily then searches the homologous complexes of the templates (query) from a complete genomic database (Integr8 with 6 352 363 protein sequences in 2274 species). According to these homologous complexes across multiple species, this sever infers binding models (e.g. hydrogen-bonds and conserved amino acids in the interfaces), functional modules, and the conserved interacting domains and Gene Ontology annotations of the PCF. Experimental results demonstrate that the PCFamily server can be useful for binding model visualizations and annotating the query proteins. We believe that the server is able to provide valuable insights for determining functional modules of biological networks across multiple species. The PCFamily sever is available at http://pcfamily.life.nctu.edu.tw.
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homologous protein–protein interactions across multiple species, Nucleic Acids Research, 37(2), W369 -W375.
ABSTRACT
As an increasing number of reliable protein–protein interactions (PPIs) become available and high-throughput experimental methods provide systematic identification of PPIs, there is a growing need for fast and accurate methods for discovering homologous PPIs of a newly determined PPI. PPISearch is a web server that rapidly identifies homologous PPIs (called PPI family) and infers transferability of interacting domains and functions of a query protein pair. This server first identifies two homologous families of the query, respectively, by using BLASTP to scan an annotated PPIs database (290 137 PPIs in 576 species), which is a collection of five public databases. We determined homologous PPIs from protein pairs of homologous families when these protein pairs were in the annotated database and have significant joint sequence similarity (E ≤ 10−40) with the query. Using these homologous PPIs across multiple species, this sever infers the conserved domain–domain pairs (Pfam and InterPro domains) and function pairs (Gene Ontology annotations). Our results demonstrate that the transferability of conserved domain-domain pairs between homologous PPIs and query pairs is 88% using 103 762 PPI queries, and the transferability of conserved function pairs is 69% based on 106 997 PPI queries. The PPISearch server should be useful for searching homologous PPIs and PPI families across multiple species. The PPISearch server is available through the website at http://gemdock.life.nctu.edu.tw/ppisearch/.
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ABSTRACT
Background: With the advance in single-cell RNA sequencing (scRNA-seq) technol‑ogy, deriving inherent biological system information from expression profles at a single-cell resolution has become possible. It has been known that network modeling by estimating the associations between genes could better reveal dynamic changes in biological systems. However, accurately constructing a single-cell network (SCN) to capture the network architecture of each cell and further explore cell-to-cell heterogeneity remains challenging.
Results: We introduce SINUM, a method for constructing the SInglecell Network Using Mutual information, which estimates mutual information between any two genes from scRNA-seq data to determine whether they are dependent or independent in a specifc cell. Experiments on various scRNA-seq datasets with diferent cell numbers based on eight performance indexes (e.g., adjusted rand index and F-measure index) validated the accuracy and robustness of SINUM in cell type identifcation, superior to the state-of-the-art SCN inference method. Additionally, the SINUM SCNs exhibit high overlap with the human interactome and possess the scale-free property.
Conclusions: SINUM presents a view of biological systems at the network level
to detect cell-type marker genes/gene pairs and investigate time-dependent changes in gene associations during embryo development. Codes for SINUM are freely available at https://github.com/SysMednet/SINUM.
Keywords: Single-cell analysis, Network inference, Mutual information, Gene expression, Single-cell network, Single-cell clustering
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ABSTRACT
Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics. Here, we propose a gene set correlation enrichment analysis method, Gscore, based on an expression dataset-derived coexpression network to examine whether a differentially expressed gene (DEG) list (or each of its DEGs) is associated with a known gene set. Gscore is better able to identify target pathways in 89 human disease expression datasets than eight other state-of-the-art methods and offers insight into how disease-wide and pathway-wide associations reflect clinical outcomes. When applied to RNA-seq data from COVID-19-related cells and patient samples, Gscore provided a means for studying how DEGs are implicated in COVID-19-related pathways. In summary, Gscore offers a powerful analytical approach for annotating individual DEGs, DEG lists, and genome-wide expression profiles based on existing biological knowledge.
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ABSTRACT
As a type of immunogenic cell death, ferroptosis participates in the creation of immunoactive tumor microenvironments. However, knowledge of spatial location of tumor cells with ferroptosis signature in tumor environments and the role of ferroptotic stress in inducing the expression of immune-related molecules in cancer cells is limited. Here the spatial association of the transcriptomic signatures is demonstrated for ferroptosis and inflammation/immune activation located in the invasive front of head and neck squamous cell carcinoma (HNSCC). The association between ferroptosis signature and inflammation/immune activation is more prominent in HPV-negative HNSCC compared to HPV-positive ones. Ferroptotic stress induces PD-L1 expression through reactive oxygen species (ROS)-elicited NF-κB signaling pathway and calcium influx. Priming murine HNSCC with the ferroptosis inducer sensitizes tumors to anti-PD-L1 antibody treatment. A positive correlation between the ferroptosis signature and the active immune cell profile is shown in the HNSCC samples. This study reveals a subgroup of ferroptotic HNSCC with immune-active signatures and indicates the potential of priming HNSCC with ferroptosis inducers to increase the antitumor efficacy of immune checkpoint inhibitors.
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ABSTRACT
Erinacines derived from Hericium erinaceus have been shown to possess various health benefits including neuroprotective effect against neurodegenerative diseases, yet the underlying mechanism remains unknown. Here we found that erinacine S enhances neurite outgrowth in a cell autonomous fashion. It promotes post-injury axon regeneration of PNS neurons and enhances regeneration on inhibitory substrates of CNS neurons. Using RNA-seq and bioinformatic analyses, erinacine S was found to cause the accumulation of neurosteroids in neurons. ELISA and neurosteroidogenesis inhibitor assays were performed to validate this effect. This research uncovers a previously unknown effect of erinacine S on raising the level of neurosteroids.
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ABSTRACT
Recently, extracting inherent biological system information (e.g. cellular networks) from genome-wide expression profiles for developing personalized diagnostic and therapeutic strategies has become increasingly important. However, accurately constructing single-sample networks (SINs) to capture individual characteristics and heterogeneity in disease remains challenging. Here, we propose a sample-specific-weighted correlation network (SWEET) method to model SINs by integrating the genome-wide sample-to-sample correlation (i.e. sample weights) with the differential network between perturbed and aggregate networks. For a group of samples, the genome-wide sample weights can be assessed without prior knowledge of intrinsic subpopulations to address the network edge number bias caused by sample size differences. Compared with the state-of-the-art SIN inference methods, the SWEET SINs in 16 cancers more likely fit the scale-free property, display higher overlap with the human interactomes and perform better in identifying three types of cancer-related genes. Moreover, integrating SWEET SINs with a network proximity measure facilitates characterizing individual features and therapy in diseases, such as somatic mutation, mut-driver and essential genes. Biological experiments further validated two candidate repurposable drugs, albendazole for head and neck squamous cell carcinoma (HNSCC) and lung adenocarcinoma (LUAD) and encorafenib for HNSCC. By applying SWEET, we also identified two possible LUAD subtypes that exhibit distinct clinical features and molecular mechanisms. Overall, the SWEET method complements current SIN inference and analysis methods and presents a view of biological systems at the network level to offer numerous clues for further investigation and clinical translation in network medicine and precision medicine.
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ABSTRACT
Alterations in membrane proteins (MPs) and their regulated pathways have been established as cancer hallmarks and extensively targeted in clinical applications. However, the analysis of MP-interacting proteins and downstream pathways across human malignancies remains challenging.
Here, we present a systematically integrated method to generate a resource of
cancer membrane protein-regulated networks (CaMPNets), containing 63,746 highconfidence protein–protein interactions (PPIs) for 1962 MPs, using expression profiles from 5922 tumors with overall survival outcomes across 15 human cancers. Comprehensive analysis of CaMPNets links MP partner communities and regulated pathways to provide MPbased gene sets for identifying prognostic biomarkers and druggable targets.
For example, we identify CHRNA9 with 12 PPIs (e.g., ERBB2) can be a therapeutic target and find its antimetastasis agent, bupropion, for treatment in nicotine-induced breast cancer. This resource is a study to systematically integrate MP interactions, genomics, and clinical outcomes for helping illuminate cancer-wide atlas and prognostic landscapes in tumor homo/ heterogeneity.
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ABSTRACT
Cancer subtype identification is an unmet need in precision diagnosis. Recently, evolutionary conservation has been indicated to contain informative signatures for functional significance in cancers. However, the importance of evolutionary conservation in distinguishing cancer subtypes remains largely unclear. Here, we identified the evolutionarily conserved genes (i.e. core genes) and observed that they are primarily involved in cellular pathways relevant to cell growth and metabolisms. By using these core genes, we developed two novel strategies, namely a feature-based strategy (FES) and an image-based strategy (IMS) by integrating their evolutionary and genomic profiles with the deep learning algorithm. In comparison with the FES using the random set and the strategy using the PAM50 classifier, the core gene set-based FES achieved a higher accuracy for identifying breast cancer subtypes. The IMS and FES using the core gene set yielded better performances than the other strategies, in terms of classifying both breast cancer subtypes and multiple cancer types. Moreover, the IMS is reproducible even using different gene expression data (i.e. RNA-seq and microarray). Comprehensive analysis of eight cancer types demonstrates that our evolutionary conservation-based models represent a valid and helpful approach for identifying cancer subtypes and the core gene set offers distinguishable clues of cancer subtypes.
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ABSTRACT
A module is a group of closely related proteins that act in concert to perform specific biological functions through protein–protein interactions (PPIs) that occur in time and space. However, the underlying module organization and variance remain unclear. In this study, we collected module templates to infer respective module families, including 58,041 homologous modules in 1,678 species, and PPI families using searches of complete genomic database. We then derived PPI evolution scores and interface evolution scores to describe the module elements, including core and ring components. Functions of core components were highly correlated with those of essential genes. In comparison with ring components, core proteins/PPIs were conserved across multiple species. Subsequently, protein/module variance of PPI networks confirmed that core components form dynamic network hubs and play key roles in various biological functions. Based on the analyses of gene essentiality, module variance, and gene co-expression, we summarize the observations of module organization and variance as follows: 1) a module consists of core and ring components; 2) core components perform major biological functions and collaborate with ring components to execute certain functions in some cases; 3) core components are more conserved and essential during organizational changes in different biological states or conditions.
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ABSTRACT
Background
The protein-protein interaction (PPI) is one of the most important features to understand biological processes. For a PPI, the physical domain-domain interaction (DDI) plays the key role for biology functions. In the post-genomic era, to rapidly identify homologous PPIs for analyzing the contact residue pairs of their interfaces within DDIs on a genomic scale is essential to determine PPI networks and the PPI interface evolution across multiple species.
Results
In this study, we proposed “pair P osition S pecific S coring M atrix (pair PSSM)” to identify homologous PPIs. The pair PSSM can successfully distinguish the true protein complexes from unreasonable protein pairs with about 90% accuracy. For the test set including 1,122 representative heterodimers and 2,708,746 non-interacting protein pairs, the mean average precision and mean false positive rate of pair PSSM were 0.42 and 0.31, respectively. Moreover, we applied pair PSSM to identify ~450,000 homologous PPIs with their interacting domains and residues in seven common organisms (e.g. Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Escherichia coli).
Conclusions
Our pair PSSM is able to provide statistical significance of residue pairs using evolutionary profiles and a scoring system for inferring homologous PPIs. According to our best knowledge, the pair PSSM is the first method for searching homologous PPIs across multiple species using pair position specific scoring matrix and a 3D dimer as the template to map interacting domain pairs of these PPIs. We believe that pair PSSM is able to provide valuable insights for the PPI evolution and networks across multiple species.
[LINK]
ABSTRACT
A module is a fundamental unit forming with highly connected proteins and performs a certain kind of biological functions. Modules and module-module interaction (MMI) network are essential for understanding cellular processes and functions. The MoNetFamily web server can identify the modules, homologous modules (called module family) and MMI networks across multiple species for the query protein(s). This server first finds module candidates of the query by using BLASTP to search the module template database (1785 experimental and 1252 structural templates). MoNetFamily then infers the homologous modules of the selected module candidate using protein-protein interaction (PPI) families. According to homologous modules and PPIs, we statistically calculated MMIs and MMI networks across multiple species. For each module candidate, MoNetFamily identifies its neighboring modules and their MMIs in module networks of Homo sapiens, Mus musculus and Danio rerio. Finally, MoNetFamily shows the conserved proteins, PPI profiles and functional annotations of the module family. Our results indicate that the server can be useful for MMI network (e.g. 1818 modules and 9678 MMIs in H. sapiens) visualizations and query annotations using module families and neighboring modules. We believe that the server is able to provide valuable insights to determine homologous modules and MMI networks across multiple species for studying module evolution and cellular processes. The MoNetFamily sever is available at http://monetfamily.life.nctu.edu.tw.
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Conference papers
- Chang, L. Y.#, Hao, T. Y.#, Wang, W. J.#, and Lin, C. Y.* (2022) Inference of single-cell network using mutual information for scRNA-seq data analysis, GIW XXXI/ISCB-Asia V conference, December.
[link] - Lin, C. Y.#, Ruan, P. Y.#, Li, R. M., Yang, J. M., See S., and Akutsu, T. (2019) Deep Learning with Evolutionary and Genomic Profiles for Identifying Cancer Subtypes, IEEE 18th International Conference on BioInformatics and BioEngineering (BIBE), 147-150 , October #These authors contributed equally [link]
- Lee, J. Y., Lin, S. Y., Chuang, Y. H., Huang, S. H., Tseng, Y. Y., Lin, C. Y., Wang, H. J. and Yang, J. M. (2019) Identification of the PCa28 Gene Signature as a Predictor in Prostate Cancer, IEEE 18th International Conference on BioInformatics and BioEngineering (BIBE), 155-158, October [link]
- Tamura, T., Lin, C. Y., Yang, J. M., and Akutsu, T. (2016) Finding influential genes using gene expression data and Boolean models of metabolic networks, IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE), 57-63, October [link]
- Lin, C. Y., Chen, Y. C., Lo, Y. S., and Yang, J. M.* (2013) Inferring homologous protein-protein interactions through pair position specific scoring matrix, The Eleventh Asia Pacific Bioinformatics Conference (APBC), January [link]
- Chiu, Y. Y., Lin, C. Y., Lin, C. T., Hsu, K. C., Chang, L. Z., and Yang, J. M.* (2012) Space-related pharma-motifs for fast search protein binding motifs and polypharmacological targets, Asia Pacific Bioinformatics Network (APBioNet) Eleventh International Conference on Bioinformatics (InCoB), October [link]
Posters
- Hao, T. Y., Yeh, T. Y., Lee, W. Y., and Lin, C. Y., Chi-Square Estimate Single-Cell Network with Transcriptomic Correlation for Single-Cell RNA-Seq Data Analysis
, 2023年多體學及精準醫學聯合會議, Taiwan, Nov, 2023. (American Physiological Society (APS) 優秀海報獎（Physiological Genomics Abstract Awards) - Lee, M. Z., Chang, L. Y., Wu, Y. J., Lee, W. K., Huang, T. C., and Lin, C. Y., Gscore: A Pathway Analysis Method for Annotating Gene Expression Data, 2023年多體學及精準醫學聯合會議, Taiwan, Nov, 2023. (壁報比賽 第二名)
- Chang, L. Y., Hao, T. Y., Wang, W. J., and Lin, C. Y., A Single-Cell Network Inference Method Using Mutual Information for scRNA-seq Data Analysis, 2022年多體學及精準醫學聯合會議, Taiwan, Nov, 2022. (最佳壁報獎 第二名)
- Wu, Y. J., and Lin, C. Y., Gene set correlation enrichment analysis for interpreting and annotating gene expression data, 2022年中研院生醫所/國立陽明交通大學生科院聯合生物醫學嶄新學術研討會, Taiwan, Mar, 2022. (壁報佳作獎)
- Wang, Y. J., and Lin, C. Y., Construction of single-cell-specific networks by using single-cell genome weights, 2020陽明交大生物科技與藥物發展研討會, Taiwan, Sep, 2020. (最佳壁報獎)
- Lin, C. Y., Yang, J. M., and Akutsu, T. Pan-cancer module network analysis for identifying dominating networks across human cancers, The 7th joint Conference on Informatics In Biology, Medicine and Pharmacology (IIBMP 2018), Japan, Sep, 2018. (The best poster award)
- Lin, C. Y., Chen, Y. C., Lo, Y. S., Lee, T. L., and Yang, J. M., Inferring homologous protein-protein interactions through pair position specific scoring matrix, The 2013 poster contest of the college of biological and technology, NCTU, May, 2013. (The best poster award)
- Lin, C. Y., Lin, Y. W., Yu, S. W., Lo, Y. S., and Yang, J. M., Evolutionary module-module interaction networks reveal cell behaviors, The 17th Biophysics Conference, Taiwan, May, 2012. (The best poster award)
- Lin, C. Y., Chen, C. C., Lo, Y. S., and Yang, J. M., Template-driven approaches for protein-protein interaction families, The 2010 poster contest of the college of biological and technology, NCTU, May, 2010. (The best poster award)
Patents
- 安非他酮和醫藥組合物在製備治療癌症之藥物的用途及抑制腫瘤細胞遷移的方法
[一種安非他酮在製備治療癌症之藥物的用途，其中該癌症為未經尼古丁誘導的乳癌] (專利號: I736452)，可新用於抑制非尼古丁誘導乳癌之腫瘤轉移。
Use of bupropion and pharmaceutical compositions in preparing drugs for treating cancer and methods for inhibiting tumor cell migration
[A use of bupropion in preparing a drug for treating cancer, wherein the cancer is breast cancer that is not induced by nicotine] (Patent No.: I736452), can be newly used to inhibit non-nicotine-induced tumor metastasis of breast cancer. - 安非他酮和醫藥組合物在製備治療癌症之藥物的用途及抑制腫瘤細胞遷移的方法
[一種安非他酮在製備治療癌症之藥物的用途，其中，該癌症沒有受到尼古丁或尼古丁衍生物的誘導，以及該癌症之腫瘤細胞過度表現神經性乙醯膽鹼受體次單元α9(CHRNA9)] (專利號: I713875)，可新用於抑制非尼古丁誘導乳癌之腫瘤轉移。
Use of bupropion and pharmaceutical compositions in preparing drugs for treating cancer and methods for inhibiting tumor cell migration
[A use of bupropion in preparing a medicament for treating cancer, wherein the cancer is not induced by nicotine or nicotine derivatives, and the tumor cells of the cancer overexpress neural acetylcholine receptor subunit α9 ( CHRNA9 )] (Patent number: I713875), can be newly used to inhibit non-nicotine-induced tumor metastasis of breast cancer.