Genome-wide analysis to identify a novel microRNA signature that predicts survival in patients with stomach adenocarcinoma

Prognostic value of nicotinamide adenine dinucleotide (NAD+) metabolic genes in patients with stomach adenocarcinoma based on bioinformatics analysis

Background

Because stomach adenocarcinoma (STAD) has a poor prognosis, it is necessary to explore new prognostic genes to stratify patients to guide existing individualized treatments.

Methods

Survival and clinical information, RNA-seq data and mutation data of STAD were acquired from The Cancer Genome Atlas (TCGA) database. Fifty-one nicotinamide adenine dinucleotide (NAD⁺) metabolism-related genes (NMRGs) were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases. Differentially expressed NMRGs (DE-NMRGs) between STAD and normal samples were screened, and consistent clustering analysis of STAD patients was performed based on the DE-NMRGs. Survival analysis, Gene Set Enrichment Analysis (GSEA), mutation frequency analysis, immune microenvironment analysis and drug prediction were performed among different clusters. Additionally, the differentially expressed genes (DEGs) among different clusters were selected, and the intersections of DEGs and DE-NMRGs were selected as the prognostic genes. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was performed on a human gastric mucosa epithelial cell line and cancer cell line to verify the expression of the prognostic genes.

Results

A total of 27 DE-NMRGs and two clusters were selected. There was a difference in survival between clusters 1 and 2. Furthermore, 18 DE-NMRGs were significantly different between clusters 1 and 2. The different Gene Ontology (GO) biological processes and KEGG pathways between clusters 1 and 2 were mainly enriched in cyclic nucleotide mediated signaling, synaptic signaling and hedgehog signaling pathway, etc. The somatic mutation frequencies were different between the two clusters, and TTN was the highest mutated gene in the patients of the clusters 1 and 2. Additionally, eight immune cells, immune score, stromal score, and estimate score were different between clusters 1 and 2. The patients in cluster 2 were sensitive to CTLA4 inhibitor treatment. Furthermore, the top five drugs (AP.24534, BX.795, Midostaurin, WO2009093927 and CCT007093) were significantly higher in cluster 1 than in cluster 2. Finally, three genes (AOX1, NNMT and PTGIS) were acquired as prognostic, and their expressions were consistent with the results of bioinformatics analysis.

Conclusions

Three prognostic genes related to NAD⁺ metabolism in STAD were screened out, which provides a theoretical basis and reference value for future treatment and prognosis of STAD.

An Unsupervised Deep Learning-Based Model Using Multiomics Data to Predict Prognosis of Patients with Stomach Adenocarcinoma

METHODS

Patients (363 in total) with stomach adenocarcinoma from The Cancer Genome Atlas (TCGA) cohort were included. An autoencoder was constructed to integrate the RNA sequencing, miRNA sequencing, and methylation data. The features of the bottleneck layer were used to perform the k-means clustering algorithm to obtain different subgroups for evaluating the prognosis-related risk of stomach adenocarcinoma. The model's robustness was verified using a 10-fold cross-validation (CV). Survival was analyzed by the Kaplan-Meier method. Univariate and multivariate Cox regression was used to estimate hazard risk. The model was validated in three independent cohorts with different endpoints.

RESULTS

The patients were divided into low-risk and high-risk groups according to the k-means clustering algorithm. The high-risk group had a significantly higher risk of poor survival (log-rank P value = 2.80e - 06; adjusted hazard ratio = 2.386, 95% confidence interval: 1.607~3.543), a concordance index (C-index) of 0.714, and a Brier score of 0.184. The model performed well both in the 10-fold CV procedure and three independent cohorts from the Gene Expression Omnibus (GEO) repository.

CONCLUSIONS

A robust and generalizable model based on the autoencoder was proposed to integrate multiomics data and predict the prognosis of patients with stomach adenocarcinoma. The model demonstrates better performance than two alternative approaches on prognosis prediction. The results might provide the grounds for further exploring the potential biomarkers to predict the prognosis of patients with stomach adenocarcinoma.

Role of Frizzled receptor expression on patients' survival with gastrointestinal cancers: A systematic review with meta-analysis

BACKGROUND

Frizzled receptors (FZD) play a pivotal role in the initiation and progression of a wide array of cancers. Dysregulated expression of FZD receptors is correlated with higher metastasis and invasive potential, as well as short survival in many malignancies. In this meta-analysis, we aimed to verify the prognostic value of FZD receptor expression on patients' survival with different types of gastrointestinal (GI) cancers, including gastric, colorectal, and esophageal cancers.

METHODS

A systematic search was performed using PubMed, Scopus, and Web of Science from 2000 to November 2020. Fourteen studies, including 2997 patients met our inclusion criteria, in which nine articles were considered FZD7 while the rest were about other FZD members. The fixed-effect model was used to estimate the pooled hazard ratio (HR) and the 5-year overall survival (OS) rate. We used the Newcastle-Ottawa scale of cohort articles to determine the quality of included studies.

RESULTS

The results showed that high expression of FZD receptors is associated with the poor survival in patients with GI cancers (HR= 1.83, 95% CI: 1.5-2.17). Moreover, multivariate analysis indicated that FZD receptors could be considered as an independent prognostic factor (HR = 1.76, 95% CI: 1.37-2.16).

CONCLUSION

According to our results, overexpression of FZD receptors predicts a poor prognosis in patients with GI cancers and could be used as a useful therapeutic target.

Identifying Lung Cancer Cell Markers with Machine Learning Methods and Single-Cell RNA-Seq Data

Non-small cell lung cancer is a major lethal subtype of epithelial lung cancer, with high morbidity and mortality. The single-cell sequencing technique plays a key role in exploring the pathogenesis of non-small cell lung cancer. We proposed a computational method for distinguishing cell subtypes from the different pathological regions of non-small cell lung cancer on the basis of transcriptomic profiles, including a group of qualitative classification criteria (biomarkers) and various rules. The random forest classifier reached a Matthew’s correlation coefficient (MCC) of 0.922 by using 720 features, and the decision tree reached an MCC of 0.786 by using 1880 features. The obtained biomarkers and rules were analyzed in the end of this study.

MicroRNA-653-5p Promotes Gastric Cancer Proliferation and Metastasis by Targeting the SOCS6-STAT3 Pathway

MicroRNAs (miRNAs) are emerging as significant regulators of the tumorigenesis of gastric cancer (GC), and may be effective biomarkers for diagnosis, prognosis, and therapeutic targeting for GC. In this study, miR-653-5p was found to be significantly upregulated in GC tissues, serum, and cell lines and was strongly associated with poor prognosis in patients with GC. Furthermore, miR-653-5p promoted GC cell proliferation and metastasis in vivo and in vitro. Suppressor of cytokine signaling 6 (SOCS6) was directly targeted by miR-653-5p, and SOCS6 attenuated miR-653-5p-mediated GC cell growth, migration, and invasion. In addition, SOCS6-mediated inactivation of the Janus kinase 2/signal transducer and activator of transcription 3 (JAK2/STAT3) signaling pathway was also reversed by the administration of miR-653-5p. The findings from this study support a novel regulatory axis between miR-653-5p, SOCS6, and JAK2/STAT3 that may be a target for diagnosis and therapeutic intervention for GC.

Identification of pivotal genes associated with the prognosis of gastric carcinoma through integrated analysis

PURPOSE

Detecting and diagnosing gastric cancer (GC) during its early period remains greatly difficult. Our analysis was performed to detect core genes correlated with GC and explore their prognostic values.

METHODS

Microarray datasets from the Gene Expression Omnibus (GEO) (GSE54129) and The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) datasets were applied for common differentially co-expressed genes using differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Functional enrichment analysis and protein-protein interaction (PPI) network analysis of differentially co-expressed genes were performed. We identified hub genes via the CytoHubba plugin. Prognostic values of hub genes were explored. Afterward, Gene Set Enrichment Analysis (GSEA) was used to analyze survival-related hub genes. Finally, the tumor-infiltrating immune cell (TIC) abundance profiles were estimated.

RESULTS

Sixty common differentially co-expressed genes were found. Functional enrichment analysis implied that cell-cell junction organization and cell adhesion molecules were primarily enriched. Hub genes were identified using the degree, edge percolated component (EPC), maximal clique centrality (MCC), and maximum neighborhood component (MNC) algorithms, and serpin family E member 1 (SERPINE1) was highly associated with the prognosis of GC patients. Moreover, GSEA demonstrated that extracellular matrix (ECM) receptor interactions and pathways in cancers were correlated with SERPINE1 expression. CIBERSORT analysis of the proportion of TICs suggested that CD8+ T cell and T-cell regulation were negatively associated with SERPINE1 expression, showing that SERPINE1 may inhibit the immune-dominant status of the tumor microenvironment (TME) in GC.

CONCLUSIONS

Our analysis shows that SERPINE1 is closely correlated with the tumorigenesis and progression of GC. Furthermore, SERPINE1 acts as a candidate therapeutic target and prognostic biomarker of GC.

Predicting Associations of miRNAs and Candidate Gastric Cancer Genes for Nanomedicine

Nanoscale miRNAs regulate the synthesis of most human proteins involved in differentiation, proliferation, cell cycle, apoptosis, and other processes associated with the growth and the development of an organism. miRNAs also play a number of important roles in the development of gastric cancer. In this work, we studied the quantitative characteristics of miRNA interactions with 69 candidate gastric cancer genes using bioinformatics approaches. To this end, the MirTarget program was used, which determines the characteristics of miRNA binding to mRNA in the 5′UTR, CDS, and 3′UTR. Associations of miRNAs with alternative target genes and associations of genes with alternative miRNAs were established. The cluster organization of miRNA binding sites (BSs) in mRNA was revealed, leading to the emergence of miRNA competition for binding to the mRNA of a target gene. Groups of target genes with clusters of overlapping BSs include miR-5095, miR-619-5p, miR-1273 family, miR-466, ID01030.3p-miR, ID00436.3p-miR, miR-574-5p, and ID00470.5p-miR. In the defined associations of target genes and miRNAs, miRNA BSs are organized into clusters of multiple BSs, which facilitate the design and the development of a system of chips that can be used to control the state of miRNA and target genes associations in gastric cancer.

Multivariate gene expression-based survival predictor model in esophageal adenocarcinoma

Background

Despite the recent development of molecular‐targeted treatment and immunotherapy, survival of patients with esophageal adenocarcinoma (EAC) with poor prognosis is still poor due to lack of an effective biomarker. In this study, we aimed to explore the ceRNA and construct a multivariate gene expression predictor model using data from The Cancer Genome Atlas (TCGA) to predict the prognosis of EAC patients.

Methods

We conducted differential expression analysis using mRNA, miRNA and lncRNA transciptome data from EAC and normal patients as well as corresponding clinical information from TCGA database, and gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of those unique differentially expressed mRNAs using the Integrate Discovery Database (DAVID) database. We then constructed the lncRNA‐miRNA‐mRNA competing endogenous RNA (ceRNA) network of EAC and used Cox proportional hazard analysis to generate a multivariate gene expression predictor model. We finally performed survival analysis to determine the effect of differentially expressed mRNA on patients' overall survival and discover the hub gene.

Results

We identified a total of 488 lncRNAs, 33 miRNAs, and 1207 mRNAs with differentially expressed profiles. Cox proportional hazard analysis and survival analysis using the ceRNA network revealed four genes (IL‐11, PDGFD, NPTX1, ITPR1) as potential biomarkers of EAC prognosis in our predictor model, and IL‐11 was identified as an independent prognostic factor.

Conclusions

In conclusion, we identified differences in the ceRNA regulatory networks and constructed a four–gene expression‐based survival predictor model, which could be referential for future clinical research.

Identification of potential prognostic small nucleolar RNA biomarkers for predicting overall survival in patients with sarcoma

Objective

The main purpose of the present study is to screen prognostic small nucleolar RNA (snoRNA) markers using the RNA‐sequencing (RNA‐seq) dataset of The Cancer Genome Atlas (TCGA) sarcoma cohort.

Methods

The sarcoma RNA‐seq dataset comes from the TCGA cohort. A total of 257 sarcoma patients were included into the prognostic analysis. Multiple bioinformatics analysis methods for functional annotation of snoRNAs and screening of targeted drugs, including biological network gene ontology tool, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and connectivity map (CMap) are used.

Results

We had identified 15 snoRNAs that were significantly related to the prognosis of sarcoma and constructed a prognostic signature based on four prognostic snoRNA (U3, SNORA73B, SNORD46, and SNORA26) expression values. Functional annotation of these four snoRNAs by their co‐expression genes suggests that some of them were closely related to cell cycle‐related biological processes and tumor‐related signaling pathways, such as Wnt, mitogen‐activated protein kinase, target of rapamycin, and nuclear factor‐kappa B signaling pathway. GSEA of the risk score suggests that high risk score phenotype was significantly enriched in cell cycle‐related biological processes, protein SUMOylation, DNA replication, p53 binding, regulation of DNA repair, and DNA methylation, as well as Myc, Wnt, RB1, E2F, and TEL pathways. Then we also used the CMap online tool to screen five targeted drugs (rilmenidine, pizotifen, amiprilose, quipazine, and cinchonidine) for this risk score model in sarcoma.

Conclusion

Our study have identified 15 snoRNAs that may be serve as novel prognostic biomarkers for sarcoma, and constructed a prognostic signature based on four prognostic snoRNA expression values.