scholarly journals Identification of Intestinal Flora-Related Key Genes and Therapeutic Drugs in Colorectal Cancer

2020 ◽  
Author(s):  
Jiayu Zhang ◽  
Huaiyu Zhang ◽  
Faping Li ◽  
Zheyu Song ◽  
Yezhou Li ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Intestinal Flora ◽  
Interaction Network ◽  
Gene Interaction ◽  
New Drugs ◽  
The Cancer Genome Atlas ◽  
Practical Significance ◽  
Receptor Interaction ◽  
Therapeutic Drugs ◽  
Key Genes

Abstract Colorectal cancer (CRC) is a multifactorial tumor and a leading cause of cancer-specific deaths worldwide. Recent research has shown that the alteration of intestinal flora contributes to the development of CRC. However, the molecular mechanism by which intestinal flora influences the pathogenesis of CRC remains unclear. This study aims to explore the key genes underlying the effect of intestinal flora on CRC and therapeutic drugs for CRC. 518 genes associated with intestinal flora were determined by text mining. Based on The Cancer Genome Atlas (TCGA) database, we identified 48 differentially expressed genes (DEGs) associated with intestinal flora, including 25 up-regulated and 23 down-regulated DEGs. The enrichment analyses indicated that the selected genes were mainly involved in cell-cell signaling, immune response, cytokine-cytokine receptor interaction, and JAK-STAT signaling pathway. The protein-protein interaction network was constructed with 13 nodes and 35 edges. Moreover, 8 genes in the significant cluster were considered as the key genes and chemokine (C-X-C motif) ligand 8 (CXCL8) correlated positively with the overall survival of CRC patients. Finally, a total of 24 drugs were predicted as possible drugs for CRC treatment using the Drug-Gene Interaction database. These findings of this study may provide new insights into CRC pathogenesis and treatments. The prediction of drug-gene interaction is of great practical significance for exploring new drugs or novel targets for existing drugs.

scholarly journals Identification of intestinal flora-related key genes and therapeutic drugs in colorectal cancer

2020 ◽  
Author(s):  
Jiayu Zhang ◽  
Huaiyu Zhang ◽  
Faping Li ◽  
Zheyu Song ◽  
Yezhou Li ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Intestinal Flora ◽  
Interaction Network ◽  
Gene Interaction ◽  
The Cancer Genome Atlas ◽  
Interaction Database ◽  
Therapeutic Drugs ◽  
Cancer Genome Atlas ◽  
Key Genes ◽  
Genome Atlas

Abstract Background: Colorectal cancer (CRC) is a multifactorial tumor and a leading cause of cancer-specific deaths worldwide. Recent research has shown that the alteration of intestinal flora contributes to the development of CRC. However, the molecular mechanism by which intestinal flora influences the pathogenesis of CRC remains unclear. This study aims to explore the key genes underlying the effect of intestinal flora on CRC and therapeutic drugs for CRC.Methods: Intestinal flora-related genes were determined using text mining. Based on The Cancer Genome Atlas database, differentially expressed genes (DEGs) between CRC and normal samples were identified with the limma package of the R software. Then, the intersection of the two gene sets was selected for enrichment analyses using the tool Database for Annotation, Visualization and Integrated Discovery. Protein interaction network analysis was performed for identifying the key genes using STRING and Cytoscape. The correlation of the key genes with overall survival of CRC patients was analyzed. Finally, the key genes were queried against the Drug-Gene Interaction database to find drug candidates for treating CRC.Results: 518 genes associated with intestinal flora were determined by text mining. Based on The Cancer Genome Atlas database, we identified 48 DEGs associated with intestinal flora, including 25 up-regulated and 23 down-regulated DEGs in CRC. The enrichment analyses indicated that the selected genes were mainly involved in cell-cell signaling, immune response, cytokine-cytokine receptor interaction, and JAK-STAT signaling pathway. The protein-protein interaction network was constructed with 13 nodes and 35 edges. Moreover, 8 genes in the significant cluster were considered as the key genes and chemokine (C-X-C motif) ligand 8 (CXCL8) correlated positively with the overall survival of CRC patients. Finally, a total of 24 drugs were predicted as possible drugs for CRC treatment using the Drug-Gene Interaction database.Conclusions: These findings of this study may provide new insights into CRC pathogenesis and treatments. The prediction of drug-gene interaction is of great practical significance for exploring new drugs or novel targets for existing drugs.

scholarly journals Identification of intestinal flora-related key genes and therapeutic drugs in colorectal cancer

2020 ◽  
Author(s):  
Jiayu Zhang ◽  
Huaiyu Zhang ◽  
Faping Li ◽  
Zheyu Song ◽  
Yezhou Li ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Intestinal Flora ◽  
Interaction Network ◽  
Gene Interaction ◽  
The Cancer Genome Atlas ◽  
Interaction Database ◽  
Therapeutic Drugs ◽  
Cancer Genome Atlas ◽  
Key Genes ◽  
Genome Atlas

Abstract Background: Colorectal cancer (CRC) is a multifactorial tumor and a leading cause of cancer-specific deaths worldwide. Recent research has shown that the alteration of intestinal flora contributes to the development of CRC. However, the molecular mechanism by which intestinal flora influences the pathogenesis of CRC remains unclear. This study aims to explore the key genes underlying the effect of intestinal flora on CRC and therapeutic drugs for CRC.Methods: Intestinal flora-related genes were determined using text mining. Based on The Cancer Genome Atlas database, differentially expressed genes (DEGs) between CRC and normal samples were identified with the limma package of the R software. Then, the intersection of the two gene sets was selected for enrichment analyses using the tool Database for Annotation, Visualization and Integrated Discovery. Protein interaction network analysis was performed for identifying the key genes using STRING and Cytoscape. The correlation of the key genes with overall survival of CRC patients was analyzed. Finally, the key genes were queried against the Drug-Gene Interaction database to find drug candidates for treating CRC.Results: 518 genes associated with intestinal flora were determined by text mining. Based on The Cancer Genome Atlas database, we identified 48 DEGs associated with intestinal flora, including 25 up-regulated and 23 down-regulated DEGs in CRC. The enrichment analyses indicated that the selected genes were mainly involved in cell-cell signaling, immune response, cytokine-cytokine receptor interaction, and JAK-STAT signaling pathway. The protein-protein interaction network was constructed with 13 nodes and 35 edges. Moreover, 8 genes in the significant cluster were considered as the key genes and chemokine (C-X-C motif) ligand 8 (CXCL8) correlated positively with the overall survival of CRC patients. Finally, a total of 24 drugs were predicted as possible drugs for CRC treatment using the Drug-Gene Interaction database.Conclusions: These findings of this study may provide new insights into CRC pathogenesis and treatments. The prediction of drug-gene interaction is of great practical significance for exploring new drugs or novel targets for existing drugs.

scholarly journals Identification of intestinal flora-related key genes and therapeutic drugs in colorectal cancer

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Jiayu Zhang ◽  
Huaiyu Zhang ◽  
Faping Li ◽  
Zheyu Song ◽  
Yezhou Li ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Intestinal Flora ◽  
Interaction Network ◽  
Gene Interaction ◽  
The Cancer Genome Atlas ◽  
Interaction Database ◽  
Therapeutic Drugs ◽  
Cancer Genome Atlas ◽  
Key Genes ◽  
Genome Atlas

Abstract Background Colorectal cancer (CRC) is a multifactorial tumor and a leading cause of cancer-specific deaths worldwide. Recent research has shown that the alteration of intestinal flora contributes to the development of CRC. However, the molecular mechanism by which intestinal flora influences the pathogenesis of CRC remains unclear. This study aims to explore the key genes underlying the effect of intestinal flora on CRC and therapeutic drugs for CRC. Methods Intestinal flora-related genes were determined using text mining. Based on The Cancer Genome Atlas database, differentially expressed genes (DEGs) between CRC and normal samples were identified with the limma package of the R software. Then, the intersection of the two gene sets was selected for enrichment analyses using the tool Database for Annotation, Visualization and Integrated Discovery. Protein interaction network analysis was performed for identifying the key genes using STRING and Cytoscape. The correlation of the key genes with overall survival of CRC patients was analyzed. Finally, the key genes were queried against the Drug-Gene Interaction database to find drug candidates for treating CRC. Results 518 genes associated with intestinal flora were determined by text mining. Based on The Cancer Genome Atlas database, we identified 48 DEGs associated with intestinal flora, including 25 up-regulated and 23 down-regulated DEGs in CRC. The enrichment analyses indicated that the selected genes were mainly involved in cell–cell signaling, immune response, cytokine-cytokine receptor interaction, and JAK-STAT signaling pathway. The protein–protein interaction network was constructed with 13 nodes and 35 edges. Moreover, 8 genes in the significant cluster were considered as the key genes and chemokine (C-X-C motif) ligand 8 (CXCL8) correlated positively with the overall survival of CRC patients. Finally, a total of 24 drugs were predicted as possible drugs for CRC treatment using the Drug-Gene Interaction database. Conclusions These findings of this study may provide new insights into CRC pathogenesis and treatments. The prediction of drug-gene interaction is of great practical significance for exploring new drugs or novel targets for existing drugs.

Comprehensive Profile Analysis Identifies Three Key Genes Related to Stemness in Thyroid Carcinoma

2020 ◽  
Vol 23 ◽  
Author(s):  
Tonglong Zhang ◽  
Chunhong Yan ◽  
Zhengdu Ye ◽  
Xingling Yin ◽  
Tian-an Jiang
Keyword(s):  
Stem Cells ◽  
Protein Interaction ◽  
Profile Analysis ◽  
Interaction Network ◽  
The Cancer Genome Atlas ◽  
Receptor Interaction ◽  
Gene Expressions ◽  
Multiple Tumor ◽  
External Data ◽  
Key Genes

Background: Tumor heterogeneity imposes great challenges on cancer treatment. Cancer stem cells (CSCs) are a primary factor in initiating tumor occurrence. However, the mechanisms stem cells in the growth of thyroid cancer (TCHA) are still unclear. Methods: Key genes regulating the stemness characteristics of THCA were identified by combining gene expressions of samples from the Cancer Genome Atlas (TCGA) and machine learning-based methods to establish an mRNA expression stemness index (mRNAsi). The relationships between mRNAsi, THCA clinical features and molecular subtypes were analyzed. Next, mRNAsi-related gene modules were obtained from Weighted Gene Co-Expression Network Analysis (WGCNA) and used to determine mRNAsi-related differentially co-expressed genes. Key genes related to mRNAsi were acquired through protein interaction network. Functional analysis was performed and expressions of key genes were verified using multiple external data sets. Results: The mRNAsi score in TCHA tissues was lower than that in normal tissues (p<0.05), and a lower mRNAsi score was positively correlated with a slow progression of tumor prognosis (p=0.0085). 83 differentially co-expressed genes were screened related to mRNAsi and were associated with multiple tumor pathways such as apoptosis, tight junction, cytokinecytokine receptor interaction, and cAMP signaling pathway (p<0.05). Finally, 28 protein interaction networks involving 32 genes were established, and 3 key genes were identified through network mining. The 3 genes were found closely related to each other, with their low expressions correlating strongly with the progression of THCA. Conclusion: The study found that NGF, FOS, GRIA1 are closely related to the characteristics of THCA stem cells. These genes, especially FOS, are significantly predictive of the prognostic progression of THCA patients. Thus, screening therapy could be use to inhibit the stemness characteristics of TCHA.

scholarly journals Elevated expression of LRRC15 Shows Putative Tumor-promoting Characteristics in tumor microenvironment of breast cancer

2021 ◽  
Author(s):  
Lei Li ◽  
Yin-Jiao Fei ◽  
Ming-Xing Liang ◽  
Hong-Lei Zhou ◽  
Guan-Qun Wo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Proliferation ◽  
Interaction Network ◽  
Gene Interaction ◽  
The Cancer Genome Atlas ◽  
Clinicopathological Parameters ◽  
Receptor Interaction ◽  
Gene Interaction Network ◽  
Kaplan Meier ◽  
Elevated Expression

Abstract Background Leucine-rich repeat containing 15 (LRRC15), belongs to the LRR superfamily and has emerged as a marker of cancer-associated fibroblasts. It was found to be particularly upregulated in breast cancer (BCa). This study aimed to investigate the correlation between LRRC15 expression and immune microenviroment and visualize its prognostic landscape in BCa. Methods The mRNA expression level, prognostic value, correlation of immunity, gene-gene interaction network of LRRC15 in BCa were analyzed utilizing the Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, Kaplan-Meier plotter, and TIMER database. We next analyzed the biological functions of LRRC15 and pathways of its co-expressed genes, and its correlation with immune system responses via the Metascape and GeneMANIA database, respectively. We validated the expression of LRRC15 in BCa via western blot and IHC assays and analyzed its correlation with clinicopathological parameters. Results We explored LRRC15 expression in multiple types of cancer based on the Cancer Genome Atlas (TCGA) database, with the effect being particularly pronounced in BCa. Both mRNA and protein abundance of LRRC15 were significantly elevated in BCa as compared to its non-tumor counterparts. Overexpression of LRRC15 significantly associated with reduced overall survival. LRRC15 knockdown significantly inhibited cell proliferation and cell cycle in BCa cells. There were significant positive correlations between LRRC15 expression and tumor-infiltrating immune cells (TIICs), with a particularly strong effect on macrophage infiltration. Moreover, markers of TIICs exhibited different LRRC15-related immune infiltration patterns. GSEA analysis showed that upregulated expression of LRRC15 was related to ECM receptor interaction, focal adhesion, regulation of actin cytoskeleton, and TGF Beta signaling pathway. Conclusions These findings revealed that LRRC15 served as a novel prognostic biomarker and putative oncogene for BCa by promoting cell proliferation, giving a novel hint for therapeutics of BCa.

scholarly journals Comprehensive analysis of tumor microenvironment landscape and potential therapeutic agents based on tumor-infiltrating immune cells in colorectal cancer

2021 ◽  
Author(s):  
Wenhui Zhong ◽  
Feng Zhang ◽  
Xin Lu ◽  
Kaijun Huang ◽  
Junming Bi ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Transforming Growth Factor ◽  
Immune Cell ◽  
Cancer Therapeutics ◽  
The Cancer Genome Atlas ◽  
Receptor Interaction ◽  
Chemotherapy Drugs ◽  
Drug Compounds

Abstract Background: Tumor-infiltrating immune cells (TIIC) are the major components of the tumor microenvironment (TME) and play vital roles in the tumorigenesis and progression of colorectal cancer (CRC). Increasing evidence has elucidated their significances in predicting prognosis and therapeutic efficacy. Nonetheless, the immune infiltrative landscape of CRC remains largely unknown. Methods: All the RNA-seq transcriptome data and full clinical annotation of 1213 colorectal cancer patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene-Expression Omnibus (GEO) database. The “CIBERSORT” and “estimate” R package were applied to calculate 22 infiltrated immune cell fractions and stromal and immune score. Three TIIC patterns were determined by Unsupervised clustering methods. Through using principal-component analysis, TIIC scores were established. Data for potential agents comes from the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) and Cancer Therapeutics Response Portal database (CTRP). Results：In this study, we identified three distinct TIIC patterns characterized by distinct immunological features in 1213 CRC samples from multiple platforms. Base on the TIIC-related gene signatures from three clusters, we constructed a scoring system to quantify the immune infiltration level of individual samples in the CRC cohort and the clinical benefits of different groups. The high TIIC score group was marked by increased immune activation status and favorable prognosis. Conversely, low TIIC score group was featured with immune-desert phenotype and poor prognosis, along with the activation of transforming growth factor-β (TGF-β), WNT, ECM receptor interaction, and VEGF signaling pathways. Meanwhile, the high TIIC score group was also correlated with enhanced efficacy of immunotherapy. Additional, four chemotherapy drugs, seven CTRP-derived drug compounds and six PRISM-derived drug compounds were identified as potential drug for CRC among high and low TIIC subgroups.Conclusions: Collectively, as an effective prognostic biomarker and predictive indicator, the TIIC score plays an important role in the evaluation of CRC prognosis and the response of immunotherapy. Investigation of the TIIC patterns might provide us a promising target for improving immunotherapeutic efficacy in CRC.

scholarly journals Drug Repurposing Using Modularity Clustering in Drug-Drug Similarity Networks Based on Drug–Gene Interactions

Pharmaceutics ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2117
Author(s):  
Vlad Groza ◽  
Mihai Udrescu ◽  
Alexandru Bozdog ◽  
Lucreţia Udrescu
Keyword(s):  
Interaction Network ◽  
Drug Repurposing ◽  
Gene Interaction ◽  
New Drugs ◽  
Interaction Data ◽  
Gene Interaction Network ◽  
Drug Databases ◽  
Drug Similarity

Drug repurposing is a valuable alternative to traditional drug design based on the assumption that medicines have multiple functions. Computer-based techniques use ever-growing drug databases to uncover new drug repurposing hints, which require further validation with in vitro and in vivo experiments. Indeed, such a scientific undertaking can be particularly effective in the case of rare diseases (resources for developing new drugs are scarce) and new diseases such as COVID-19 (designing new drugs require too much time). This paper introduces a new, completely automated computational drug repurposing pipeline based on drug–gene interaction data. We obtained drug–gene interaction data from an earlier version of DrugBank, built a drug–gene interaction network, and projected it as a drug–drug similarity network (DDSN). We then clustered DDSN by optimizing modularity resolution, used the ATC codes distribution within each cluster to identify potential drug repurposing candidates, and verified repurposing hints with the latest DrugBank ATC codes. Finally, using the best modularity resolution found with our method, we applied our pipeline to the latest DrugBank drug–gene interaction data to generate a comprehensive drug repurposing hint list.

scholarly journals INHBB Is a Novel Prognostic Biomarker Associated with Cancer-Promoting Pathways in Colorectal Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jinpeng Yuan ◽  
Aosi Xie ◽  
Qiangjian Cao ◽  
Xinxin Li ◽  
Juntian Chen
Keyword(s):  
Colorectal Cancer ◽  
Signaling Pathways ◽  
Signaling Pathway ◽  
Transforming Growth Factor ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Disease Free Survival ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Receptor Interaction

Background. Inhibin subunit beta B (INHBB) is a protein-coding gene that participated in the synthesis of the transforming growth factor-β (TGF-β) family members. The study is aimed at exploring the clinical significance of INHBB in patients with colorectal cancer (CRC) by bioinformatics analysis. Methods. Real-time PCR and analyses of Oncomine, Gene Expression Omnibus (GEO), and The Cancer Genome Atlas (TCGA) databases were utilized to evaluate the INHBB gene transcription level of colorectal cancer (CRC) tissue. We evaluated the INHBB methylation level and the relationship between expression and methylation levels of CpG islands in CRC tissue. The corresponding clinical data were obtained to further explore the association of INHBB with clinical and survival features. In addition, Gene Set Enrichment Analysis (GSEA) was performed to explore the gene ontology and signaling pathways of INHBB involved. Results. INHBB expression was elevated in CRC tissue. Although the promoter of INHBB was hypermethylated in CRC, methylation did not ultimately correlate with the expression of INHBB. Overexpression of INHBB was significantly and positively associated with invasion depth, distant metastasis, and TNM stage. Cox regression analyses and Kaplan-Meier survival analysis indicated that high expression of INHBB was correlated with worse overall survival (OS) and disease-free survival (DFS). GSEA showed that INHBB was closely correlated with 5 cancer-promoting signaling pathways including the Hedgehog signaling pathway, ECM receptor interaction, TGF-β signaling pathway, focal adhesion, and pathway in cancer. INHBB expression significantly promoted macrophage infiltration and inhibited memory T cell, mast cell, and dendritic cell infiltration. INHBB expression was positively correlated with stromal and immune scores of CRC samples. Conclusion. INHBB might be a potential prognostic biomarker and a novel therapeutic target for CRC.

scholarly journals The possible role of SRMS in colorectal cancer by bioinformatics analysis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jie Zhang ◽  
Weidong Liu ◽  
Sisi Feng ◽  
Baiyun Zhong
Keyword(s):  
Colorectal Cancer ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Receptor Interaction ◽  
Marker Genes ◽  
Cell Marker ◽  
Translational Initiation ◽  
Expression Levels

Abstract Background Src-related kinase lacking C-terminal regulatory tyrosine and N-terminal myristoylation sites (SRMS) is a non-receptor tyrosine kinase that has been found to be overexpressed in various tumors. However, the role of SRMS in colorectal cancer (CRC) has not been well established. Methods We evaluated the expression levels of SRMS in CRC using GEPIA, Oncomine, and HPA datasets. Survival information and gene expression data of CRC were obtained from The Cancer Genome Atlas (TCGA). Then, the association between SRMS and clinicopathological features was analyzed using UALCAN dataset. LinkedOmics was used to determine co-expression and functional networks associated with SRMS. Besides, we used TISIDB to assess the correlation between SRMS and immune signatures, including tumor-infiltrating immune cells and immunomodulators. Lastly, protein-protein interaction network (PPI) was established and the function enrichment analysis of the SRMS-associated immunomodulators and immune cell marker genes were performed using the STRING portal. Results Compared to normal colorectal tissues, SRMS was found to be overexpressed in CRC tissues, which was correlated with a poor prognosis. In colon adenocarcinoma (COAD), the expression levels of SRMS are significantly correlated with pathological stages and nodal metastasis status. Functional network analysis suggested that SRMS regulates intermediate filament-based processes, protein autophosphorylation, translational initiation, and elongation signaling through pathways involving ribosomes, proteasomes, oxidative phosphorylation, and DNA replication. In addition, SRMS expression was correlated with infiltrating levels of CD4+ T cells, CD56dim, MEM B, Neutrophils, Th2, Th17, and Act DC. The gene ontology (GO) analysis of SRMS-associated immunomodulators and immune cell marker genes showed that they were mainly enriched in the immune microenvironment molecule-related signals. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of these genes indicated that they are involved in multiple cancer-related pathways. Conclusions SRMS is a promising prognostic biomarker and potential therapeutic target for CRC patients. In particular, SRMS regulates CRC progression by modulating cytokine-cytokine receptor interaction, chemokines, IL-17, and intestinal immune networks for IgA production signaling pathways among others. However, more studies are needed to validate these findings.

scholarly journals Identification of Survival-Associated Hub Genes in Pancreatic Adenocarcinoma Based on WGCNA

2022 ◽  
Vol 12 ◽  
Author(s):  
Liya Huang ◽  
Ting Ye ◽  
Jingjing Wang ◽  
Xiaojing Gu ◽  
Ruiting Ma ◽  
...  
Keyword(s):  
Pancreatic Adenocarcinoma ◽  
Biological Networks ◽  
Clinical Symptoms ◽  
Fundamental Problem ◽  
Early Period ◽  
Interaction Network ◽  
Gene Interaction ◽  
The Cancer Genome Atlas ◽  
Gene Interaction Network ◽  
New Genes

Pancreatic adenocarcinoma is one of the leading causes of cancer-related death worldwide. Since little clinical symptoms were shown in the early period of pancreatic adenocarcinoma, most patients were found to carry metastases when diagnosis. The lack of effective diagnosis biomarkers and therapeutic targets makes pancreatic adenocarcinoma difficult to screen and cure. The fundamental problem is we know very little about the regulatory mechanisms during carcinogenesis. Here, we employed weighted gene co-expression network analysis (WGCNA) to build gene interaction network using expression profile of pancreatic adenocarcinoma from The Cancer Genome Atlas (TCGA). STRING was used for the construction and visualization of biological networks. A total of 22 modules were detected in the network, among which yellow and pink modules showed the most significant associations with pancreatic adenocarcinoma. Dozens of new genes including PKMYT1, WDHD1, ASF1B, and RAD18 were identified. Further survival analysis yielded their valuable effects on the diagnosis and treatment of pancreatic adenocarcinoma. Our study pioneered network-based algorithm in the application of tumor etiology and discovered several promising regulators for pancreatic adenocarcinoma detection and therapy.

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