Identifying Mitochondrial-Related Genes NDUFA10 and NDUFV2 as Prognostic Markers for Prostate Cancer through Biclustering.
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ABSTRACT:
SUBMITTER: Zhang H
PROVIDER: S-EPMC8168472 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundProstate cancer (PCa) is an immune-responsive disease. The current study sought to explore a robust immune-related prognostic gene signature for PCa.MethodsData were retrieved from the tumor Genome Atlas (TCGA) database and GSE46602 database for performing the least absolute shrinkage and selection operator (LASSO) cox regression model analysis. Immune related genes (IRGs) data were retrieved from ImmPort database.ResultsThe weighted gene co-expression network analysis (WGCNA) showed that nine functional modules are correlated with the biochemical recurrence of PCa, including 259 IRGs. Univariate regression analysis and survival analysis identified 35 IRGs correlated with the prognosis of PCa. LASSO Cox regression model analysis was used to construct a risk prognosis model comprising 18 IRGs. Multivariate regression analysis showed that risk score was an independent predictor of the prognosis of PCa. A nomogram comprising a combination of this model and other clinical features showed good prediction accuracy in predicting the prognosis of PCa. Further analysis showed that different risk groups harbored different gene mutations, differential transcriptome expression and different immune infiltration levels. Patients in the high-risk group exhibited more gene mutations compared with those in the low-risk group. Patients in the high-risk groups showed high-frequency mutations in TP53. Immune infiltration analysis showed that M2 macrophages were significantly enriched in the high-risk group implying that it affected prognosis of PCa patients. In addition, immunostimulatory genes were differentially expressed in the high-risk group compared with the low-risk group. BIRC5, as an immune-related gene in the prediction model, was up-regulated in 87.5% of prostate cancer tissues. Knockdown of BIRC5 can inhibit cell proliferation and migration.ConclusionIn summary, a risk prognosis model based on IGRs was developed. A nomogram comprising a combination of this model and other clinical features showed good accuracy in predicting the prognosis of PCa. This model provides a basis for personalized treatment of PCa and can help clinicians in making effective treatment decisions.
| S-EPMC8417385 | biostudies-literature
Project description:Oral squamous cell carcinoma (OSCC), the most prevalent form of oral cancer, poses significant challenges to the medical community due to its high recurrence rate and low survival rate. Mitochondrial Damage-Related Genes (MDGs) have been closely associated with the occurrence, metastasis, and progression of OSCC. Consequently, we constructed a prognostic model for OSCC based on MDGs and identified potential mitochondrial damage-related biomarkers. Gene expression profiles and relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) database. Differential analysis was conducted to identify MDGs associated with OSCC. COX analysis was employed to screen seven prognosis-related MDGs and build a prognostic prediction model for OSCC. Cases were categorized into low-risk or high-risk groups based on the optimal risk score threshold. Kaplan-Meier (KM) analysis revealed significant survival differences (P < 0.05). Additionally, the area under the ROC curve (AUC) for patient survival at 1 year, 3 years, and 5 years were 0.687, 0.704, and 0.70, respectively, indicating a high long-term predictive accuracy of the prognostic model. To enhance predictive accuracy, age, gender, risk score, and TN staging were incorporated into a nomogram and verified using calibration curves. Risk scoring based on MDGs was identified as a potential independent prognostic biomarker. Furthermore, BID and SLC25A20 were identified as two potential independent mitochondrial damage-related prognostic biomarkers, offering new therapeutic targets for OSCC.
| S-EPMC11321089 | biostudies-literature
Project description:Gastric adenocarcinoma (GAC) is the most common histological type of gastric cancer and imposes a considerable health burden globally. The purpose of this study was to identify significant genes and key pathways participated in the initiation and progression of GAC. Four datasets (GSE13911, GSE19826, GSE54129, and GSE79973) including 171 GAC and 77 normal tissues from Gene Expression Omnibus (GEO) database were collected and analyzed. Through integrated bioinformatics analysis, we obtained 69 commonly differentially expressed genes (DEGs) among the four datasets, including 20 upregulated and 49 downregulated genes. The prime module in protein-protein interaction network of DEGs, including ADAMTS2, COL10A1, COL1A1, COL1A2, COL8A1, BGN, and SPP1, was enriched in protein digestion and absorption, ECM-receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and amoebiasis. Furthermore, expression and survival analysis found that all seven hub genes were highly expressed in GAC tissues and 6 of them (except for SPP1) were able to predict poor prognosis of GAC. Finally, we verified the 6 high-expressed hub genes in GAC tissues via immunohistochemistry, Western blot, and RNA quantification analysis. Altogether, we identified six significantly upregulated DEGs as poor prognostic markers in GAC based on integrated bioinformatical methods, which could be potential molecular markers and therapeutic targets for GAC patients.
| S-EPMC9206555 | biostudies-literature
Project description:BackgroundTranscriptome analysis aims at gaining insight into cellular processes through discovering gene expression patterns across various experimental conditions. Biclustering is a standard approach to discover genes subsets with similar expression across subgroups of samples to be identified. The result is a set of biclusters, each forming a specific submatrix of rows (e.g. genes) and columns (e.g. samples). Relevant biclusters can, however, be missed when, due to the presence of a few outliers, they lack the assumed homogeneity of expression values among a few gene/sample combinations. The Max-Sum SubMatrix problem addresses this issue by looking at highly expressed subsets of genes and of samples, without enforcing such homogeneity.ResultsWe present here the K-CPGC algorithm to identify K relevant submatrices. Our main contribution is to show that this approach outperforms biclustering algorithms to identify several gene subsets representative of specific subgroups of samples. Experiments are conducted on 35 gene expression datasets from human tissues and yeast samples. We report comparative results with those obtained by several biclustering algorithms, including CCA, xMOTIFs, ISA, QUBIC, Plaid and Spectral. Gene enrichment analysis demonstrates the benefits of the proposed approach to identify more statistically significant gene subsets. The most significant Gene Ontology terms identified with K-CPGC are shown consistent with the controlled conditions of each dataset. This analysis supports the biological relevance of the identified gene subsets. An additional contribution is the statistical validation protocol proposed here to assess the relative performances of biclustering algorithms and of the proposed method. It relies on a Friedman test and the Hochberg's sequential procedure to report critical differences of ranks among all algorithms.ConclusionsWe propose here the K-CPGC method, a computationally efficient algorithm to identify K max-sum submatrices in a large gene expression matrix. Comparisons show that it identifies more significantly enriched subsets of genes and specific subgroups of samples which are easily interpretable by biologists. Experiments also show its ability to identify more reliable GO terms. These results illustrate the benefits of the proposed approach in terms of interpretability and of biological enrichment quality. Open implementation of this algorithm is available as an R package.
| S-EPMC6888937 | biostudies-literature
Project description:BACKGROUND: Currently, the TNM classification of malignant tumours based on clinicopathological staging remains the standard for colorectal cancer (CRC) prognostication. Recently, we identified the mitochondrial oxidative phosphorylation chain as a consistently overrepresented category in the published gene expression profiling (GEP) studies on CRC prognosis. METHODS: We evaluated associations of putative regulatory single nucleotide polymorphisms (SNPs) in genes from the oxidative phosphorylation chain with survival and disease prognosis in 613 CRC patients from Northern Germany (PopGen cohort). RESULTS: Two SNPs in the 3' untranslated region of UQCRB (complex III), rs7836698 and rs10504961, were associated with overall survival (HR = 0.52, 95% CI 0.32-0.85 and HR = 0.64, 95% CI 0.42-0.99, for TT carriers). These associations were restricted to the group of patients with cancer located in the colon (HR = 0.42, 95% CI 0.22-0.82 and HR = 0.46, 95% CI 0.25-0.83). Multivariate analysis indicated that both markers might act as independent prognostic markers. Additionally, the TT carriers were ~2 times more likely to develop tumours in the colon than in the rectum. Two SNPs in COX6B1 (complex IV) were associated with lymph node metastasis in a dominant model (rs6510502, OR = 1.75, 95% CI 1.20-2.57; rs10420252, OR = 1.68, 95% CI 1.11-2.53); rs6510502 was associated also with distant metastasis (OR = 1.67, 95% CI 1.09-2.56 in a dominant model). CONCLUSIONS: This is the first report suggesting that markers in genes from the mitochondrial oxidative chain might be prognostic factors for CRC. Additional studies replicating the presented findings are needed.
| S-EPMC3420261 | biostudies-literature
Project description:Lung adenocarcinoma (LUAD) is a prevalent cancer killer. Investigation on potential prognostic markers of LUAD is crucial for a patient's postoperative planning. LUAD-associated datasets were acquired from Gene Expression Omnibus (GEO) as well as The Cancer Genome Atlas (TCGA). LUAD metabolism-associated differentially expressed genes were obtained, combining tumor metabolism-associated genes. COX regression analyses were conducted to build a five-gene prognostic model. Samples were divided into high- and low-risk groups by the established model. Survival analysis displayed favorable prognosis in the low-risk group in the training set. Favorable predictive performance of the model was discovered as hinted by receiver's operative curve (ROC). Survival analysis and ROC analysis in the validation set held an agreement. Gene Set Enrichment Analysis (GSEA), tumor mutation bearing (TMB), and immune infiltration differential analysis were performed. The two groups displayed differences in glycolysis gluconeogenesis, P53 signaling pathway, etc. The high-risk group showed higher TP53 mutation frequency as well as TMB. The low-risk group displayed higher immune activity along with immune score. Altogether, this study casts light on further development of novel prognostic markers for LUAD.
| S-EPMC8560970 | biostudies-literature
Project description:MotivationRecent genomic studies have confirmed that cancer is of utmost phenotypical complexity, varying greatly in terms of subtypes and evolutionary stages. When classifying cancer tissue samples, subnetwork marker approaches have proven to be superior over single gene marker approaches, most importantly in cross-platform evaluation schemes. However, prior subnetwork-based approaches do not explicitly address the great phenotypical complexity of cancer.ResultsWe explicitly address this and employ density-constrained biclustering to compute subnetwork markers, which reflect pathways being dysregulated in many, but not necessarily all samples under consideration. In breast cancer we achieve substantial improvements over all cross-platform applicable approaches when predicting TP53 mutation status in a well-established non-cross-platform setting. In colon cancer, we raise prediction accuracy in the most difficult instances from 87% to 93% for cancer versus non-cancer and from 83% to (astonishing) 92%, for with versus without liver metastasis, in well-established cross-platform evaluation schemes.AvailabilitySoftware is available on request.
| S-EPMC2935415 | biostudies-literature
Project description:BackgroundMicroRNA (miRNA) sponges with multiple tandem miRNA binding sequences can sequester miRNAs from their endogenous target mRNAs. Therefore, miRNA sponge acting as a decoy is extremely important for long-term loss-of-function studies both in vivo and in silico. Recently, a growing number of in silico methods have been used as an effective technique to generate hypotheses for in vivo methods for studying the biological functions and regulatory mechanisms of miRNA sponges. However, most existing in silico methods only focus on studying miRNA sponge interactions or networks in cancer, the module-level properties of miRNA sponges in cancer is still largely unknown.ResultsWe propose a novel in silico method, called miRSM (miRNA Sponge Module) to infer miRNA sponge modules in breast cancer. We apply miRSM to the breast invasive carcinoma (BRCA) dataset provided by The Cancer Genome Altas (TCGA), and make functional validation of the computational results. We discover that most miRNA sponge interactions are module-conserved across two modules, and a minority of miRNA sponge interactions are module-specific, existing only in a single module. Through functional annotation and differential expression analysis, we also find that the modules discovered using miRSM are functional miRNA sponge modules associated with BRCA. Moreover, the module-specific miRNA sponge interactions among miRNA sponge modules may be involved in the progression and development of BRCA. Our experimental results show that miRSM is comparable to the benchmark methods in recovering experimentally confirmed miRNA sponge interactions, and miRSM outperforms the benchmark methods in identifying interactions that are related to breast cancer.ConclusionsAltogether, the functional validation results demonstrate that miRSM is a promising method to identify miRNA sponge modules and interactions, and may provide new insights for understanding the roles of miRNA sponges in cancer progression and development.
| S-EPMC5374547 | biostudies-literature
Project description:BackgroundAn increasing number of studies has indicated that the tumor microenvironment (TME), an important component of tumor tissue, has clinicopathological significance in predicting disease outcome and therapeutic efficacy. However, little evidence in prostate cancer (PCa) is available.MethodsThe cohort of TCGA-PRAD (n=477) was used in this study. Based on the proportion of 22 types of immune cells calculated by CIBERSORT, the TME was classified by K-means clustering and differentially expressed genes (DEGs) were determined. The TMEscore was calculated based on cluster signature genes, which were obtained from DEGs by the random forest method, and the samples were classified into two subtypes. Analyses of somatic mutation and copy number variation (CNVs) were further conducted to identify the genetic characteristics of the two subtypes. Correlation analysis was performed to explore the correlation between TMEscore and the tumor response to immune checkpoint inhibitors (ICIs) as well as the prognosis of PCa.ResultsBased on the distribution of infiltrating immune cells in the TME, we constructed the TMEscore model and classified PCa samples into high and low TMEscore groups. Survival analysis indicated that the high TMEscore group had significantly better survival outcome than the low TMEscore group. Correlation analysis showed a significantly positive correlation between TMEscore and the known prognostic factors of PCa.ConclusionsOur study indicates that the TMEscore could be a potential prognostic biomarker in PCa. A comprehensive description of the characteristics of TME may help predict the response to therapies and provide new treatment strategies for PCa patients.
| S-EPMC8661256 | biostudies-literature
Project description:BackgroundApoptosis-related genes(Args)play an essential role in the occurrence and progression of hepatocellular carcinoma(HCC). However, few studies have focused on the prognostic significance of Args in HCC. In the study, we aim to explore an efficient prognostic model of Asian HCC patients based on the Args.MethodsWe downloaded mRNA expression profiles and corresponding clinical data of Asian HCC patients from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The Args were collected from Deathbase, a database related to cell death, combined with the research results of GeneCards、National Center for Biotechnology Information (NCBI) databases and a lot of literature. We used Wilcoxon-test and univariate Cox analysis to screen the differential expressed genes (DEGs) and the prognostic related genes (PRGs) of HCC. The intersection genes of DEGs and PGGs were seen as crucial Args of HCC. The prognostic model of Asian HCC patients was constructed by least absolute shrinkage and selection operator (lasso)- proportional hazards model (Cox) regression analysis. Kaplan-Meier curve, Principal Component Analysis (PCA) analysis, t-distributed Stochastic Neighbor Embedding (t-SNE) analysis, risk score curve, receiver operating characteristic (ROC) curve, and the HCC data of ICGC database and the data of Asian HCC patients of Kaplan-Meier plotter database were used to verify the model.ResultsA total of 20 of 56 Args were differentially expressed between HCC and adjacent normal tissues (p < 0.05). Univariate Cox regression analysis showed that 10 of 56 Args were associated with survival time and survival status of HCC patients (p < 0.05). There are seven overlapping genes of these 20 and 10 genes, including BAK1, BAX, BNIP3, CRADD, CSE1L, FAS, and SH3GLB1. Through Lasso-Cox analysis, an HCC prognostic model composed of BAK1, BNIP3, CSE1L, and FAS was constructed. Kaplan-Meier curve, PCA, t-SNE analysis, risk score curve, ROC curve, and secondary verification of ICGC database and Kaplan-Meier plotter database all support the reliability of the model.ConclusionsLasso-Cox regression analysis identified a 4-gene prognostic model, which integrates clinical and gene expression and has a good effect. The expression of Args is related to the prognosis of HCC patients, but the specific mechanism remains to be further verified.
| S-EPMC7891020 | biostudies-literature