Integrated Analysis of DNA Repair Genes Identifies SLC6A1 as a New Marker for the Clinical Outcome of Patients with Colorectal Cancer.

Colorectal cancer (CRC) remains an important malignancy worldwide with poor prognosis. It has been known that DNA repair genes are involved in the development and progression of various tumors. Therefore, the purpose of this study was to explore DNA repair gene-based prognostic biomarkers for CRC. In this study, the expressing pattern and prognostic values of DNA repair genes in CRC patients were analyzed using TCGA database. GO and KEGG enrichment analyses were conducted to clarify the functional roles of dysregulated genes. We observed 358 differentially expressed DNA repair genes in CRC specimens, including 84 downregulated genes and 275 upregulated genes. 36 survival-related DNA repair genes were correlated with CRC patients' five-year survival, including 6 low-risk genes and 30 high-risk genes. Among the 10 overlapping genes, we focused on SLC6A1 which was highly expressed in CRC, and multivariate analysis confirmed that SLC6A1 expression as well as age and clinical stage could be regarded as an independent predicting factor for CRC prognosis. KEGG assays revealed that SLC6A1 may influence the clinical progression via regulating TGF-beta and PI3K-Akt signaling pathways. In addition, we observed that SLC6A1 was negatively regulated by SLC6A1 methylation, leading to its low expression in CRC specimens. Overall, SLC6A1 is upexpressed in CRC and can be used as a marker of poor prognosis in CRC patients.

Bioinformatics Analysis of mRNAs and miRNAs for Identifying Potential Biomarkers in Lung Adenosquamous Carcinoma.

Background: Lung adenosquamous carcinoma (LASC) is a special type of lung cancer. LASC is a malignant tumor with strong aggressiveness and a poor prognosis. Previous studies have revealed that microRNAs (miRNAs) are widely involved in the development of tumors by targeting mRNA. This study is aimed at identifying the key mRNAs and miRNAs of LASC and constructing miRNA-mRNA networks for deeply comprehending the latent molecular mechanisms. Methods: mRNA dataset (GSE51852) and miRNA dataset (GSE51853) were extracted and downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were picked out by the GEO2R web tool. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted in the DAVID database. The protein-protein interaction (PPI) network was performed and analyzed by using the STRING database and Cytoscape software, respectively. TransmiR v2.0 was applied to predict potential transcription factors of miRNAs. The target genes of DEMs were predicted in the miRWalk database. Results: In comparison to normal tissues, a total of 1458 DEGs (511 upregulated and 947 downregulated) and 13 DEMs (5 upregulated and 8 downregulated) were screened out in LASC tissues. The PPI network of the DEGs displayed five key modules and seventeen hub genes. Six target genes of the DEMs were predicted, and five essential miRNA-mRNA regulatory pairs were established. Ensuingly, CENPF, one of the target genes, was also the hub genes of GSE51852, which was obtained from MCODE and cytoHubba and regulated by hsa-miR-205. Conclusions: We constructed the miRNA-mRNA regulatory pairs, which are helpful to study the potential regulatory mechanisms and find out promising diagnosis biomarkers and therapeutic targets for LASC.

A Pyroptosis-Related Gene Signature to Predict Patients' Prognosis and Immune Landscape in Liver Hepatocellular Carcinoma.

BACKGROUND: Liver hepatocellular carcinoma (LIHC) is a malignance with high incidence and recurrence. Pyroptosis is a programed cell death pattern which both activates the effective immune response and causes cell damage. However, their potential applications of pyroptosis-related genes (PRGs) in the prognostic evaluation and immunotherapy of LIHC are still rarely discussed. METHODS: Comprehensive bioinformatics analyses based on TCGA-LIHC dataset were performed in the current study. RESULTS: A total of 33 PRGs were selected to perform the current study. Of these 33 PRGs, 26 PRGs with upregulation or downregulation in LIHC tissues were identified. We also summarized the related genetic mutation variation landscape. GO and KEGG pathway analysis demonstrated that these 26 PRGs were primarily associated with pyroptosis, positive regulation of interleukin-1 beta production, and NOD-like receptor signaling pathway. An unfavorable OS appeared in LIHC patients with high CASP3, CASP4, CASP6, CASP8, GPX4, GSDMA, GSDME, NLRP3, NLRP7, NOD1, NOD2, PLCG1, and SCAF11 expression and low NLRP6 expression. A prognostic signature constructed by the above 14 prognostic PRGs had moderate to high accuracy to predict LIHC patients' prognosis. And risk score was correlated with the expression of CASP6, CASP8, GPX4, GSDMA, GSDME, NLRP6, and NOD2. Of these 7 genes, CASP8 was identified as the core gene in PPI network. Moreover, lncRNA MIR17HG/hsa-miRNA-130b-3p/CASP8 regulatory axis in LIHC was also detected. CONCLUSIONS: The current study indicated the crucial role of PRGs in the prognostic evaluation of LIHC patients and their correlations with tumor microenvironment in LIHC.

Identification of Four Metabolic Subtypes of Glioma Based on Glycolysis-Cholesterol Synthesis Genes.

Based on alterations in gene expression associated with the production of glycolysis and cholesterol, this research classified glioma into prognostic metabolic subgroups. In this study, data from the CGGA325 and The Cancer Genome Atlas (TCGA) datasets were utilized to extract single nucleotide variants (SNVs), RNA-seq expression data, copy number variation data, short insertions and deletions (InDel) mutation data, and clinical follow-up information from glioma patients. Glioma metabolic subtypes were classified using the ConsensusClusterPlus algorithm. This study determined four metabolic subgroups (glycolytic, cholesterogenic, quiescent, and mixed). Cholesterogenic patients had a higher survival chance. Genome-wide investigation revealed that inappropriate amplification of MYC and TERT was associated with improper cholesterol anabolic metabolism. In glioma metabolic subtypes, the mRNA levels of mitochondrial pyruvate carriers 1 and 2 (MPC1/2) presented deletion and amplification, respectively. Differentially upregulated genes in the glycolysis group were related to pathways, including IL-17, HIF-1, and TNF signaling pathways and carbon metabolism. Downregulated genes in the glycolysis group were enriched in terpenoid backbone biosynthesis, nitrogen metabolism, butanoate metabolism, and fatty acid metabolism pathway. Cox analysis of univariate and multivariate survival showed that risks of glycolysis subtypes were significantly higher than other subtypes. Those results were validated in the CGGA325 dataset. The current findings greatly contribute to a comprehensive understanding of glioma and personalized treatment.

Integrated Analysis of Multi-Omics Data to Identify Prognostic Genes for Pancreatic Cancer.

Pancreatic cancer (PC) is a common cause of cancer-related deaths. Current research shows that prognostic biomarkers play a key role in the treatment of PC. This study aimed to identify prognostic genes through bioinformatics research. We combined data from 175 cases of PC from the cancer genome atlas (TCGA) database with gene mutation expression, level distribution of methylation, mRNA expression, and through weighted correlation network analysis to nine hub genes. Subsequently, these genes were verified on TCGA and Gene Expression Profiling Interactive Analysis (GEPIA) platforms. Reverse transcription quantitative PCR (RT-qPCR) was performed to investigate the expression levels of 9 genes in PC cells and cancerous and 30 PC cases and corresponding adjacent tissues. CIBERSORT database analysis was conducted for hub genes. Our findings demonstrated that the 9 genes (MST1R, TMPRSS4, PTK6, KLF5, CGN, ABHD17C, MUC1, CAPN8, and B3GNT3) were prognostic biomarkers of PC identified from the top 10 genes of the 2 coexpression modules. The nine genes were then used to divide early PC cases into two subgroups with significant differences in prognosis and differences in function (digestion, extracellular cell adhesion). Further analysis revealed that the nine genes were highly expressed in PC tissues. In addition, MST1R, PTK6, ABHD17C, and CGN mRNA were expressed high in PC cells and clinical tissues. CIBERSORT analysis indicated that the expression of these genes was closely correlated with naive B cells, CD8+ T cells, and M0 macrophages. This suggests that these genes could play a carcinogenic role in the preservation of immune-dominant status for the tumor microenvironment. The nine key genes identified in this study could enhance our understanding of the molecular mechanisms associated with PC.

Construction of a Prognosis-Related Gene Signature by Weighted Gene Coexpression Network Analysis in Ewing Sarcoma.

BACKGROUND: Ewing sarcoma (ES) is the second most common pediatric bone tumor with a high rate of metastasis, high recurrence, and low survival rate. Therefore, the identification of new biomarkers which can improve the prognosis of ES patients is urgently needed. METHODS: Here, GSE17679 dataset was downloaded from GEO databases. WGCNA method was used to identify one module associating with OVS (overall vital survival) and event. cytoHubba was used to screen out 50 hub genes from the module genes. Then, GSE17679 dataset was randomly divided into train cohort and test cohort. Next, univariate Cox analysis, LASSO regression analysis, and multivariate Cox analysis were conducted on 50 hub genes combined with train cohort data to select pivotal genes. Finally, an optimal 7-gene-based risk assessment model was established, which was verified by test cohort, entire GSE17679, and two independent datasets (GSE63157 and TCGA-SARC). RESULTS: The results of the functional enrichment analysis revealed that the OVS and event-associated module were mainly enriched in the protein transcription, cell proliferation, and cell-cycle control. And the train cohort was divided into high-risk and low-risk subgroups based on the median risk score; the results showed that the survival of the low-risk subgroup was significantly longer than high-risk. ROC analysis revealed that AUC values of 1, 3, and 5-year survival were 0.85, 0.94, and 0.88, and Kaplan-Meier analysis also revealed that P value < 0.0001, indicating that this model was accurate, which was also verified in the test, entire cohort, and two independent datasets (GSE63157 and TCGA-SARC). Then, we performed a comprehensive analysis (differential expression analysis, correlation analysis and survival analysis) of seven pivotal genes, and found that four genes (NCAPG, KIF4A, NUF2 and CDC20) plays a more crucial role in the prognosis of ES. CONCLUSION: Taken together, this study established an optimal 7-gene-based risk assessment model and identified 4 potential therapeutic targets, to improve the prognosis of ES patients.

Circadian Clock Genes Are Correlated with Prognosis and Immune Cell Infiltration in Colon Adenocarcinoma.

BACKGROUND: Colon adenocarcinoma (COAD) is a malignancy with a high incidence and is associated with poor quality of life. Dysfunction of circadian clock genes and disruption of normal rhythms are associated with the occurrence and progression of many cancer types. However, studies that systematically describe the prognostic value and immune-related functions of circadian clock genes in COAD are lacking. METHODS: Genomic data obtained from The Cancer Genome Atlas (TCGA) database was analyzed for expression level, mutation status, potential biological functions, and prognostic performance of core circadian clock genes in COAD. Their correlations with immune infiltration and TMB/MSI score were analyzed by Spearman's correlation analysis. Pearson's correlation analysis was performed to analyze their associations with drug sensitivity. Lasso Cox regression analysis was performed to construct a prognosis signature. Moreover, an mRNA-miRNA-lncRNA regulatory axis was also detected by ceRNA network. RESULTS: In COAD tissues, the mRNA levels of CLOCK, CRY1, and NR1D1 were increased, while the mRNA levels of ARNTL, CRY2, PER1, PER3, and RORA were decreased. We also summarized the relative genetic mutation variation landscape. GO and KEGG pathway analyses demonstrated that these circadian clock genes were primarily correlated with the regulation of circadian rhythms and glucocorticoid receptor signaling pathways. COAD patients with high CRY2, NR1D1, and PER2 expression had worse prognosis. A prognostic model constructed based on the 9 core circadian clock genes predicted the COAD patients' overall survival with medium to high accuracy. A significant association between prognostic circadian clock genes and immune cell infiltration was found. Moreover, the lncRNA KCNQ1OT1/hsa-miRNA-32-5p/PER2/CRY2 regulatory axis in COAD was also detected through a mRNA-miRNA-lncRNA network. CONCLUSION: Our results identified CRY2, NR1D1, and PER2 as potential prognostic biomarkers for COAD patients and correlated their expression with immune cell infiltration. The lncRNA KCNQ1OT1/hsa-miRNA-32-5p/PER2/CRY2 regulatory axis was detected in COAD and might play a vital role in the occurrence and progression of COAD.

Uncovering potential genes in colorectal cancer based on integrated and DNA methylation analysis in the gene expression omnibus database.

BACKGROUND: Colorectal cancer (CRC) is major cancer-related death. The aim of this study was to identify differentially expressed and differentially methylated genes, contributing to explore the molecular mechanism of CRC. METHODS: Firstly, the data of gene transcriptome and genome-wide DNA methylation expression were downloaded from the Gene Expression Omnibus database. Secondly, functional analysis of differentially expressed and differentially methylated genes was performed, followed by protein-protein interaction (PPI) analysis. Thirdly, the Cancer Genome Atlas (TCGA) dataset and in vitro experiment was used to validate the expression of selected differentially expressed and differentially methylated genes. Finally, diagnosis and prognosis analysis of selected differentially expressed and differentially methylated genes was performed. RESULTS: Up to 1958 differentially expressed (1025 up-regulated and 993 down-regulated) genes and 858 differentially methylated (800 hypermethylated and 58 hypomethylated) genes were identified. Interestingly, some genes, such as GFRA2 and MDFI, were differentially expressed-methylated genes. Purine metabolism (involved IMPDH1), cell adhesion molecules and PI3K-Akt signaling pathway were significantly enriched signaling pathways. GFRA2, FOXQ1, CDH3, CLDN1, SCGN, BEST4, CXCL12, CA7, SHMT2, TRIP13, MDFI and IMPDH1 had a diagnostic value for CRC. In addition, BEST4, SHMT2 and TRIP13 were significantly associated with patients' survival. CONCLUSIONS: The identified altered genes may be involved in tumorigenesis of CRC. In addition, BEST4, SHMT2 and TRIP13 may be considered as diagnosis and prognostic biomarkers for CRC patients.

flopp: Extremely Fast Long-Read Polyploid Haplotype Phasing by Uniform Tree Partitioning.

Resolving haplotypes in polyploid genomes using phase information from sequencing reads is an important and challenging problem. We introduce two new mathematical formulations of polyploid haplotype phasing: (1) the min-sum max tree partition problem, which is a more flexible graphical metric compared with the standard minimum error correction (MEC) model in the polyploid setting, and (2) the uniform probabilistic error minimization model, which is a probabilistic analogue of the MEC model. We incorporate both formulations into a long-read based polyploid haplotype phasing method called flopp. We show that flopp compares favorably with state-of-the-art algorithms-up to 30 times faster with 2 times fewer switch errors on 6 × ploidy simulated data. Further, we show using real nanopore data that flopp can quickly reveal reasonable haplotype structures from the autotetraploid Solanum tuberosum (potato).

Identification of Key Genes and Pathways in Osteosarcoma by Bioinformatics Analysis.

PURPOSE: Osteosarcoma (OS) is the most primary bone malignant tumor in adolescents. Although the treatment of OS has made great progress, patients' prognosis remains poor due to tumor invasion and metastasis. MATERIALS AND METHODS: We downloaded the expression profile GSE12865 from the Gene Expression Omnibus database. We screened differential expressed genes (DEGs) by making use of the R limma software package. Based on Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, we performed the function and pathway enrichment analyses. Then, we constructed a Protein-Protein Interaction network and screened hub genes through the Search Tool for the Retrieval of Interacting Genes. RESULT: By analyzing the gene expression profile GSE12865, we obtained 703 OS-related DEGs, which contained 166 genes upregulated and 537 genes downregulated. The DEGs were primarily abundant in ribosome, cell adhesion molecules, ubiquitin-ubiquitin ligase activity, and p53 signaling pathway. The hub genes of OS were KDR, CDH5, CD34, CDC42, RBX1, POLR2C, PPP2CA, and RPS2 through PPI network analysis. Finally, GSEA analysis showed that cell adhesion molecules, chemokine signal pathway, transendothelial migration, and focal adhesion were associated with OS. CONCLUSION: In this study, through analyzing microarray technology and bioinformatics analysis, the hub genes and pathways about OS are identified, and the new molecular mechanism of OS is clarified.

ScalpelSig Designs Targeted Genomic Panels from Data to Detect Activity of Mutational Signatures.

Over the past decade, a promising line of cancer research has utilized machine learning to mine statistical patterns of mutations in cancer genomes for information. Recent work shows that these statistical patterns, commonly referred to as "mutational signatures," have diverse therapeutic potential as biomarkers for cancer therapies. However, translating this potential into reality is hindered by limited access to sequencing in the clinic. Almost all methods for mutational signature analysis (MSA) rely on whole genome or whole exome sequencing data, while sequencing in the clinic is typically limited to small gene panels. To improve clinical access to MSA, we considered the question of whether targeted panels could be designed for the purpose of mutational signature detection. Here we present ScalpelSig, to our knowledge the first algorithm that automatically designs genomic panels optimized for detection of a given mutational signature. The algorithm learns from data to identify genome regions that are particularly indicative of signature activity. Using a cohort of breast cancer genomes as training data, we show that ScalpelSig panels substantially improve accuracy of signature detection compared to baselines. We find that some ScalpelSig panels even approach the performance of whole exome sequencing, which observes over 10 × as much genomic material. We test our algorithm under a variety of conditions, showing that its performance generalizes to another dataset of breast cancers, to smaller panel sizes, and to lesser amounts of training data.

Deep Unfolding for Non-Negative Matrix Factorization with Application to Mutational Signature Analysis.

Non-negative matrix factorization (NMF) is a fundamental matrix decomposition technique that is used primarily for dimensionality reduction and is increasing in popularity in the biological domain. Although finding a unique NMF is generally not possible, there are various iterative algorithms for NMF optimization that converge to locally optimal solutions. Such techniques can also serve as a starting point for deep learning methods that unroll the algorithmic iterations into layers of a deep network. In this study, we develop unfolded deep networks for NMF and several regularized variants in both a supervised and an unsupervised setting. We apply our method to various mutation data sets to reconstruct their underlying mutational signatures and their exposures. We demonstrate the increased accuracy of our approach over standard formulations in analyzing simulated and real mutation data.

Circular RNA hsa_circ_0001658 Inhibits Intervertebral Disc Degeneration Development by Regulating hsa-miR-181c-5p/FAS.

METHODS: We obtained microarray data (GSE116726, GSE67566) from Gene Expression Omnibus database, and differential expression level of ncRNA in nucleus pulposus (NP) tissues of IDD patients was analyzed. The potential circRNA-miRNA-mRNA regulatory network was analyzed by starBase. The effect of the interaction between hsa_circ_0001658, hsa-miR-181c-5p, and FAS on the proliferation and apoptosis of human neural progenitor cells (hNPCs) was studied. RESULTS: hsa_circ_0001658 was significantly upregulated (logFC > 2.0 and adj.P.Val < 0.01) in the NP tissues of IDD patients, and hsa-miR-181c-5p expression was downregulated (logFC < -2.0 and adj.P.Val < 0.01). Silencing of hsa-miR-181c-5p or overexpression of hsa_circ_0001658 inhibited the proliferation of hNPCs and promoted their apoptosis. hsa_circ_0001658 acted as a sponge of hsa-miR-181c-5p. hsa-miR-181c-5p downregulated the expression of Fas cell surface death receptor (FAS), promoted the proliferation, and inhibited the apoptosis of hNPCs. hsa_circ_0001658 functioned in hNPCs through targeting hsa-miR-181c-5p/FAS. CONCLUSION: Circular RNA hsa_circ_0001658 inhibits IDD development by regulating hsa-miR-181c-5p/FAS. It is expected to be a potential target for the therapy of IDD.

Analysis of the Expression and Prognostic Value of MSH2 in Pan-Cancer Based on Bioinformatics.

BACKGROUND: MutS homolog 2 (MSH2), with the function of identifying mismatches and participating in DNA repair, is the "housekeeping gene" in the mismatch repair (MMR) system. MSH2 deficiency has been reported to enhance cancer susceptibility for the association of hereditary nonpolyposis colorectal cancer. However, the expression and prognostic significance of MSH2 have not been studied from the perspective of pan-cancer. METHODS: The GTEx database was used to analyze the expression of MSH2 in normal tissues. The TCGA database was used to analyze the differential expression of MSH2 in pan-cancers. The prognostic value of MSH2 in pan-cancer was assessed using Cox regression and Kaplan-Meier analysis. Spearman correlations were used to measure the relationship between the expression level of MSH2 in pan-cancer and the level of immune infiltration, tumor mutational burden (TMB), and microsatellite instability (MSI). RESULTS: MSH2 is highly expressed in most type of cancers and significantly correlated with prognosis. In COAD, KIRC, LIHC, and SKCM, the expression of MSH2 was significantly positively correlated with the abundance of B cells, CD4+ T cells, CD8+ T cells, dendritic cells, macrophages, and neutrophils. In THCA, MSH2 expression correlated with CD8+T Cell showed a significant negative correlation. MSH2 had significantly negative correlations with stromal score and immune score in a variety of cancers and significantly correlated with TMB and MSI of a variety of tumors. CONCLUSIONS: MSH2 may play an important role in the occurrence, development, and immune infiltration of cancer. MSH2 can emerge as a potential biomarker for cancer diagnosis and prognosis.

Machine learning random forest for predicting oncosomatic variant NGS analysis.

Since 2017, we have used IonTorrent NGS platform in our hospital to diagnose and treat cancer. Analyzing variants at each run requires considerable time, and we are still struggling with some variants that appear correct on the metrics at first, but are found to be negative upon further investigation. Can any machine learning algorithm (ML) help us classify NGS variants? This has led us to investigate which ML can fit our NGS data and to develop a tool that can be routinely implemented to help biologists. Currently, one of the greatest challenges in medicine is processing a significant quantity of data. This is particularly true in molecular biology with the advantage of next-generation sequencing (NGS) for profiling and identifying molecular tumors and their treatment. In addition to bioinformatics pipelines, artificial intelligence (AI) can be valuable in helping to analyze mutation variants. Generating sequencing data from patient DNA samples has become easy to perform in clinical trials. However, analyzing the massive quantities of genomic or transcriptomic data and extracting the key biomarkers associated with a clinical response to a specific therapy requires a formidable combination of scientific expertise, biomolecular skills and a panel of bioinformatic and biostatistic tools, in which artificial intelligence is now successful in developing future routine diagnostics. However, cancer genome complexity and technical artifacts make identifying real variants challenging. We present a machine learning method for classifying pathogenic single nucleotide variants (SNVs), single nucleotide polymorphisms (SNPs), multiple nucleotide variants (MNVs), insertions, and deletions detected by NGS from different types of tumor specimens, such as: colorectal, melanoma, lung and glioma cancer. We compared our NGS data to different machine learning algorithms using the k-fold cross-validation method and to neural networks (deep learning) to measure the performance of the different ML algorithms and determine which one is a valid model for confirming NGS variant calls in cancer diagnosis. We trained our machine learning with 70% of our data samples, extracted from our local database (our data structure had 7 parameters: chromosome, position, exon, variant allele frequency, minor allele frequency, coverage and protein description) and validated it with the 30% remaining data. The model offering the best accuracy was chosen and implemented in the NGS analysis routine. Artificial intelligence was developed with the R script language version 3.6.0. We trained our model on 70% of 102,011 variants. Our best error rate (0.22%) was found with random forest machine learning (ntree = 500 and mtry = 4), with an AUC of 0.99. Neural networks achieved some good scores. The final trained model with the neural network achieved an accuracy of 98% and an ROC-AUC of 0.99 with validation data. We tested our RF model to interpret more than 2000 variants from our NGS database: 20 variants were misclassified (error rate < 1%). The errors were nomenclature problems and false positives. After adding false positives to our training database and implementing our RF model routinely, our error rate was always < 0.5%. The RF model shows excellent results for oncosomatic NGS interpretation and can easily be implemented in other molecular biology laboratories. AI is becoming increasingly important in molecular biomedical analysis and can be very helpful in processing medical data. Neural networks show a good capacity in variant classification, and in the future, they may be useful in predicting more complex variants.

Identification of 5 Hub Genes Related to the Early Diagnosis, Tumour Stage, and Poor Outcomes of Hepatitis B Virus-Related Hepatocellular Carcinoma by Bioinformatics Analysis.

BACKGROUND: The majority of primary liver cancers in adults worldwide are hepatocellular carcinomas (HCCs, or hepatomas). Thus, a deep understanding of the underlying mechanisms for the pathogenesis and carcinogenesis of HCC at the molecular level could facilitate the development of novel early diagnostic and therapeutic treatments to improve the approaches and prognosis for HCC patients. Our study elucidates the underlying molecular mechanisms of HBV-HCC development and progression and identifies important genes related to the early diagnosis, tumour stage, and poor outcomes of HCC. METHODS: GSE55092 and GSE121248 gene expression profiling data were downloaded from the Gene Expression Omnibus (GEO) database. There were 119 HCC samples and 128 nontumour tissue samples. GEO2R was used to screen for differentially expressed genes (DEGs). Volcano plots and Venn diagrams were drawn by using the ggplot2 package in R. A heat map was generated by using Heatmapper. By using the clusterProfiler R package, KEGG and GO enrichment analyses of DEGs were conducted. Through PPI network construction using the STRING database, key hub genes were identified by cytoHubba. Finally, KM survival curves and ROC curves were generated to validate hub gene expression. RESULTS: By GO enrichment analysis, 694 DEGs were enriched in the following GO terms: organic acid catabolic process, carboxylic acid catabolic process, carboxylic acid biosynthetic process, collagen-containing extracellular matrix, blood microparticle, condensed chromosome kinetochore, arachidonic acid epoxygenase activity, arachidonic acid monooxygenase activity, and monooxygenase activity. In the KEGG pathway enrichment analysis, DEGs were enriched in arachidonic acid epoxygenase activity, arachidonic acid monooxygenase activity, and monooxygenase activity. By PPI network construction and analysis of hub genes, we selected the top 10 genes, including CDK1, CCNB2, CDC20, BUB1, BUB1B, CCNB1, NDC80, CENPF, MAD2L1, and NUF2. By using TCGA and THPA databases, we found five genes, CDK1, CDC20, CCNB1, CENPF, and MAD2L1, that were related to the early diagnosis, tumour stage, and poor outcomes of HBV-HCC. CONCLUSIONS: Five abnormally expressed hub genes of HBV-HCC are informative for early diagnosis, tumour stage determination, and poor outcome prediction.

Bioinformatics Analysis Reveals MCM3 as an Important Prognostic Marker in Cervical Cancer.

The minichromosome maintenance complex 3 (MCM3) is essential for the regulation of DNA replication and cell cycle progression. However, the expression and prognostic values of MCM3 in cervical cancer (CC) have not been well-studied. Herein, we investigated the expression patterns and survival data of MCM3 in cervical cancer patients from the ONCOMINE, GEPIA, Human Protein Atlas, UALCAN, Kaplan-Meier Plotter, and LinkedOmics databases. The expression level of MCM3 is negatively correlated with advanced tumor stage and metastatic status. Specifically, MCM3 is significantly differentially expressed between patients in stage 1 and stage 3 cervical cancer with p value 0.0138. Similarly, the p values between stage 1 and stage 4 cervical cancer, between stage 2 and stage 3, and between stage 2 and stage 4 are 0.00089, 0.0244, and 0.00197, respectively. Not only that, cervical cancer patients with high mRNA expression of MCM3 may indicate longer overall survival but indicate shorter relapse-free survival. PRIM2 and MCM6 are positively correlated genes of MCM3. Bioinformatics analysis revealed that MCM3 might be considered a biological indicator for prognostic evaluation of cervical cancer. However, it is currently limited to bioinformatics analysis, and more clinical tissue specimens and cell experiments are needed to further explore the role of MCM3 in the occurrence and progression of cervical cancer.

Gene Expression Analysis Reveals Key Genes and Signalings Associated with the Prognosis of Prostate Cancer.

It is urgent to identify novel biomarkers for prostate cancer (PCa) prognosis and to understand the mechanisms regulating the tumorigenesis for PCa treatment. In this study, GSE17951 and TCGA were used to identify the differentially expressed genes (DEGs). Our study demonstrated that 1533 genes with increased expression and 2301 genes with decreased expression in PCa. Bioinformatics analysis data indicated that these up-regulated genes had an association with the modulation of mitotic nuclear division, sister chromatid cohesion, cell division, and cell cycle. Additionally, our results revealed downregulated genes took part in modulating extracellular matrix organization, angiogenesis, signal transduction, and Ras signaling pathway. Hub upregulated and downregulated PPI networks were identified by protein-protein interaction (PPI) network analysis and MCODE analysis. Of note, 12 cell cycle regulators, comprising CCNB1, CCNB2, PLK1, TTK, AURKA, CDC20, BUB1, PTTG1, CDC45, CDC25C, CCNA2, and BUB1B, were demonstrated to function crucially in PCa development. By detecting their expression in PCa cell lines, we confirmed that these cell cycle regulator expressions were heightened in PCa cells. GEPIA databases analysis showed that higher expression of these cell cycle regulators was correlated to shorter disease-free survival (DFS) time in PCa samples. Our findings collectively suggested targeting cell cycle pathways may offer novel prognosis and treatment biomarkers for PCa.

[Interest of the bc-GenExMiner web tool in oncology]. / Intérêt de l'outil web bc-GenExMiner en oncologie.

We are taking advantage of the launch of the latest version (v4.6) of our web-based data mining tool "breast cancer gene-expression miner" (bc-GenExMiner) to take stock of its position within the oncology research landscape and to present an activity report ten years after its establishment (http://bcgenex.ico.unicancer.fr). bc-GenExMiner is an open-access, user-friendly tool for statistical mining on breast tumor transcriptomes, annotated with more than 20 clinicopathologic and molecular characteristics. The database comprises more than 16,000 patients from 64 cohorts - including TCGA, METABRIC and SCAN-B - for whom several thousands of genes have been quantified by microarrays or RNA-seq. Correlation, expression and prognostic analyses are available for targeted, exhaustive or customized explorations of queried genes. bc-GenExMiner facilitates the validation, investigation, and prioritization of discoveries and hypotheses on genes of interest. It allows users to analyse large databases, create data visualizations, and obtain robust statistical analysis, thereby accelerating biomarker discovery. Ten years after its launch, judging by the number of visits, analyses, and scientific citations of bc-GenExMiner, we conclude that this web resource serves its purpose in the international scientific community working in breast cancer research, with a never-ending rise in its use.

Bayesian Gene Selection Based on Pathway Information and Network-Constrained Regularization.

High-throughput data make it possible to study expression levels of thousands of genes simultaneously under a particular condition. However, only few of the genes are discriminatively expressed. How to identify these biomarkers precisely is significant for disease diagnosis, prognosis, and therapy. Many studies utilized pathway information to identify the biomarkers. However, most of these studies only incorporate the group information while the pathway structural information is ignored. In this paper, we proposed a Bayesian gene selection with a network-constrained regularization method, which can incorporate the pathway structural information as priors to perform gene selection. All the priors are conjugated; thus, the parameters can be estimated effectively through Gibbs sampling. We present the application of our method on 6 microarray datasets, comparing with Bayesian Lasso, Bayesian Elastic Net, and Bayesian Fused Lasso. The results show that our method performs better than other Bayesian methods and pathway structural information can improve the result.