A comprehensive survey for human transcription factors on expression, regulation, interaction, phenotype and cancer survival

Transcription factors (TFs) act as key regulators in biological processes through controlling gene expression. Here, we conducted a systematic study for all human TFs on the expression, regulation, interaction, mutation, phenotype and cancer survival. We revealed that the average expression levels of TFs in normal tissues were lower than 50% expression of non-TFs, whereas TF expression was increased in cancers. TFs that are specifically expressed in an individual tissue or cancer may be potential marker genes. For instance, TGIF2LX/Y were preferentially expressed in testis and NEUROG1, PRDM14, SRY, ZNF705A and ZNF716 were specifically highly expressed in germ cell tumors. We found different distributions of target genes and TF co-regulations in different TF families. Some small TF families have huge protein interaction pairs, suggesting their central roles in transcriptional regulation. The bZIP family is a small family involving many signaling pathways. Survival analysis indicated that most TFs significantly affect survival of one or more cancers. Some survival-related TFs were also specifically highly expressed in the corresponding cancer types, which may be potential targets for cancer therapy. Finally, we identified 43 TFs whose mutations were closely correlated to survival, suggesting their cancer-driven roles. The systematic analysis of TFs provides useful clues for further investigation of TF regulatory mechanisms and the role of TFs in diseases.

miRNASNP-v3: a comprehensive database for SNPs and disease-related variations in miRNAs and miRNA targets

MicroRNAs (miRNAs) related single-nucleotide variations (SNVs), including single-nucleotide polymorphisms (SNPs) and disease-related variations (DRVs) in miRNAs and miRNA-target binding sites, can affect miRNA functions and/or biogenesis, thus to impact on phenotypes. miRNASNP is a widely used database for miRNA-related SNPs and their effects. Here, we updated it to miRNASNP-v3 (http://bioinfo.life.hust.edu.cn/miRNASNP/) with tremendous number of SNVs and new features, especially the DRVs data. We analyzed the effects of 7 161 741 SNPs and 505 417 DRVs on 1897 pre-miRNAs (2630 mature miRNAs) and 3'UTRs of 18 152 genes. miRNASNP-v3 provides a one-stop resource for miRNA-related SNVs research with the following functions: (i) explore associations between miRNA-related SNPs/DRVs and diseases; (ii) browse the effects of SNPs/DRVs on miRNA-target binding; (iii) functional enrichment analysis of miRNA target gain/loss caused by SNPs/DRVs; (iv) investigate correlations between drug sensitivity and miRNA expression; (v) inquire expression profiles of miRNAs and their targets in cancers; (vi) browse the effects of SNPs/DRVs on pre-miRNA secondary structure changes; and (vii) predict the effects of user-defined variations on miRNA-target binding or pre-miRNA secondary structure. miRNASNP-v3 is a valuable and long-term supported resource in functional variation screening and miRNA function studies.

Glycolysis-Related Genes Serve as Potential Prognostic Biomarkers in Clear Cell Renal Cell Carcinoma

Metabolic rearrangement is a marker of cancer that has been widely studied in recent years. One of the major metabolic characteristics of tumor cells is the high levels of glycolysis, even under aerobic conditions, a phenomenon that is called the "Warburg effect." We investigated the expression and copy number variation (CNV) frequency of all glycolysis-related genes in multiple cancer types and found many differentially expressed genes, particularly in clear cell renal cell carcinoma (ccRCC). Single nucleotide variants (SNVs) showed that the overall average mutation frequency for all genes was low. The purpose of this study was to establish a predictive model by studying glycolysis-related genes in ccRCC. We compared the expression of glycolysis-related genes in 539 ccRCC tissues and 72 normal renal tissues from The Cancer Genome Atlas dataset and identified 17 upregulated and 26 downregulated genes. Pathway analysis revealed that PSAT1 and SDHB could activate the cell cycle, RPIA could activate the DNA damage response, and HK3 could activate apoptosis and EMT signaling, while PDK2 could inhibit apoptosis. The results of the drug sensitivity analysis suggested that some of these differentially expressed genes were positively correlated with drug sensitivity. Thirteen genes were selected from the gene coexpression network and the LASSO regression analysis. The Kaplan-Meier overall survival curves showed that the expression of upregulated genes in ccRCC patients was associated with lower overall survival. We established a predictive model consisting of 13 genes (RPIA, G6PD, PSAT1, ENO2, HK3, IDH1, PDK4, PGM2, PGK1, FBP1, OGDH, SUCLA2, and SUCLG2). This predictive model correlated well with the development and progression of ccRCC. Thus, it is of great value in the diagnosis and prognostic evaluation of ccRCC and may aid the identification of potential prognostic biomarkers and drug targets.

Identification of candidate genes encoding tumor-specific neoantigens in early- and late-stage colon adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most common gastrointestinal malignant tumors and is characterized by a high mortality rate. Here, we integrated whole-exome and RNA sequencing data from The Cancer Genome Atlas and investigated the mutational spectra of COAD-overexpressed genes to define clinically relevant diagnostic/prognostic signatures and to unmask functional relationships with both tumor-infiltrating immune cells and regulatory miRNAs. We identified 24 recurrently mutated genes (frequency > 5%) encoding putative COAD-specific neoantigens. Five of them (NEB, DNAH2, ABCA12, CENPF and CELSR1) had not been previously reported as COAD biomarkers. Through machine learning-based feature selection, four early-stage-related (COL11A1, TG, SOX9, and DNAH2) and four late-stage-related (COL11A1, SOX9, TG and BRCA2) candidate neoantigen-encoding genes were selected as diagnostic signatures. They respectively showed 100% and 97% accuracy in predicting early- and late-stage patients, and an 8-gene signature had excellent prognostic performance predicting disease-free survival (DFS) in COAD patients. We also found significant correlations between the 24 candidate neoantigen genes and the abundance and/or activation status of 22 tumor-infiltrating immune cell types and 56 regulatory miRNAs. Our novel neoantigen-based signatures may improve diagnostic and prognostic accuracy and help design targeted immunotherapies for COAD treatment.

GLI3 Promotes Invasion and Predicts Poor Prognosis in Colorectal Cancer

Purpose

The epithelial–mesenchymal transition (EMT) is a key hallmark of cancer which promotes malignant progression, especially during the process of cancer invasion. A better understanding of EMT will help elucidate the molecular mechanism underlying colorectal cancer (CRC) metastasis and may provide new insights into the identification of potential biomarkers and therapeutic targets.

Methods

A series of bioinformatic approaches were combined and identify GLI3 as a potential key regulator in EMT. In vitro experiments were performed to knockdown GLI3 expression in two CRC cell lines and to reveal the oncogenic role of GLI3 in CRC. qRT-PCR and western blot were performed to show the influence of GLI3 in EMT and downstream pathways. The Kaplan-Meier analysis and log-rank test were used to evaluate the prognostic value of GLI3 in CRC patients.

Results

GLI3 was identified as a key regulator in coexpression and protein-protein interaction (PPI) networks involved in EMT. Bioinformatic analyses indicated that GLI3 had a high correlation with EMT markers in CRC. In vitro experiments showed that GLI3 knockdown attenuated the migratory and invasive capacities of CRC cells via influencing EMT property, especially by regulating phosphorylation of ERK signaling pathway. In addition, higher expression of GLI3 predicts worse prognosis in CRC patients.

Conclusions

In summary, we presented the first evidence that GLI3 could promote the migratory and invasive capacities of CRC cells by regulating the EMT process. Our study might provide some useful clues to a better understanding of GLI3 in EMT during CRC progression.

CXCL10 is a potential biomarker and associated with immune infiltration in human papillary thyroid cancer

Background: In recent years, the annual incidence of thyroid cancer (TC) has increased, with papillary thyroid cancer (PTC) identified as the most commonwinwordpathological type accounting for approximately 80% of all thyroid cancer cases. The tumor microenvironment is known to play a vital role in tumor information transmission and immune detection.

Methods: In the present study, we examined gene expression data from 518 patients with PTC. The ESTIMATE algorithm was used to calculate immune and stromal scores of PTC patients. Based on a protein–protein interaction (PPI) network, functional enrichment and overall survival analyses, C-X-C motif chemokine ligand 10 (CXCL10) was identified as a core gene. We further investigated the roles of core genes of PTC in the tumor immune microenvironment using LinkedOmics, GSEA, and TIMER tools.

Results: Immune, stromal and ESTIMATE scores were related to clinicopathological variables of patients with PTC, but not survival outcomes. Eight differentially expressed genes (DEGs) were associated with survival outcome. In addition, immunochemical staining experiments revealed lower expression of CXCL10 in PTC than paracancerous tissues. GSEA pathway enrichment analysis revealed downregulation of CXCL10 in multiple cancer pathways. CXCL10 and related genes were enriched in pathways related to adaptive immune response, cellular defense response and regulation of innate immune response.

Conclusion: The tumor microenvironment plays a critical role in development of PTC and CXCL10 may serve as a novel target of precision therapy for this patient population.

Characteristics and prognostic value of potential dependency genes in clear cell renal cell carcinoma based on a large-scale CRISPR-Cas9 and RNAi screening database DepMap

BACKGROUND

Large-scale loss-of-function screening database such as Cancer Dependency Map (Depmap) provide abundant resources. Investigation of these potential dependency genes from human cancer cell lines in the real-world patients cohort would evaluate their prognostic value thus facilitate their clinical application and guide drug development.

METHODS

A few genes were selected from top clear cell renal cell carcinoma (ccRCC) lineage preferential dependency candidates from Depmap. Their characteristic including expression levels both in normal and tumor tissues and correlations with methylation or copy number, genetic alterations, functional enrichment, immune-associated interactions, prognostic value were evaluated in KIRC cohort from TCGA, GTEx, and multiple other open databases and platforms.

RESULTS

16 genes were collected from 106 ccRCC preferential candidates and further analyzed including B4GALT4, BCL2L1, CDH2, COPG1, CRB3, FERMT2, GET4, GPX4, HNF1B, ITGAV, MDM2, NFE2L2, PAX8, RUVBL1, TFRC, and TNFSF10. The normalized gene effect scores of these genes varied from different ccRCC cell lines and principal component analysis (PCA) showed their tissue specificity expression profiles. Genetic alteration rates of them were low to moderate (0.7%-13%) in KIRC cohort. CDH2, MDM2, TNFSF10 showed a statistically significant higher level in tumors than normal tissues while PAX8 and FERMT2 were significantly downregulated. Moderate positive or negative correlations were observed in several genes between their expression and relative gene copy number or methylation levels, respectively. Based on the multivariable COX regression model adjusted by critical clinical variables revealed the expression of GET4 (p=0.002, HR=1.023 95%CI 1.009-1.038) and CRB3 (p<0.001, HR=0.969 95%CI 0.960-0.980) were independent predictive factors for overall survival in KIRC cohort.

CONCLUSIONS

A dependency gene validated in cell lines didn't directly represent its role in corresponding patients with same histological type and their prognostic value might be determined by multiple factors including dependency driven types, genetic alteration rates and expression levels. GET4 and CRB3 were the independent prognostic factors for ccRCC patients. CRB3 seemed like a potential broad tumor suppressor gene while GET4 might be a ccRCC preferential dependency gene with a ligandable structure.

Immunological Role and Prognostic Value of APBB1IP in Pan-Cancer Analysis

Objective: APBB1IP is a Rap1-binding protein that mainly acts as a regulator of leukocyte recruitment and pathogen clearance through complement-mediated phagocytosis. However, the role of APBB1IP in tumor immunity remains unclear. This study was carried out to evaluate the prognostic landscape of APBB1IP in pan-cancer analysis and investigate the relationship between APBB1IP expression and immune infiltration. Methods: We explored the expression pattern and prognostic value of APBB1IP in pan-cancer analysis through Kaplan-Meier Plotter and multiple databases, including TCGA, Oncomine. We then assessed the correlation between APBB1IP expression and immune cell infiltration using the TIMER database. Furthermore, we identified the proteins that interact with APBB1IP and performed epigenetic and transcriptional analyses. Multivariate Cox regression analyses were applied to construct a prognostic model, which consisted of APBB1IP and its interacting proteins, based on the lung cancer cohorts from the Gene Expression Omnibus (GEO) database. Results: The expression of APBB1IP was correlated with the prognosis of several types of cancer. APBB1IP upregulation was found to be associated with increased immune cell infiltration, especially for CD8+ T cells, natural killer (NK) cells, and immune regulators. A link was found between APBB1IP and immune-related proteins including RAP1A/B, TLN1/2 and VCL in the interaction network. Conclusion: APBB1IP can serve as a prognostic biomarker in pan-cancer analysis. APBB1IP upregulation was correlated with increased immune-cell infiltration, and the expression APBB1IP in different tumors might be related to the tumor immune microenvironment.

Integrative Analysis of Neuregulin Family Members-Related Tumor Microenvironment for Predicting the Prognosis in Gliomas

Gliomas, including brain lower grade glioma (LGG) and glioblastoma multiforme (GBM), are the most common primary brain tumors in the central nervous system. Neuregulin (NRG) family proteins belong to the epidermal growth factor (EGF) family of extracellular ligands and they play an essential role in both the central and peripheral nervous systems. However, roles of NRGs in gliomas, especially their effects on prognosis, still remain to be elucidated. In this study, we obtained raw counts of RNA-sequencing data and corresponding clinical information from 510 LGG and 153 GBM samples from The Cancer Genome Atlas (TCGA) database. We analyzed the association of NRG1-4 expression levels with tumor immune microenvironment in LGG and GBM. GSVA (Gene Set Variation Analysis) was performed to determine the prognostic difference of NRGs gene set between LGG and GBM. ROC (receiver operating characteristic) curve and the nomogram model were constructed to estimate the prognostic value of NRGs in LGG and GBM. The results demonstrated that NRG1-4 were differentially expressed in LGG and GBM in comparison to normal tissue. Immune score analysis revealed that NRG1-4 were significantly related to the tumor immune microenvironment and remarkably correlated with immune cell infiltration. The investigation of roles of m⁶A (N6-methyladenosine, m⁶A)-related genes in gliomas revealed that NRGs were prominently involved in m⁶A RNA modification. GSVA score showed that NRG family members are more associated with prognosis in LGG compared with GBM. Prognostic analysis showed that NRG3 and NRG1 can serve as potential independent biomarkers in LGG and GBM, respectively. Moreover, GDSC drug sensitivity analysis revealed that NRG1 was more correlated with drug response compared with other NRG subtypes. Based on these public databases, we preliminarily identified the relationship between NRG family members and tumor immune microenvironment, and the prognostic value of NRGs in gliomas. In conclusion, our study provides comprehensive roles of NRG family members in gliomas, supporting modulation of NRG signaling in the management of glioma.

Identification of mRNA Prognostic Markers for TGCT by Integration of Co-Expression and CeRNA Network

Testicular germ cell tumor (TGCT) is the most common malignant tumor in young men and is associated with poor prognosis. We assessed the RNA expression profiles of 13 TGCT tissues and 4 adjacent normal tissues by transcriptome sequencing to identify novel prognostic biomarkers. We detected several differentially expressed mRNAs in TGCT that were functionally annotated by GO and KEGG enrichment analyses to tumorigenesis-related processes such as immunity and chemotherapeutic resistance. An mRNA-lncRNA-miRNA regulatory network was constructed using RNA-Seq data and public databases, and integrated with TCGA database to develop a prediction model for metastasis and recurrence. Finally, GRK4, PCYT2 and RGSL1 were identified as predictive markers of survival and therapeutic response. In conclusion, we found several potential predictors for TGCT prognosis and immunotherapeutic response by ceRNA network analysis.

Bioinformatics Analysis of the Prognostic and Biological Significance of ZDHHC-Protein Acyltransferases in Kidney Renal Clear Cell Carcinoma

ZDHHC-protein acyltransferases (ZDHHCs) are a family of 23 signature Asp-His-His-Cys (DHHC) domain-containing enzymes that mediate palmitoylation by covalent attachment of the 16-carbon fatty acid palmitate to thiol groups of specific cysteine residues in substrate proteins. Emerging evidence has shown abnormal expression of ZDHHCs in a variety of disease states, including cancer. Kidney renal clear cell carcinoma (KIRC) is the eighth most common type of cancer, which accounts for the majority of malignant kidney tumors. However, there are currently no effective therapeutic targets or biomarkers for clinical treatment and prognosis in KIRC. In this study, we first analyzed the expression pattern of the 23 ZDHHCs in KIRC using TCGA and GEPIA database, and found that the expression of ZDHHC2, 3, 6, 14, 15, 21, and 23 was significantly down-regulated whereas the expression of ZDHHC9, 17, 18, 19 and 20 was significantly up-regulated in KIRC patient tissues vs. normal tissues. And the expression of ZDHHC2, 3, 6, 9, 14, 15, and 21 in tumors decreased with the increase of the pathological stage of KIRC patients. Notably, KIRC patients with decreased expression of ZDHHC3, 6, 9, 14, 15, 17, 20, 21, 23 and increased expression of ZDHHC19 were significantly associated with poor prognosis. Further, we found that there was a significant correlation between ZDHHC3, 6, 9, 14, 15, 17, 19, 20, 21, 23 expressions and immune cell infiltration. Besides, high mRNA expression was the most common type of gene alteration and there was a high correlation among the expression of ZDHHC6, 17, 20 and 21. Finally, function prediction indicated that the immune or metabolic disorders or the activation of oncogenic signaling pathways caused by abnormal expression of these ZDHHCs may be important mechanisms of tumor progression and poor prognosis in patients with KIRC. Our results may provide novel insight for identifying tumor markers or molecular targets for the treatment of KIRC.

IGFLR1 as a Novel Prognostic Biomarker in Clear Cell Renal Cell Cancer Correlating With Immune Infiltrates

Objective

Insulin Growth Factor-Like receptor 1 (IGFLR1) reflects progressive disease and confers a poor prognosis in clear cell renal cell cancer (ccRCC). However, extensive studies highlighting the mechanisms involved in how IGFLR1 triggers the progression of ccRCC remain lacking.

Methods

In the present study, the expression level of IGFLR1 mRNA and correlation between IGFLR1 expression and prognosis of ccRCC were analyzed based on The Cancer Genome Atlas (TCGA) ccRCC cohort. Further, we analyzed methylation and copy number variation to try to explain the difference in IGFLR1 expression. Subsequently, we investigated the correlation between IGFLR1 and tumor-infiltrating immune cells with the aid of TIMER (Tumor Immune Estimation Resource). The potential candidates' genes associated with IGFLR1 were screened by variation analysis, which were used for further enrichment analysis of signaling pathways and immune gene sets to infer the certain function and corresponding mechanisms in which IGFLR1 was involved in ccRCC. Finally, we establish prognostic risk models using multivariate Cox regression analysis and analyzed the possible involvement of IGFLR1 in chemotherapeutic drug resistance.

Results

The results showed that upregulated IGFLR1 was detected in ccRCC compared with para-cancer tissues and significantly affected the prognosis of ccRCC (overall survival: Logrank p < 0.0001; disease free survival: Logrank p = 0.022). Univariate and multivariate analyses indicated that IGFLR1 was an independent prognostic factor for ccRCC (HR = 2.064, p = 0.006) and the risk prognostic model based on age, M, level of platelet and calcium and IGFLR1 expression had satisfying predictive ability. The correlation analysis showed that the expression level of IGFLR1 was positively correlated with the abundance of myeloid derived suppressor cell and their marker genes in ccRCC significantly. IGFLR1 may be related to the regulatory activation, intercellular adhesion of lymphocytes and drug resistance in cancer.

Conclusion

These findings suggested that IGFLR1 was significantly associated with the prognosis in a variety of cancers, particularly ccRCC. IGFLR1 may play an important role in tumor related immune infiltration and showed potential diagnostic, therapeutic and prognostic value in ccRCC.

Bioinformatics Identification of the Expression and Clinical Significance of E2F Family in Endometrial Cancer

Background

Besides being one of the most prevalent cancers among women, incidence and mortality rates of endometrial cancer (EC) are still increasing. The E2F family of transcriptional factors is involved in cell differentiation, apoptosis, and inhibition of DNA damage response, thus affecting growth and invasion of tumor cells.

Methods

We used multiple bioinformatics tools to explore the role of E2F family in endometrial cancer.

Results

The expression of E2F1/2/3/7/8 was significantly upregulated in endometrial cancer tissues, converse to E2F4, which was downregulated. Methylation downregulates all E2Fs except for E2F2. Accordingly, E2F1/2/3/5/7/8 are potential diagnostic biomarkers for EC. In particular, EC patients displaying upregulated E2F1, and E2F3 expression had a worse overall survival and relapse-free survival. E2F8, E2F7, and E2F1 were the top three, most-frequently altered genes in endometrial cancer. E2F family activates apoptosis pathways, regulates cell cycle, and impairs DNA damage response pathways. Drug-sensitivity analysis demonstrated that the level of E2F2/3/8 negatively correlated with drug resistance. Meanwhile, immune infiltrations analysis revealed that E2F family is associated with recruitment of several immune cells. Enrichment analysis on its part revealed that the E2F family is mainly associated with cell cycle, sequence-specific DNA binding, nuclear transcription factor complex, PI3K-Akt signaling, and p53 signaling pathway. We also identified multiple E2Fs-associated miRNA and kinase targets in endometrial cancer.

Conclusion

Our study revealed the unique expression signature and clinical significance of E2F family in EC, demonstrating the potential clinical utility of these transcription factors (TF) in endometrial cancer.

Mining therapeutic and prognostic significance of STATs in renal cell carcinoma with bioinformatics analysis

Renal cell carcinoma is one of the most common malignancies with high morbidity and mortality. STAT proteins play a significant role in cell biological behavior and immune response associated with cancer progression. In our study, the datasets analyzed for the expression and potential functions can be found in several bioinformatics analysis tools. We found that STAT1/2/4/6 were upregulated in RCC while STAT3/5B were downregulated. The expression of STAT2/4/5B were significantly associated with the pathological stage of RCC patients. RCC patients with high expression of STAT2/4 and low/medium expression of STAT5B had a poor overall survival. The function of STATs and the neighboring genes mainly enriched in JAK-STAT signaling pathway and NOD-like receptor signaling pathway. Several transcription factor, kinase, and miRNA targets were identified. Close correlations were obtained between immune cell infiltration and STATs in RCC. Our results have provided novel insights for the selection of immunotherapeutic targets and prognostic biomarkers.

miR-587 promotes cervical cancer by repressing interferon regulatory factor 6

BACKGROUND

Interferon regulatory factor 6 (IRF6) exhibits tumor-suppressive functions in several cancer types. In the present study, the antitumor properties and related pathway mechanism of IRF6 were investigated in cervical cancer.

METHODS

Forty-one pairs of cervical cancer specimens and para-carcinoma tissues were collected to evaluate IRF6 expression using immunohistochemical staining and miR-587. The effects of miR-587 and IRF6 on cervical cancer cell growth were explored by MTT assays and in a HeLa tumor xenograft mouse model. The migration and invasion of cervical cancer cells were monitored using transwell assays.

RESULTS

IRF6 expression in cervical cancer specimens and cell lines was significantly reduced compared to that in the corresponding control group. In addition, IRF6 expression was negatively correlated with miR-587 in cervical cancer tissues. Bioinformatics algorithms and luciferase assays revealed that IRF6 is a potential target of miR-587, and miR-587 mimic transfection led to a significant repression of IRF6 protein levels in cervical cancer cells. We also discovered that the antineoplastic properties of IRF6 could be reversed by overexpressing miR-587 in cervical cancer cells. The up-regulation of miR-587 was correlated with poor overall survival in cervical cancer. In an in vivo experiment, miR-587 silencing induced HeLa tumor growth inhibition, which was associated with the up-regulation of IRF6 protein in the tumor.

CONCLUSIONS

miR-587 post-transcriptionally represses IRF6 protein expression to abrogate the antineoplastic activity of IRF6. The miR-587/IRF6 signaling pathway plays a crucial role in the progression of cervical cancer and serves as a potential therapeutic target for the treatment of cervical cancer.

SMARCD3 is a potential prognostic marker and therapeutic target in CAFs

OBJECTIVE

Screening for novel prognostic biomarkers and potential therapeutic targets from colorectal cancer microenvironment.

RESULTS

372 genes were overexpressed in colorectal cancer microenvironment, five of which that had the most prognostic powers were enriched in Epithelial-Mesenchymal Transition and cell cycle pathways. For the first time, we showed that SMARCD3 was mainly expressed in CAFs and could be a novel prognostic marker and potential therapeutic target. Function analyses indicated that MSARCD3 might promote CAFs activation and colorectal cancer metastasis through SMARCD3-WNT5A/TGF-β-MAPK14-SMARCD3 positive feedback loop. Signaling map of SMARCD3 was constructed and several potential drugs that could regulate SMARCD3 were also presented.

CONCLUSIONS

SMARCD3 is a novel prognostic biomarker and potential therapeutic target of colorectal cancer, which may promote cancer metastasis through activation of CAFs.

METHODS

Colorectal cancer microenvironment related genes were screened based on immune and stromal scores. Function enrichment analyses were performed to show the underlying mechanistic insights of these tumor microenvironment related genes. Kaplan-Meier survival analysis was used for evaluating the prognostic power. Gene-Pathway interaction network analysis and cellular heterogeneity analysis of tumor microenvironment were also performed. Gene set enrichment analysis was performed for signal gene pathway analysis. Protein data from The Cancer Genome Atlas were used for validation.

Tripartite-motif family genes associated with cancer stem cells affect tumor progression and can assist in the clinical prognosis of kidney renal clear cell carcinoma

Ubiquitination is presently a hot topic in the field of oncology. The tripartite-motif (TRIM) family of proteins represents one of the largest classes of putative single protein RING-finger E3 ubiquitin ligases, which play an essential role in the ubiquitination of proteins in the body. At the same time, research related to cancer stem cells (CSCs) is increasing in popularity in the field of oncology. CSCs are potentially chemically resistant and can be selectively enriched in patients receiving chemotherapy, ultimately leading to adverse outcomes, such as treatment failure and cancer recurrence. There is a close relationship between multiple TRIM family genes and CSCs. Accumulating evidence suggests that TRIM family proteins are expressed in diverse human cancers and act as regulators of oncoproteins or tumor suppressor proteins. In this study, we used biological information to explore the potential function of TRIM family genes related to CSCs in the development of pan-cancer. Kidney renal clear cell carcinoma (KIRC) is one of the deadliest malignant tumors in the world. Owing to its complex molecular and cellular heterogeneity, the effectiveness of existing KIRC-related risk prediction models is not satisfactory at present. Therefore, we focused on the potential role of these TRIM family genes in KIRC and used seven TRIM family genes to establish a prognostic risk model. This model includes TRIM16, TRIM32, TRIM24, TRIM8, TRIM27, PML, and TRIM11. In conclusion, this study provides further insight into the prognosis of KIRC, which may guide treatment.

The nine ADAMs family members serve as potential biomarkers for immune infiltration in pancreatic adenocarcinoma

Background

The functional significance of ADAMs family members in the immune infiltration of pancreatic adenocarcinoma (PAAD) awaits elucidation.

Methods

ADAMs family members with significant expression were identified among differentially expressed genes of PAAD based on The Cancer Genome Atlas (TCGA) database followed by a verification based on the Oncomine database. The correlation of ADAMs in PAAD was estimated with the Spearman’s rho value. The pathway enrichment of ADAMs was performed by STRING and GSEALite, respectively. The protein–protein interaction and Gene Ontology analyses of ADAMs and their similar genes were exanimated in STRING and visualized by Cytoscape. Subsequently, the Box-Whisker plot was used to show a correlation between ADAMs and different tumor grade 1/2/3/4 with Student’s t-test. TIMER was applied to estimate a correlation of ADAMs expressions with immune infiltrates and immune checkpoint blockade (ICB) immunotherapy-related molecules. Furthermore, the effect of copy number variation (CNV) of ADAMs genes was assessed on the immune infiltration levels.

Result

ADAM8/9/10/12/15/19/28/TS2/TS12 were over-expressed in PAAD. Most of the nine ADAMs had a significant correlation. ADAM8/12/15/19 expression was remarkably increased in the comparison between grade 1 and grade 2/3 of PAAD. ADAM8/9/10/12/19/28/TS2/TS12 had a positive correlation with almost five immune infiltrates. ADAM12/19/TS2/TS12 dramatically related with ICB immunotherapy-related molecules. CNV of ADAMs genes potentially influenced the immune infiltration levels.

Conclusion

Knowledge of the expression level of the ADAMs family could provide a reasonable strategy for improved immunotherapies to PAAD.

Potential relationship between the selenoproteome and cancer

The role of the selenoproteome, which is the collection of all proteins containing selenium in an organism, in cancer development, growth and progression requires further investigation, due to the importance of selenium in both cancer and immune system function. Data about the selenoproteome, including its differential expression, single nucleotide variations, copy number variations, methylation, pathways and overall survival (OS) in five leading types of cancer are available from the GSCALite website. Subsequent to the analysis of these datasets, it was revealed that there was increased expression of GPX3 in stomach adenocarcinoma and lung squamous cell carcinoma, SELENOV in oesophageal carcinoma, GPX8 and GPX4 in colon adenocarcinoma, TXNRD1 and SEPHS1 in hepatocellular carcinoma and GPX8 in lung adenocarcinoma were associated with poor survival. Decreased gene expression of SELENOP was indicated in liver hepatocellular carcinoma and GPX3, and SELENOW, SELENOK, SELENBP1 and SECISBP2 in lung adenocarcinoma were associated with a poor prognosis. OS data suggested that hypermethylation of GPX4 in colon adenocarcinoma, GPX8 in lung squamous cell carcinoma, GPX1 in stomach adenocarcinoma and GPX3 in lung adenocarcinoma was associated with low survival, as is hypomethylation of GPX5 in lung adenocarcinoma. The selenoproteome is heterogeneous, especially in its effect on the OS of patients with cancer. The present study demonstrated that the roles of GPX4 in colon adenocarcinoma, SCLY and SELENOV in oesophageal carcinoma, SEPHS1 in liver hepatocellular carcinoma, SELENOK in lung cancer, as well as SELENOM and SELENOW in stomach adenocarcinoma requires further research. The present study may lead to the identification of novel biomarkers or potential therapeutic targets for use in the treatment of cancers, such as colon adenocarcinoma, oesophageal carcinoma, liver hepatocellular carcinoma, lung cancer and stomach adenocarcinoma.

Genetic alteration and clinical significance of SUMOylation regulators in multiple cancer types

The purpose of this study was to investigate the genetic variation, gene expression differences, and clinical significance of SUMOylation regulators in pan-cancers. Based on previous studies, we gained a better understanding of the biological process of SUMOylation and the status of current research. In the present study, we employed a wide range of bioinformatics methods. We used genetic variation and mRNA expression data in the Cancer Genome Atlas (TCGA) to construct a panoramic view of the single nucleotide variants, copy number variants, and gene expression changes in SUMOylation regulators in various tumors. Subsequently, we used the String website and the Cytoscape tool to construct the PPI network between these regulators. We used the GSCALite website to determine the relationship between these regulators and cancer pathways and drug sensitivity. We constructed images of co-expression between these regulators using the R programming language. Using clinical data from TCGA, we performed hazard ratio analysis for these regulators in pan-cancer. Most importantly, we used these regulators to successfully establish risk signatures related to patient prognosis in multiple tumors. Finally, in KIRC, we conducted gene-set enrichment analysis (GSEA) of the five molecules in its risk signatures. We found that these five molecules are involved in multiple cancer pathways. In short, we have comprehensively interpreted the detailed biological process of SUMOylation at the genetic level for the first time, successfully constructed multiple risk signatures, and conducted GSEA in KIRC. We believe that these findings provide credible and valuable information that is relevant for future clinical diagnoses and scientific research.

ERO1L promotes NSCLC development by modulating cell cycle-related molecules

Lung cancer is the leading cause of cancer‐related death worldwide. Previous studies revealed that endoplasmic reticulum oxidoreductase 1 alpha (ERO1L) played critical roles in the malignant behaviors of several cancer types, but its role in non‐small cell lung cancer (NSCLC) remained unclear. In this study, we identified 26 upregulated and 102 downregulated genes in NSCLC using bioinformatics analyses, and these genes were enriched in the biological processes of the cell cycle. ERO1L was remarkably upregulated in NSCLC and overexpression of ERO1L was associated with poor prognosis of NSCLC. ERO1L deficiency markedly suppressed NSCLC cell proliferation, colony formation, migration, and invasion. ERO1L depletion caused a dramatically decreased expression of cell cycle‐related factors in NSCLC cells. Collectively, our data validated that ERO1L could function as a tumor promoter in NSCLC, indicating the potential of targeting ERO1L for the treatment of NSCLC.

Mining Database for the Expression and Clinical Significance of NF-κB Family in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the deadliest diseases affecting humans. Its incidence has been increasing over the last decade. It is characterized by poor prognosis as well as lack of therapeutic regimens for patients in the advanced stages. It is therefore important to develop effective biomarkers for diagnosis, prognosis, and immunotherapy of HCC. Research suggests that the NF-κB family plays vital roles in immune response, inflammation, tumorigenesis, and the progress of malignancy in various cancers. However, its role in HCC remains unidentified. Methodology. The expression and clinical significance of the NF-κB family in HCC were analyzed using several bioinformatics tools including UALCAN, The Human Protein Atlas, GEPIA, GSCALite, David, GeneMANIA, and TIMER.

Results

The mRNA expression levels of RelA, RelB, NF-κB1, and NF-κB2 were significantly elevated in HCC. The mRNA levels of RelB and NF-κB2 were significantly upregulated in HCC tissues compared to normal liver tissues in subgroup analyses based on patient's race, gender, age, weight, tumor grade, cancer stage, and nodal metastasis status. Moreover, HCC patients with elevated levels of RelB and NF-κB2 had a worse overall survival and disease-free survival. Methylation downregulated the expressions of RelA, RelB, and NF-κB1 in HCC. NF-κB family was also significantly involved in various hallmark cancer-related pathways such as the apoptosis, EMT, RTK, and cell cycle pathways. Similarly, the expression of RelB and NF-κB2 was positively correlated with the abundance of immune cells and the expression of immune biomarkers. Several kinase and miRNA targets of RelB and NF-κB2 were also identified.

Conclusion

RelB and NF-κB2 are potential biomarkers for the diagnosis, prognosis, and immunotherapy of HCC.

Mining database for the clinical significance and prognostic value of CBX family in skin cutaneous melanoma

BACKGROUND

Skin cutaneous melanoma (SKCM) is one of the most aggressive malignancies with high invasiveness. Chromobox (CBX) family are involved in the regulation of the tumorigenesis, progression, invasion, and apoptosis of many malignancies.

METHODS

The clinical significance and prognostic value of CBX family in SKCM were analyzed via a series of databases, including ONCOMINE, GEPIA, UALCAN, TIMER, GSCALite, DAVID 6.8, GeneMANIA, and LinkedOmics.

RESULTS

We found that the level of CBX2, CBX3, CBX5, and CBX6 was upregulated while the level of CBX7 and CBX8 was downregulated in tumor tissues in SKCM. Moreover, the mRNA expression of CBX1 and CBX2 was significantly associated with the pathological stage in SKCM. Prognosis analysis revealed that SKCM patients with high CBX5 level and low CBX7 level had a poor prognosis. Immune infiltrations analysis revealed that the expression of CBX family was associated with the abundance of certain immune cells in SKCM. We also found that CBX family were associated with the activation of cell cycle pathway and DNA damage response, and the inhibition of apoptosis pathway. Moreover, enrichment analysis revealed that CBX family and correlated genes were enriched in chromatin modification, PcG protein complex, transcription coactivator activity, protein binding, and RNA splicing. Several Kinase targets (ATM, CDK1, and PLK1) and miRNA targets (MIR-331, MIR-296, and MIR-496) of CBX family were also identified.

CONCLUSION

Our study may uncover CBX family-associated molecular mechanisms involved in the tumorigenesis and progression of SKCM and provide additional choice for the prognosis and therapy biomarker for SKCM.

hTFtarget: A Comprehensive Database for Regulations of Human Transcription Factors and Their Targets

Transcription factors (TFs) as key regulators play crucial roles in biological processes. The identification of TF–target regulatory relationships is a key step for revealing functions of TFs and their regulations on gene expression. The accumulated data of chromatin immunoprecipitation sequencing (ChIP-seq) provide great opportunities to discover the TF–target regulations across different conditions. In this study, we constructed a database named hTFtarget, which integrated huge human TF target resources (7190 ChIP-seq samples of 659 TFs and high-confidence binding sites of 699 TFs) and epigenetic modification information to predict accurate TF–target regulations. hTFtarget offers the following functions for users to explore TF–target regulations: (1) browse or search general targets of a query TF across datasets; (2) browse TF–target regulations for a query TF in a specific dataset or tissue; (3) search potential TFs for a given target gene or non-coding RNA; (4) investigate co-association between TFs in cell lines; (5) explore potential co-regulations for given target genes or TFs; (6) predict candidate TF binding sites on given DNA sequences; (7) visualize ChIP-seq peaks for different TFs and conditions in a genome browser. hTFtarget provides a comprehensive, reliable and user-friendly resource for exploring human TF–target regulations, which will be very useful for a wide range of users in the TF and gene expression regulation community. hTFtarget is available at http://bioinfo.life.hust.edu.cn/hTFtarget.

Expression profile of immune checkpoint genes and their roles in predicting immunotherapy response

Immune checkpoint genes (ICGs) play critical roles in circumventing self-reactivity and represent a novel target to develop treatments for cancers. However, a comprehensive analysis for the expression profile of ICGs at a pan-cancer level and their correlation with patient response to immune checkpoint blockade (ICB) based therapy is still lacking. In this study, we defined three expression patterns of ICGs using a comprehensive survey of RNA-seq data of tumor and immune cells from the functional annotation of the mammalian genome (FANTOM5) project. The correlation between the expression patterns of ICGs and patients survival and response to ICB therapy was investigated. The expression patterns of ICGs were robust across cancers, and upregulation of ICGs was positively correlated with high lymphocyte infiltration and good prognosis. Furthermore, we built a model (ICGe) to predict the response of patients to ICB therapy using five features of ICG expression. A validation scenario of six independent datasets containing data of 261 patients with CTLA-4 and PD-1 blockade immunotherapies demonstrated that ICGe achieved area under the curves of 0.64-0.82 and showed a robust performance and outperformed other mRNA-based predictors. In conclusion, this work revealed expression patterns of ICGs and underlying correlations between ICGs and response to ICB, which helps to understand the mechanisms of ICGs in ICB signal pathways and other anticancer treatments.

Aberrant methylation and microRNA-target regulation are associated with downregulated NEURL1B: a diagnostic and prognostic target in colon cancer

BACKGROUND

Aberrant methylation and miRNA-target-gene regulation function as important mechanisms for gene inactivation in colon carcinogenesis. Although a serious of molecular events (such as aberrant alterations of genomics and epigenetics) have been identified to be related to prognostic in colon cancer (CC) patients, beneficial biomarkers for early diagnosis and prognostic evaluation remain largely unknown.

METHODS

In our study, the role of NEURL1B, including gene expression analysis, methylation characteristic, miRNA-target regulation, diagnostic and prognostic significance, were evaculated using multiple bioinformatic tools based on TCGA database and clinical samples.

RESULTS

Our data showed that NEURL1B was aberrantly downregulated in CC, regardless of the mRNA level or protein level. Moreover, ROC curve and multivariate Cox regression analysis demonstrated that NEURL1B was a diagnostic and independent prognostic facter for CC patients. Of interest, methylation of NEURL1B was also high and closely associated with poor survival in CC. In addition, multiple NEURL1B-target miRNAs were found to be overexpressed in CC tissues. Thus, our findings suggested that NEURL1B participated in the pathological processes of CC as a tumor suppressor gene. Double management, including DNA methylation modification and miRNA-target regulation, were considered to be related to the downregulation of NEURL1B. Importantly, there existing be an significant intersection between miRNAs-target pathways and NEURL1B-target pathways, suggesting that miR-17 and miR-27a might promote tumor cell malignant property by targeting NEURL1B degradation via the activation of PI3K/AKT signaling pathway.

CONCLUSIONS

Taking together, the first investigation of NEURL1B in CC provide us a strong evidences that it might be served as a potential biomarkers for early diagnosis and prognostic evaluation in CC.

A new survival model based on ferroptosis-related genes for prognostic prediction in clear cell renal cell carcinoma

In this study, we analyzed the clinical significance of ferroptosis-related genes (FRGs) in 32 cancer types in the GSCA database. We detected a 2-82% mutation rate among 36 FRGs. In clear cell renal cell carcinoma (ccRCC; n=539) tissues from the The Cancer Genome Atlas database, 30 of 36 FRGs were differentially expressed (up- or down-regulated) compared to normal kidney tissues (n=72). Consensus clustering analysis identified two clusters of FRGs based on similar co-expression in ccRCC tissues. We then used LASSO regression analysis to build a new survival model based on five risk-related FRGs (CARS, NCOA4, FANCD2, HMGCR, and SLC7A11). Receiver operating characteristic curve analysis confirmed good prognostic performance of the new survival model with an area under the curve of 0.73. High FANCD2, CARS, and SLC7A11 expression and low HMGCR and NCOA4 expression were associated with high-risk ccRCC patients. Multivariate analysis showed that risk score, age, stage, and grade were independent risk factors associated with prognosis in ccRCC. These findings demonstrate that this five risk-related FRG-based survival model accurately predicts prognosis in ccRCC patients, and suggest FRGs are potential prognostic biomarkers and therapeutic targets in several cancer types.

Shiny-DEG: A Web Application to Analyze and Visualize Differentially Expressed Genes in RNA-seq

RNA-seq analysis has become one of the most widely used methods for biological and medical experiments, aiming to identify differentially expressed genes at a large scale. However, due to lack of programming skills and statistical background, it is difficult for biologists including faculty and students to fully understand what the RNA-seq results are and how to interpret them. In recent years, even though, there are several programs or websites that assist researchers to analyze and visualize NGS results, they have several limitations. Therefore, Shiny-DEG, a web application that facilitates the exploration and visualization of differentially expressed genes from RNA-seq, was developed. It integrates multi-factor design experiments, allows users to modify the parameters interactively according to experiments purpose and all analysis results can be downloaded directly, aiming to further assisting the interpretation and explanation of the biological questions. Therefore, it serves better for biologists without programming skills. Overall, this project is of great significance to reveal the mechanism of transcriptome differences.

The expression and regulation of HOX genes and membrane proteins among different cytogenetic groups of acute myeloid leukemia

BACKGROUND

The cytogenetic aberrations were considered as markers for diagnosis and prognosis in acute myeloid leukemia (AML), while the expression and regulation under different cytogenetic groups remain to be fully elucidated.

METHODS

In this paper, for favorable, poor, and cytogenetically normal groups of AML patients, we performed comprehensive bioinformatics analyses including identifying differentially expressed genes (DEGs) and microRNAs (miRNAs) among them, functional enrichment and regulatory networks.

RESULTS

We found that DEGs were enriched in membrane-related processes. Eleven genes and two miRNAs were significantly differentially expressed among these three AML groups. In survival analysis, membrane-related genes and several miRNAs were significant on prognostic outcome. Notably, six HOXA and three HOXB genes were significantly in low expression and high methylation in AML with favorable cytogenetics. Meanwhile, the miRNA-HOX gene co-regulatory networks revealed that HOXA5 was a hub node and regulated an AML oncogene SPARC.

CONCLUSION

Our work may provide novel insights to the molecular characteristics and classification between AML with different cytogenetics.

Development and validation of a hypoxia-related gene signature to predict overall survival in early-stage lung adenocarcinoma patients

Background:

Patients with early-stage lung adenocarcinoma (LUAD) exhibit significant heterogeneity in overall survival. The current tumour-node-metastasis staging system is insufficient to provide precise prediction for prognosis.

Methods:

We quantified the levels of various hallmarks of cancer and identified hypoxia as the primary risk factor for overall survival in early-stage LUAD. Different bioinformatic and statistical methods were combined to construct a robust hypoxia-related gene signature for prognosis. Furthermore, a decision tree and a nomogram were constructed based on the gene signature and clinicopathological features to improve risk stratification and quantify risk assessment for individual patients.

Results:

The hypoxia-related gene signature discriminated high-risk patients at an early stage in our investigated cohorts. Survival analyses demonstrated that our gene signature served as an independent risk factor for overall survival. The decision tree identified risk subgroups powerfully, and the nomogram exhibited high accuracy.

Conclusions:

Our study might contribute to the optimization of risk stratification for survival and personalized management of early-stage LUAD.

The correlation and role analysis of KCNK2/4/5/15 in Human Papillary Thyroid Carcinoma microenvironment

Background: KCNKs, potassium two pore domain channel family K members, can maintain the resting potential, regulate the amplitude and duration of the plateau of the action potential, and change the membrane potential and membrane excitability. Evidence from many studies indicates that KCNKs is abnormally expressed in many solid tumors and plays a regulatory role in the development and malignant progression of cancer. However, the expression pattern and prognostic value of KCNK factors in papillary thyroid carcinoma have not been reported.

Methods: In this study, we used the data from databases such as ONCOMINE, GEPIA, Kaplan-Meier Plotter, and cBioPortal to perform bioinformatics analysis of KCNK factors in patients with thyroid cancer.

Results: We found that the mRNA expression of KCNK1, KCNK5, KCNK6, KCNK7, and KCNK15 were significantly higher in thyroid cancer tissues than that in normal tissues, while KCNK2, KCNK4, KCNK9, KCNK16 and KCNK17 mRNA levels were decreased compared to normal tissues. And the expression levels of KCNK1/2/4/5/6/7/15 were correlated with the tumor stage. Survival analysis using the Kaplan-Meier Plotter database revealed that KCNK2/3/4/5/12/15 were associated with overall survival (OS) in patients with thyroid cancer.

Conclusion: Finally, the results of ROC curves, immunohistochemical staining, immune cell infiltration and kinase / miRNA / transcription factor regulation showed that KCNK2, KCNK4, KCNK5 and KCNK15 levels could be used as biomarkers for PTC diagnosis. This study implied that KCNK2, KCNK4, KCNK5 and KCNK15 are potential targets of precision therapy for patients with thyroid cancer and these genes are new biomarkers for the therapeutic target for thyroid cancer.

JAK3 is a potential biomarker and associated with immune infiltration in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is one of the most common cancers globally, with an overall poor prognosis. The Janus kinase (JAK) family plays an essential role in cellular mechanisms such as proliferation, metastasis, invasion, and immunity. In our study, various web-portals were used to explore the expression and clinical significance of JAK3 in KIRC. JAK3 expression was significantly up-regulated in KIRC tissues. Patients with KIRC having high JAK3 levels displayed a substantially decreased disease-free survival rate and overall survival rate. Significant correlations were obtained between JAK3 expression and the abundance of immune cells and immune biomarker sets. Enrichment function analysis revealed that gene function significantly correlated with JAK3, which was primarily associated with the immune response, JAK-STAT signaling pathway, Ras signaling pathway via several cancer-related kinases, miRNAs, and transcription factors. Moreover, we also identified several kinase, miRNA or transcription factor targets of JAK3 in KIRC. The hub genes (JAK3, FCHO1, INSl3, DEF6, and GPR132) were associated with the activation or inhibition of several famous cancer related pathways. Our results demonstrated that JAK3 is a potential biomarker and associated with immune infiltration in KIRC.

An Analysis Regarding the Association Between the ISLR Gene and Gastric Carcinogenesis

For datasets of gastric cancer collected by TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus) repositories, we applied a bioinformatics approach to obtain expression data for the ISLR (immunoglobulin superfamily containing leucine-rich repeat) gene, which is highly expressed in gastric cancer tissues and closely associated with clinical prognosis. Although we did not observe an overall association of ISLR mutation, high expression or copy number variation with survival, hypomethylation of four methylated sites (assessed by the probes cg05195566, cg17258195, cg09664357, and cg07297039) of ISLR was negatively correlated with high expression levels of ISLR and was associated with poor clinical prognosis. In addition, we detected a correlation between ISLR expression and the infiltration levels of several immune cells, especially CD8⁺ T cells, macrophages and dendritic cells. We also identified a series of genes that were positively and negatively correlated with ISLR expression based on the TCGA-STAD, GSE13861, and GSE29272 datasets. Principal component analysis and random forest analysis were employed to further screen for six hub genes, including ISLR, COL1A2, CDH11, SPARC, COL3A1, and COL1A1, which exhibited a good ability to differentiate between tumor and normal samples. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and gene set enrichment analysis data also suggested a potential relationship between ISLR gene expression and epithelial-mesenchymal transition (EMT). ISLR expression was negatively correlated with sensitivity to PX-12 and NSC632839. Taken together, these results show that the ISLR gene is involved in gastric carcinogenesis, and the underlying molecular mechanisms may include DNA methylation, EMT, and immune cell infiltration.

Time serial transcriptome reveals Cyp2c29 as a key gene in hepatocellular carcinoma development

Objective: Hepatocellular carcinoma (HCC) is a severely lethal cancer that usually originates from chronic liver injury and inflammation. Although progress on diagnosis and treatment is obvious, the cause of HCC remains unclear. In this study, we sought to determine key genes in HCC development. Methods: To identify key regulators during HCC progression, we performed transcriptome sequencing to obtain time series gene expression data from a mouse model with diethylnitrosamine-induced liver tumors and further verified gene expression and function in vitro and in vivo. Results: Among the differentially expressed genes, Cyp2c29 was continuously downregulated during HCC progression. Overexpression of Cyp2c29 suppressed NF-κB activation and proinflammatory cytokine production by increasing the production of 14,15-epoxyeicosatrienoic acid in vitro. Furthermore, overexpression of Cyp2c29 in vivo protected against liver inflammation in mouse models of liver injury induced by both acetaminophen and CCl4. Two human homologs of mouse Cyp2c29, CYP2C8 and CYP2C9, were found to be downregulated in human HCC progression, and their expression was positively correlated with overall survival in patients with HCC (significance: P = 0.046 and 0.0097, respectively). Conclusions: Collectively, through systematic analysis and verification, we determined that Cyp2c29 is a novel gene involved in liver injury and inflammation, which may be a potential biomarker for HCC prevention and prognosis determination.

AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors

The Animal Transcription Factor DataBase (AnimalTFDB) is a resource aimed to provide the most comprehensive and accurate information for animal transcription factors (TFs) and cofactors. The AnimalTFDB has been maintained and updated for seven years and we will continue to improve it. Recently, we updated the AnimalTFDB to version 3.0 (http://bioinfo.life.hust.edu.cn/AnimalTFDB/) with more data and functions to improve it. AnimalTFDB contains 125,135 TF genes and 80,060 transcription cofactor genes from 97 animal genomes. Besides the expansion in data quantity, some new features and functions have been added. These new features are: (i) more accurate TF family assignment rules; (ii) classification of transcription cofactors; (iii) TF binding sites information; (iv) the GWAS phenotype related information of human TFs; (v) TF expressions in 22 animal species; (vi) a TF binding site prediction tool to identify potential binding TFs for nucleotide sequences; (vii) a separate human TF database web interface (HumanTFDB) was designed for better utilizing the human TFs. The new version of AnimalTFDB provides a comprehensive annotation and classification of TFs and cofactors, and will be a useful resource for studies of TF and transcription regulation.

EVmiRNA: a database of miRNA profiling in extracellular vesicles

Extracellular vesicles (EVs), such as exosomes and microvesicles, acted as cell-to-cell communication vectors and potential biomarkers for diseases. microRNAs (miRNAs) are the most well studied molecules in EVs, thus a comprehensive investigation of miRNA expression profiles in EVs will be helpful to explore their functions and biomarkers. We curated 462 small RNA sequencing samples of EVs from 17 sources/diseases and constructed the EVmiRNA database (http://bioinfo.life.hust.edu.cn/EVmiRNA) to show the miRNA expression profiles. We found >1000 miRNAs expressed in these EVs and detected specific miRNAs for EVs of each source/disease. EVmiRNA provides three functional modules: (i) the miRNA expression profiles and the sample information of EVs from different sources (such as blood, breast milk etc.); (ii) the specifically expressed miRNAs in different EVs that would be helpful for biomarker identification; (iii) the miRNA annotations including the miRNA expression in EVs and TCGA cancer types, miRNA pathway regulations as well as miRNA function and publications. EVmiRNA has a user-friendly web interface with powerful browse and search functions, as well as data downloading. It is the first database focusing on miRNA expression profiles in EVs and will be useful for the research and application community of EV biomarker, miRNA function and liquid biopsy.

Identification of Crucial Genes Associated With Immune Cell Infiltration in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis

The dreadful prognosis of hepatocellular carcinoma (HCC) is primarily due to the low early diagnosis rate, rapid progression, and high recurrence rate. Valuable prognostic biomarkers are urgently needed for HCC. In this study, microarray data were downloaded from GSE14520, GSE22058, International Cancer Genome Consortium (ICGC), and The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were identified among GSE14520, GSE22058, and ICGC databases. Weighted gene co-expression network analysis (WGCNA) was used to establish gene co-expression modules of DEGs, and genes of key modules were examined to identify hub genes using univariate Cox regression in the ICGC cohort. Expression levels and time-dependent receiver operating characteristic (ROC) and area under the curve (AUC) were determined to estimate the prognostic competence of the hub genes. These hub genes were also validated in the Gene Expression Profiling Interactive Analysis (GEPIA) and TCGA databases. TIMER algorithm and GSCALite database were applied to analyze the association of the hub genes with immunocytotic infiltration and their pathway enrichment. Altogether, 276 DEGs were identified and WGCNA described a unique and significantly DEGs-associated co-expression module containing 148 genes, with 10 hub genes selected by univariate Cox regression in the ICGC cohort (BIRC5, FOXM1, CENPA, KIF4A, DTYMK, PRC1, IGF2BP3, KIF2C, TRIP13, and TPX2). Most of the genes were validated in the GEPIA databases, except IGF2BP3. The results of multivariate Cox regression analysis indicated that the abovementioned hub genes are all independent predictors of HCC. The 10 genes were also confirmed to be associated with immune cell infiltration using the TIMER algorithm. Moreover, four-gene signature was developed, including BIRC5, CENPA, FOXM1, DTYMK. These hub genes and the model demonstrated a strong prognostic capability and are likely to be a therapeutic target for HCC. Moreover, the association of these genes with immune cell infiltration improves our understanding of the occurrence and development of HCC.

SOD1 Promotes Cell Proliferation and Metastasis in Non-small Cell Lung Cancer via an miR-409-3p/SOD1/SETDB1 Epigenetic Regulatory Feedforward Loop

Superoxide dismutase 1(SOD1) is a major antioxidant with oncogenic effects in many human cancers. Although SOD1 is overexpressed in various cancers, the clinical significance and functions of SOD1 in non-small cell lung cancer (NSCLC), particularly the epigenetic regulation of SOD1 in NSCLC carcinogenesis and progression have been less well investigated. In this study, we found that SOD1 expression was upregulated in NSCLC cell lines and tissues. Further, elevated SOD1 expression could promote NSCLC cell proliferation, invasion and migration. While inhibition of SOD1 expression induced NSCLC G1-phase cell cycle arrest and promoted apoptosis. In addition, miR-409-3p could repress SOD1 expression and significantly counteract its oncogenic activities. Bioinformatics analysis indicated that SET domain bifurcated histone lysine methyltransferase1 (SETDB1) was involved in the epigenetic regulation of miR-409-3p and SOD1 expression and functions in NSCLC cells. Identification of this miR-409-3p/SOD1/SETDB1 epigenetic regulatory feedforward loop may provide new insights into further understanding of NSCLC tumorigenesis and progression. Additionally, our results incicate that SOD1 may be a potential new therapeutic target for NSCLC treatment.

Identification of potential crucial genes associated with the pathogenesis and prognosis of prostate cancer

Aim: Prostate cancer (PCa) is the sixth leading cause of cancer-related deaths in men throughout the world. This study aimed to investigate genes associated with the pathogenesis and prognosis of PCa. Materials & methods: Data of PCa cases were obtained from public datasets and were analyzed using an integrated bioinformatics strategy. Results: A total of 969 differential expression genes were identified. Moreover, GSE16560 and The Cancer Genome Atlas (TCGA) data showed a prognostic prompt function of the nine-gene signature, as well as in PCa with Gleason 7. Finally, majority of the nine hub genes were associated with drug sensitivity, mutational landscape, immune infiltrates and clinical characteristics of PCa. Conclusion: The nine-gene signature was correlated with drug sensitivity, mutational landscape, immune infiltrates, clinical characteristics and survival from PCa.

The pan-cancer landscape of netrin family reveals potential oncogenic biomarkers

Recent cancer studies have found that the netrin family of proteins plays vital roles in the development of some cancers. However, the functions of the many variants of these proteins in cancer remain incompletely understood. In this work, we used the most comprehensive database available, including more than 10000 samples across more than 30 tumor types, to analyze the six members of the netrin family. We performed comprehensive analysis of genetic change and expression of the netrin genes and analyzed epigenetic and pathway relationships, as well as the correlation of expression of these proteins with drug sensitivity. Although the mutation rate of the netrin family is low in pan-cancer, among the tumor patients with netrin mutations, the highest number are Uterine Corpus Endometrial Carcinoma patients, accounting for 13.6% of cases (54 of 397). Interestingly, the highest mutation rate of a netrin family member is 38% for NTNG1 (152 of 397). Netrin proteins may participate in the development of endocrine-related tumors and sex hormone-targeting organ tumors. Additionally, the participation of NTNG1 and NTNG2 in various cancers shows their potential for use as new tumor markers and therapeutic targets. This analysis provides a broad molecular perspective of this protein family and suggests some new directions for the treatment of cancer.

Identification of cross-talk between m6A and 5mC regulators associated with onco-immunogenic features and prognosis across 33 cancer types

Methylation of RNA and DNA, notably in the forms of N6-methyladenosine (m⁶A) and 5-methylcytosine (5mC) respectively, plays crucial roles in diverse biological processes. Currently, there is a lack of knowledge regarding the cross-talk between m⁶A and 5mC regulators. Thus, we systematically performed a pan-cancer genomic analysis by depicting the molecular correlations between m⁶A and 5mC regulators across ~ 11,000 subjects representing 33 cancer types. For the first time, we identified cross-talk between m⁶A and 5mC methylation at the multiomic level. Then, we further established m⁶A/5mC epigenetic module eigengenes by combining hub m⁶A/5mC regulators and informed a comprehensive epigenetic state. The model reflected status of the tumor-immune-stromal microenvironment and was able to predict patient survival in the majority of cancer types. Our results lay a solid foundation for epigenetic regulation in human cancer and pave a new road for related therapeutic targets.

The potential drug for treatment in pancreatic adenocarcinoma: a bioinformatical study based on distinct drug databases

Background

The prediction of drug-target interaction from chemical and biological data can advance our search for potential drug, contributing to a therapeutic strategy for pancreatic adenocarcinoma (PAAD). We aim to identify hub genes of PAAD and search for potential drugs from distinct databases. The docking simulation is adopted to validate our findings from computable perspective.

Methods

Differently expressed genes (DEGs) of PAAD were performed based on TCGA. With two Cytoscape plugins of CentiScaPe and MCODE, hub genes were analyzed and visualized by STRING analysis of Protein–protein Interaction (PPI). The hub genes were further selected with significant prognostic values. In addition, we examined the correlation between hub genes and immune infiltration in PAAD. Subsequently, we searched for the hub gene-targeted drugs in Connectivity map (Cmap) and cBioportal, which provided a large body of candidate drugs. The hub gene, which was covered in the above two databases, was estimated in Traditional Chinese Medicine Systems Pharmacology (TCMSP) and Herbal Ingredients’ Targets (HIT) database, which collected natural herbs and related ingredients. After obtaining molecular structures, the potential ingredient from TCMSP was applied for a docking simulation. We finalized a network connectivity of ingredient and its targets.

Results

A total of 2616 DEGs of PAAD were identified, then we further determined and visualized 24 hub genes by a connectivity analysis of PPI. Based on prognostic value, we identified 5 hub genes including AURKA (p = 0.0059), CCNA2 (p = 0.0047), CXCL10 (p = 0.0044), ADAM10 (p = 0.00043), and BUB1 (p = 0.0033). We then estimated tumor immune correlation of these 5 hub genes, because the immune effector process was one major result of GO analysis. Subsequently, we continued to search for candidate drugs from Cmap and cBioportal database. BUB1, not covered in the above two databases, was estimated in TCMSP and HIT databases. Our results revealed that genistein was a potential drug of BUB1. Next, we generated two docking modes to validate drug-target interaction based on their 3D structures. We eventually constructed a network connectivity of BUB1 and its targets.

Conclusions

All 5 hub genes that predicted poor prognosis had their potential drugs, especially our findings showed that genistein was predicted to target BUB1 based on TCMSP and docking simulation. This study provided a reasonable approach to extensively retrieve and initially validate putative therapeutic agents for PAAD. In future, these drug-target results should be investigated with solid data from practical experiments.

Screening and identification of genes associated with cell proliferation in cholangiocarcinoma

Cholangiocarcinoma (CCA), an aggressive tumor with poor prognosis, is a malignant cancer with increasing incidence and mortality rates. It is important to survey crucial genes in CCA to find and design potential drug targets, especially for those genes associated with cell proliferation that is a key biological process in tumorgenesis. Herein, we surveyed genes associated with cell proliferation via a comprehensive pan-cancer analysis. Candidate genes were further analyzed using multiple approaches, including cross-analysis from diverse molecular levels, examination of potential function and interactions, and additional experimental validation. We primarily screened 15 potential genes based on 11 validated genes, and these 26 genes were further examined to delineate their biological functions and potential roles in cancer treatment. Several of them were involved synthetically lethal genetic interactions, especially for RECQL4, TOP2A, MKI67 and ASPM, indicating their potential roles in drug design and cancer treatment. Further experimental validation indicated that some genes were significantly upregulated in several cancer cell lines, implying their important roles in tumorigenesis. Our study identifies some genes associated with cell proliferation, which may be potential future targets in molecular targeted therapy.

Comprehensive Review of Web Servers and Bioinformatics Tools for Cancer Prognosis Analysis

Prognostic biomarkers are of great significance to predict the outcome of patients with cancer, to guide the clinical treatments, to elucidate tumorigenesis mechanisms, and offer the opportunity of identifying therapeutic targets. To screen and develop prognostic biomarkers, high throughput profiling methods including gene microarray and next-generation sequencing have been widely applied and shown great success. However, due to the lack of independent validation, only very few prognostic biomarkers have been applied for clinical practice. In order to cross-validate the reliability of potential prognostic biomarkers, some groups have collected the omics datasets (i.e., epigenetics/transcriptome/proteome) with relative follow-up data (such as OS/DSS/PFS) of clinical samples from different cohorts, and developed the easy-to-use online bioinformatics tools and web servers to assist the biomarker screening and validation. These tools and web servers provide great convenience for the development of prognostic biomarkers, for the study of molecular mechanisms of tumorigenesis and progression, and even for the discovery of important therapeutic targets. Aim to help researchers to get a quick learning and understand the function of these tools, the current review delves into the introduction of the usage, characteristics and algorithms of tools, and web servers, such as LOGpc, KM plotter, GEPIA, TCPA, OncoLnc, PrognoScan, MethSurv, SurvExpress, UALCAN, etc., and further help researchers to select more suitable tools for their own research. In addition, all the tools introduced in this review can be reached at http://bioinfo.henu.edu.cn/WebServiceList.html.

A 6-Membrane Protein Gene score for prognostic prediction of cytogenetically normal acute myeloid leukemia in multiple cohorts

Background: Cytogenetically normal acute myeloid leukemia (CN-AML) is a large proportion of AMLs with diverse prognostic outcomes. Identifying membrane protein genes as prognostic factors to stratify CN-AML patients will be critical to improve their outcomes.

Purpose: This study aims to identify prognostic factors to stratify CN-AML patients to choose better treatments and improve their outcomes.

Methods: CN-AML data were from TCGA cohort (n = 79) and four GEO datasets. We identified independent prognostic genes by Cox regression and Kaplan-Meier methods, and constructed linear regression model using LASSO algorithm. The prediction error curve was calculated using R package “pec”.

Results: Based on independent prognostic membrane genes, we constructed a regression model for CN-AML prognosis prediction: score = (0.0492 * CD52) - (0.0018 * CD96) + (0.0131 * EMP1) + (0.2058 * TSPAN2) + (0.0234 * STAB1) - (0.3658 * MBTPS1), which was named as MPG6 (6-Membrane Protein Gene) score. Tested in multiple CN-AML datasets, consistent results showed that CN-AML patients with high MPG6 score had poor survival, higher WBC count and shorter EFS. Comparing with other reported scoring models, the benchmark result of MPG6 achieved better association with survival in multiple cohorts. Moreover, by combining with other clinical indicators in CN-AML, MPG6 could improve the performance of survival prediction and serve as a robust prognostic factor.

Conclusions: We identified the MPG6 score as a stable indicator with great potential for clinical application in risk stratification and outcome prediction in CN-AML.

FFLtool: a web server for transcription factor and miRNA feed forward loop analysis in human

Summary

Transcription factors (TFs) and microRNAs (miRNAs) are two kinds of important regulators for transcriptional and post-transcriptional regulations. Understanding cross-talks between the two regulators and their targets is critical to reveal complex molecular regulatory mechanisms. Here, we developed FFLtool, a web server for detecting potential feed forward loop (FFL) of TF-miRNA-target regulation in human. In FFLtool, we integrated comprehensive regulations of TF-target and miRNA-target, and developed two functional modules: (i) The ‘FFL Analysis’ module can detect potential FFLs and internal regulatory networks in a user-defined gene set. FFLtool also provides three levels of evidence to illustrate the reliability for each FFL and enrichment functions for co-target genes of the same TF and miRNA; (ii) The ‘Browse FFLs’ module displays FFLs comprised of differentially or specifically expressed TFs and miRNAs and their target genes in cancers. FFLtool is a valuable resource for investigating gene expression regulation and mechanism study in biological processes and diseases.

Availability and implementation

FFLtool is available on http://bioinfo.life.hust.edu.cn/FFLtool/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Landscape of cancer diagnostic biomarkers from specifically expressed genes

Although there has been great progress in cancer treatment, cancer remains a serious health threat to humans because of the lack of biomarkers for diagnosis, especially for early-stage diagnosis. In this study, we comprehensively surveyed the specifically expressed genes (SEGs) using the SEGtool based on the big data of gene expression from the The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) projects. In 15 solid tumors, we identified 233 cancer-specific SEGs (cSEGs), which were specifically expressed in only one cancer and showed great potential to be diagnostic biomarkers. Among them, three cSEGs (OGDH, MUDENG and ACO2) had a sample frequency >80% in kidney cancer, suggesting their high sensitivity. Furthermore, we identified 254 cSEGs as early-stage diagnostic biomarkers across 17 cancers. A two-gene combination strategy was applied to improve the sensitivity of diagnostic biomarkers, and hundreds of two-gene combinations were identified with high frequency. We also observed that 13 SEGs were targets of various drugs and nearly half of these drugs may be repurposed to treat cancers with SEGs as their targets. Several SEGs were regulated by specific transcription factors in the corresponding cancer, and 39 cSEGs were prognosis-related genes in 7 cancers. This work provides a survey of cancer biomarkers for diagnosis and early diagnosis and new insights to drug repurposing. These biomarkers may have great potential in cancer research and application.

Novel Biomarkers Associated With Progression and Prognosis of Bladder Cancer Identified by Co-expression Analysis

Our study's goal was to screen novel biomarkers that could accurately predict the progression and prognosis of bladder cancer (BC). Firstly, we used the Gene Expression Omnibus (GEO) dataset GSE37815 to screen differentially expressed genes (DEGs). Secondly, we used the DEGs to construct a co-expression network by weighted gene co-expression network analysis (WGCNA) in GSE71576. We then screened the brown module, which was significantly correlated with the histologic grade (r = 0.85, p = 1e-12) of BC. We conducted functional annotation on all genes of the brown module and found that the genes of the brown module were mainly significantly enriched in "cell cycle" correlation pathways. Next, we screened out two real hub genes (ANLN, HMMR) by combining WGCNA, protein-protein interaction (PPI) network and survival analysis. Finally, we combined the GEO datasets (GSE13507, GSE37815, GSE31684, GSE71576). Oncomine, Human Protein Atlas (HPA), and The Cancer Genome Atlas (TCGA) dataset to confirm the predict value of the real hub genes for BC progression and prognosis. A gene-set enrichment analysis (GSEA) revealed that the real hub genes were mainly enriched in "bladder cancer" and "cell cycle" pathways. A survival analysis showed that they were of great significance in predicting the prognosis of BC. In summary, our study screened and confirmed that two biomarkers could accurately predict the progression and prognosis of BC, which is of great significance for both stratification therapy and the mechanism study of BC.

GEDS: A Gene Expression Display Server for mRNAs, miRNAs and Proteins

High-throughput technologies generate a tremendous amount of expression data on mRNA, miRNA and protein levels. Mining and visualizing the large amount of expression data requires sophisticated computational skills. An easy to use and user-friendly web-server for the visualization of gene expression profiles could greatly facilitate data exploration and hypothesis generation for biologists. Here, we curated and normalized the gene expression data on mRNA, miRNA and protein levels in 23,315, 9009 and 9244 samples, respectively, from 40 tissues (The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GETx)) and 1594 cell lines (Cancer Cell Line Encyclopedia (CCLE) and MD Anderson Cell Lines Project (MCLP)). Then, we constructed the Gene Expression Display Server (GEDS), a web-based tool for quantification, comparison and visualization of gene expression data. GEDS integrates multiscale expression data and provides multiple types of figures and tables to satisfy several kinds of user requirements. The comprehensive expression profiles plotted in the one-stop GEDS platform greatly facilitate experimental biologists utilizing big data for better experimental design and analysis. GEDS is freely available online.

Transcriptome and Regulatory Network Analyses of CD19-CAR-T Immunotherapy for B-ALL

Chimeric antigen receptor (CAR) T cell therapy has exhibited dramatic anti-tumor efficacy in clinical trials. In this study, we reported the transcriptome profiles of bone marrow cells in four B cell acute lymphoblastic leukemia (B-ALL) patients before and after CD19-specific CAR-T therapy. CD19-CAR-T therapy remarkably reduced the number of leukemia cells, and three patients achieved bone marrow remission (minimal residual disease negative). The efficacy of CD19-CAR-T therapy on B-ALL was positively correlated with the abundance of CAR and immune cell subpopulations, e.g., CD8⁺ T cells and natural killer (NK) cells, in the bone marrow. Additionally, CD19-CAR-T therapy mainly influenced the expression of genes linked to cell cycle and immune response pathways, including the NK cell mediated cytotoxicity and NOD-like receptor signaling pathways. The regulatory network analyses revealed that microRNAs (e.g., miR-148a-3p and miR-375), acting as oncogenes or tumor suppressors, could regulate the crosstalk between the genes encoding transcription factors (TFs; e.g., JUN and FOS) and histones (e.g., HIST1H4A and HIST2H4A) involved in CD19-CAR-T therapy. Furthermore, many long non-coding RNAs showed a high degree of co-expression with TFs or histones (e.g., FOS and HIST1H4B) and were associated with immune processes. These transcriptome analyses provided important clues for further understanding the gene expression and related mechanisms underlying the efficacy of CAR-T immunotherapy.