An integrated approach to identify bimodal genes associated with prognosis in cancer.
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ABSTRACT:
SUBMITTER: Justino JR
PROVIDER: S-EPMC8495773 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Nowadays, the massive amount of data generated by modern sequencing technologies provides an unprecedented opportunity to find genes associated with cancer patient prognosis, connecting basic and translational research. However, treating high dimensionality of gene expression data and integrating it with clinical variables are major challenges to perform these analyses. Here, we present Reboot, an integrative approach to find and validate genes and transcripts (splicing isoforms) associated with cancer patient prognosis from high dimensional expression datasets. Reboot innovates by using a multivariate strategy with penalized Cox regression (LASSO method) combined with a bootstrap approach, in addition to statistical tests and plots to support the findings. Applying Reboot on data from 154 glioblastoma patients, we identified a three-gene signature (IKBIP, OSMR, PODNL1) whose increased derived risk score was significantly associated with worse patients' prognosis. Similarly, Reboot was able to find a seven-splicing isoforms signature related to worse overall survival in 177 pancreatic adenocarcinoma patients with elevated risk scores after uni- and multivariate analyses. In summary, Reboot is an efficient, intuitive and straightforward way of finding genes or splicing isoforms signatures relevant to patient prognosis, which can democratize this kind of analysis and shed light on still under-investigated cancer-related genes and splicing isoforms.
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Project description:BackgroundWith the rapid development and wide application of high-throughput sequencing technology, biomedical research has entered the era of large-scale omics data. We aim to identify genes associated with breast cancer prognosis by integrating multi-omics data.MethodGene-gene interactions were taken into account, and we applied two differential network methods JDINAC and LGCDG to identify differential genes. The patients were divided into case and control groups according to their survival time. The TCGA and METABRIC database were used as the training and validation set respectively.ResultIn the TCGA dataset, C11orf1, OLA1, RPL31, SPDL1 and IL33 were identified to be associated with prognosis of breast cancer. In the METABRIC database, ZNF273, ZBTB37, TRIM52, TSGA10, ZNF727, TRAF2, TSPAN17, USP28 and ZNF519 were identified as hub genes. In addition, RPL31, TMEM163 and ZNF273 were screened out in both datasets. GO enrichment analysis shows that most of these hub genes were involved in zinc ion binding.ConclusionIn this study, a total of 15 hub genes associated with long-term survival of breast cancer were identified, which can promote understanding of the molecular mechanism of breast cancer and provide new insight into clinical research and treatment.
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Project description:BackgroundIntratumoral oxidative stress (OS) has been associated with the progression of various tumors. However, OS has not been considered a candidate therapeutic target for pancreatic cancer (PC) owing to the lack of validated biomarkers.MethodsWe compared gene expression profiles of PC samples and the transcriptome data of normal pancreas tissues from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed OS genes in PC. PC patients' gene profile from the Gene Expression Omnibus (GEO) database was used as a validation cohort.ResultsA total of 148 differentially expressed OS-related genes in PC were used to construct a protein-protein interaction network. Univariate Cox regression analysis, least absolute shrinkage, selection operator analysis revealed seven hub prognosis-associated OS genes that served to construct a prognostic risk model. Based on integrated bioinformatics analyses, our prognostic model, whose diagnostic accuracy was validated in both cohorts, reliably predicted the overall survival of patients with PC and cancer progression. Further analysis revealed significant associations between seven hub gene expression levels and patient outcomes, which were validated at the protein level using the Human Protein Atlas database. A nomogram based on the expression of these seven hub genes exhibited prognostic value in PC.ConclusionOur study provides novel insights into PC pathogenesis and provides new genetic markers for prognosis prediction and clinical treatment personalization for PC patients.
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Project description:Colorectal cancer (CRC) is one of the most common malignances in the gut. Liver is the most common metastasis site of CRC. This study focuses on the primary CRC and its liver metastasis, aiming to discover several liver metastasis related genes and provide therapeutic candidates. We compared gene expression patterns among the groups of normal colorectal mucosa, primary tumor and the liver metastasis using a CRC gene expression dataset. 84 genes were found to be upregulated in both primary tumor and liver metastases. Function enrichment analysis indicated that these genes are enriched in pathways such as chemotaxis, coagulation and lipid metabolism which are crucial in multi-step cancer metastasis. Gene network analysis identified several important hub genes that may be involved in carcinogenesis and liver metastasis. Then we used a validation dataset containing 562 CRC samples with detailed clinical information, to screen prognostic biomarkers for overall survival (OS) and relapse free survival (RFS). Finally, overexpression of THBS2 (thrombospondin 2), INHBB (inhibin, beta B) and BGN (biglycan) were proved to be correlated with poor OS and RFS. In conclusion, this study indicated that chemotaxis, coagulation and lipid metabolism might play critical roles in the processes of carcinogenesis and liver metastasis. THBS2, INHBB and BGN are prognostic markers and potential therapeutic targets for CRC.
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Project description:Tamoxifen is an estrogen receptor (ER) antagonist that is most commonly used for the treatment of ER-positive breast cancer. However, tamoxifen resistance remains a major cause of cancer recurrence and progression. Here, we aimed to identify hub genes implicated in the progression and prognosis of ER-positive breast cancer following tamoxifen treatment. Microarray data (GSE9893) for 155 tamoxifen-treated primary ER-positive breast cancer samples were obtained from the Gene Expression Omnibus database. In total, 1706 differentially expressed genes (DEGs), including 859 up-regulated and 847 down-regulated genes, were identified between relapse and relapse-free samples. Weighted correlation network analysis clustered genes into 13 modules, among which the tan and blue modules were the most significantly related to prognosis. From these two modules, we further identified and validated two prognosis-related hub genes (G-rich RNA sequence binding factor 1 (GRSF1) and microtubule-associated protein τ (MAPT)) via survival analysis based on several publicly available datasets. High expression of GRSF1 predicted poor prognosis, whereas MAPT indicated favorable outcomes in ER-positive breast cancer. Using breast cancer cell lines and tissue samples, we confirmed that GRSF1 was significantly up-regulated and MAPT was down-regulated in the tamoxifen-resistant group compared with the tamoxifen-sensitive group. The prognostic value of GRSF1 and MAPT was also verified in 48 tamoxifen-treated ER-positive breast cancer patients in our hospital. Gene set enrichment analysis (GSEA) suggested that GRSF1 was potentially involved in RNA degradation and cell cycle pathways, while MAPT was strongly linked to immune-related signaling pathways. Taken together, our findings established novel prognostic biomarkers to predict tamoxifen sensitivity, which may facilitate individualized management of breast cancer.
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Project description:The failure to identify biomarkers of clinical significance for cancer diagnosis and prognosis generated a great deal of skepticism in regard to the usefulness of autoantibody-based methods. SEREX was a major advancement in immunoscreening that resulted in the identification of a large group of autoantigens recognized by cancer sera. However, few SEREX-defined autoantigens have proven to have definitive diagnostic value in clinical practice. Often, the identified antigens are patient-specific rather than tumor-specific and many tumor-associated antigens are rare in expression libraries made from non-autologous cells. Since autoantibodies are part of the normal immune response, it can be difficult to single out tumor-associated antibodies from the scores of irrelevant patient-specific responses. In our view, any practical approach for identifying cancer-related autoantigens must include an integral strategy for demonstrating tumor relevance early in the screening process. Care must also be taken not to exclude potentially important autoantibodies by pre-screening manipulations to patient sera. We have introduced substantial modifications in SEREX, designed to minimize confounding effects of unrelated autoantibodies and to eliminate steps that preclude the identification of cancer-related autoantigens commonly recognized by cancer sera. In addition, we incorporate methodology to identify candidate antigens that have potential diagnostic or prognostic value prior to their molecular cloning and characterization.
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Project description:Cancer is a complex disease where molecular mechanism remains elusive. A systems approach is needed to integrate diverse biological information for the prognosis and therapy risk assessment using mechanistic approach to understand gene interactions in pathways and networks and functional attributes to unravel the biological behaviour of tumors.We weighted the functional attributes based on various functional properties observed between cancerous and non-cancerous genes reported from literature. This weighing schema was then encoded in a Boolean logic framework to rank differentially expressed genes. We have identified 17 genes to be differentially expressed from a total of 11,173 genes, where ten genes are reported to be down-regulated via epigenetic inactivation and seven genes are up-regulated. Here, we report that the overexpressed genes IRAK1, CHEK1 and BUB1 may play an important role in ovarian cancer. We also show that these 17 genes can be used to form an ovarian cancer signature, to distinguish normal from ovarian cancer subjects and that the set of three genes, CHEK1, AR, and LYN, can be used to classify good and poor prognostic tumors.We provided a workflow using a Boolean logic schema for the identification of differentially expressed genes by integrating diverse biological information. This integrated approach resulted in the identification of genes as potential biomarkers in ovarian cancer.
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Project description:Bladder cancer prognosis remains dismal due to lack of appropriate biomarkers that can predict its progression. The study aims to identify novel prognostic biomarkers associated with the progression of bladder cancer by utilizing three Gene Expression Omnibus (GEO) datasets to screen differentially expressed genes (DEGs). A total of 1516 DEGs were identified between non-muscle invasive and muscle invasive bladder cancer specimens. To identify genes of prognostic value, we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A total of seven genes, including CDKN2A, CDC20, CTSV, FOXM1, MAGEA6, KRT23, and S100A9 were confirmed with strong prognostic values in bladder cancer and validated by qRT-PCR conducted in various human bladder cancer cells representing stage-specific disease progression. ULCAN, human protein atlas and The Cancer Genome Atlas datasets were used to confirm the predictive value of these genes in bladder cancer progression. Moreover, Kaplan-Meier analysis and Cox hazard ratio analysis were performed to determine the prognostic role of these genes. Univariate analysis performed on a validation set identified a 3-panel gene set viz. CDKN2A, CTSV and FOXM1 with 95.5% sensitivity and 100% specificity in predicting bladder cancer progression. In summary, our study screened and confirmed a 3-panel biomarker that could accurately predict the progression and prognosis of bladder cancer.
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Project description:The aim of the present study was to identify the hub genes and provide insight into the tumorigenesis and development of breast cancer. To examine the hub genes in breast cancer, integrated bioinformatics analysis was performed. Gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database and the differentially expressed genes (DEGs) were identified using the 'limma' package in R. Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis was used to determine the functional annotations and potential pathways of the DEGs. Subsequently, a protein-protein interaction network analysis and weighted correlation network analysis (WGCNA) were conducted to identify hub genes. To confirm the reliability of the identified hub genes, RNA gene expression profiles were obtained from The Cancer Genome Atlas (TCGA)-breast cancer database, and WGCNA was used to screen for genes that were markedly correlated with breast cancer. By combining the results from the GEO and TCGA datasets, 15 hub genes were identified to be associated with breast cancer pathophysiology. Overall survival analysis was performed to examine the association between the expression of hub genes and the overall survival time of patients with breast cancer. Higher expression of all hub genes was associated with significantly shorter overall survival in patients with breast cancer compared with patients with lower levels of expression of the respective gene.
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Project description:Background Esophageal squamous cell carcinoma (ESCC) is a serious threat to human health and life. The National Center for Biotechnology Information Gene Expression Omnibus (NCBI-GEO) database provides valuable information on genes related to the pathogenesis and prognosis of ESCC, which helps us to make in-depth understanding about the disease and improve its prognosis. Methods Four microarray profiles [GSE77861 (African Americans), GSE26886 (Germans), GSE17351 (Americans), and GSE45670 (Chinese)] from the NCBI-GEO including 49 ESCC tissues and 41 corresponding normal tissues were collected. Integrated bioinformatics methods, including protein-protein interaction (PPI) network analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and Kaplan-Meier plotter were applied to determine the differentially expressed genes (DEGs) in ESCC together with their core functions and relationship with survival. Results A total of 220 upregulated and 112 downregulated genes were identified as DEGs in ESCC, of which, 40 upregulated genes were core function genes. The DEGs were mostly involved in DNA replication and cell cycle pathways. Survival analysis and Bonferroni adjustment showed kinesin family member 18A (KIF18A) and TTK protein kinase (TTK) to be related to prognosis in ESCC. Conclusions The findings of the present study verified the previously proposed association between TTK and patient survival in ESCC, and identified KIF18A as ESCC prognosis-related gene markers for the first time. The underlying mechanism needs to be further investigated using larger sample size studies and biological experiments in future.
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