Dataset Information

Construction of a pancreatic cancer prediction model for oxidative stress-related lncRNA.

ABSTRACT: Long non-coding RNAs (lncRNAs) may play a role in oxidative stress by altering the tumor microenvironment, thereby affecting pancreatic cancer progression. There is currently limited information on oxidative stress-related lncRNAs as novel prognostic markers of pancreatic cancer. Gene expression and clinical data of patients with pancreatic cancer were downloaded from The Cancer Genome Atlas (TCGA-PAAD) and the International Cancer Genome Consortium (ICGC-PACA) database. A weighted gene co-expression network analysis (WGCNA) was constructed to identify genes that were differentially expressed between normal and tumor samples. Based on the TCGA-PAAD cohort, a prediction model was established using lasso regression and Cox regression. The TCGA-PAAD and ICGC-PACA cohorts were used for internal and external validation, respectively. Furthermore, a nomogram based on clinical characteristics was used to predict mortality of patients. Differences in mutational status and tumor-infiltrating immune cells between risk subgroups were also explored and model-based lncRNAs were analyzed for potential immune-related therapeutic drugs. A prediction model for 6-lncRNA was established using lasso regression and Cox regression. Kaplan-Meier survival curves and receiver operating characteristic (ROC) curves indicated that patients with lower risk scores had a better prognosis. Combined with Cox regression analysis of clinical features, risk score was an independent factor predicting overall survival of patients with pancreatic cancer in both the TCGA-PAAD and ICGC-PACA cohorts. Mutation status and immune-related analysis indicated that the high-risk group had a significantly higher gene mutation rate and a higher possibility of immune escape, respectively. Furthermore, the model genes showed a strong correlation with immune-related therapeutic drugs. A pancreatic cancer prediction model based on oxidative stress-related lncRNA was established, which may be used as a biomarker related to the prognosis of pancreatic cancer to evaluate the prognosis of pancreatic cancer patients.

SUBMITTER: Huang H

PROVIDER: S-EPMC10076407 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Construction of a pancreatic cancer prediction model for oxidative stress-related lncRNA.

Huang Hao H Wei Yaqing Y Yao Hao H Chen Ming M Sun Jinjin J

Functional & integrative genomics 20230405 2

Long non-coding RNAs (lncRNAs) may play a role in oxidative stress by altering the tumor microenvironment, thereby affecting pancreatic cancer progression. There is currently limited information on oxidative stress-related lncRNAs as novel prognostic markers of pancreatic cancer. Gene expression and clinical data of patients with pancreatic cancer were downloaded from The Cancer Genome Atlas (TCGA-PAAD) and the International Cancer Genome Consortium (ICGC-PACA) database. A weighted gene co-expre ...[more]

PMID: 37020128

Similar Datasets

Project description:IntroductionBreast cancer is the most common form of cancer worldwide and a serious threat to women. Hypoxia is thought to be associated with poor prognosis of patients with cancer. Long non-coding RNAs are differentially expressed during tumorigenesis and can serve as unambiguous molecular biomarkers for the prognosis of breast cancer.MethodsHere, we accessed the data from The Cancer Genome Atlas for model construction and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses to identify biological functions. Four prognostic hypoxia-related lncRNAs identified by univariate, LASSO, and multivariate Cox regression analyses were used to develop a prognostic risk-related signature. Kaplan-Meier and receiver operating characteristic curve analyses were performed, and independent prognostic factor analysis and correlation analysis with clinical characteristics were utilized to evaluate the specificity and sensitivity of the signature. Survival analysis and receiver operating characteristic curve analyses of the validation cohort were operated to corroborate the robustness of the model.ResultsOur results demonstrate the development of a reliable prognostic gene signature comprising four long non-coding RNAs (AL031316.1, AC004585.1, LINC01235, and ACTA2-AS1). The signature displayed irreplaceable prognostic power for overall survival in patients with breast cancer in both the training and validation cohorts. Furthermore, immune cell infiltration analysis revealed that B cells, CD4 T cells, CD8 T cells, neutrophils, and dendritic cells were significantly different between the high-risk and low-risk groups. The high-risk and low-risk groups could be precisely distinguished using the risk signature to predict patient outcomes.DiscussionIn summary, our study proves that hypoxia-related long non-coding RNAs serve as accurate indicators of poor prognosis and short overall survival, and are likely to act as potential targets for future cancer therapy.

Project description:Objective: Pancreatic adenocarcinoma (PAAD) is a highly malignant gastrointestinal tumor with almost similar morbidity and mortality. In this study, based on bioinformatics, we investigated the role of gene methylation in PAAD, evaluated relevant factors affecting patient prognosis, screened potential anti-cancer small molecule drugs, and constructed a prediction model to assess the prognosis of PAAD. Methods: Clinical and genomic data of PAAD were collected from the Tumor Genome Atlas Project (TCGA) database and gene expression profiles were obtained from the GTEX database. Analysis of differentially methylated genes (DMGs) and significantly differentially expressed genes (DEGs) was performed on tumorous samples with KRAS wild-type and normal samples using the "limma" package and combined analysis. We selected factors significantly associated with survival from the significantly differentially methylated and expressed genes (DMEGs), and their fitting into a relatively streamlined prognostic model was validated separately from the internal training and test sets and the external ICGC database to show the robustness of the model. Results: In the TCGA database, 2,630 DMGs were identified, with the largest gap between DMGs in the gene body and TSS200 region. 318 DEGs were screened, and the enrichment analysis of DMGs and DEGs was taken to intersect DMEGs, showing that the DMEGs were mainly related to Olfactory transduction, natural killer cell mediated cytotoxicity pathway, and Cytokine -cytokine receptor interaction. DMEGs were able to distinguish well between PAAD and paraneoplastic tissues. Through techniques such as drug database and molecular docking, we screened a total of 10 potential oncogenic small molecule compounds, among which felbamate was the most likely target drug for PAAD. We constructed a risk model through combining three DMEGs (S100P, LY6D, and WFDC13) with clinical factors significantly associated with prognosis, and confirmed the model robustness using external and internal validation. Conclusion: The classification model based on DMEGs was able to accurately separate normal samples from tumor samples and find potential anti-PAAD drugs by performing gene-drug interactions on DrugBank.

Project description:BackgroundPancreatic cancer patients with similar clinicopathological status exhibit substantially different therapeutic responses, which might be caused by the vast molecular heterogeneity of tumors. In this study, we attempted to identify specific molecular subgroups and construct a prognostic prediction model based on the expression level of immune-related genes in pancreatic cancer. The transcriptome profiling, single nucleotide variation, copy number variation, clinicopathological information, and follow-up data of pancreatic cancer patients were obtained from The Cancer Genome Atlas database. Thereafter, the immune-related genes with prognostic significance were identified for further consensus cluster analysis. The molecular characteristics and clinicopathological information were compared between the identified subgroups, and a weighted correlation network analysis was performed to identify the hub genes associated with the subgroups. Finally, the prognostic prediction model based on immune-related genes was established using the least absolute shrinkage and selection operator (LASSO) analysis.ResultsA total of 67 immune-relevant genes with prognostic significance were selected and used for the consensus cluster analysis. The total samples were divided into two groups, C1 and C2. The subgroup C1 had a significantly worse prognosis than C2, as well as lower levels of immune cell infiltration, which indicate an immunosuppressed state. The mutational rate of the cancer-related genes including KRAS, TP53, and RNF43 was higher in the C1 subgroup. The C1 subgroup was associated with more advanced tumor grade and T stage and with higher mortality. Using LASSO regression, we developed a prognostic prediction model based on the expression levels of 19 immune-related genes, which we validated in three external data sets. In addition, we identified four potential therapeutic and prognostic biomarkers (TNNT1, KCNN4, SH2D3A, and PHLDA2).ConclusionWe identified two novel molecular subgroups of pancreatic cancer and developed a prognostic prediction model based on the expression levels of immune-related genes, which could be used in a clinical setting and could aid in unraveling the molecular processes leading to the development of pancreatic cancer.

Project description:Background Esophageal cancer (EC), one highly malignant gastrointestinal cancer, is the 6th leading cause of cancer-related deaths worldwide. Necroptosis and long non-coding RNA (lncRNA) play important roles in the occurrence and development of EC, but the research on the role of necroptosis-related lncRNA in EC is not conclusive. This study aims to use bioinformatics to investigate the prognostic value of necroptosis-related lncRNA in EC. Methods Transcriptome data containing EC and normal samples, and clinical information were obtained from the Cancer Genome Atlas database. 102 necroptosis-related genes were obtained from Kanehisa Laboratories. Necroptosis-related lncRNAs were screened out via univariate, multivariate Cox and the least absolute shrinkage and selection operator regression analyses to construct the risk predictive model. The reliability of the risk model was evaluated mainly through quantitative real-time PCR (qRT-PCR), the receiver operating characteristic (ROC) curves and the constructed nomogram. KEGG pathways were explored in the high- and low-risk groups of EC patients via gene set enrichment analyses (GSEA) software. Immune microenvironment and potential therapeutic agents in risk groups were also analyzed. Results A 6 necroptosis-related lncRNAs risk model composed of AC022211.2, Z94721.1, AC007991.2, SAMD12-AS1, AL035461.2 and AC051619.4 was established to predict the prognosis level of EC patients. qRT-PCR analysis showed upregulated Z94721.1 and AL035461.2 mRNA levels and downregulated AC051619.4 mRNA level in EC tissues compared with normal tissues. According to clinical characteristics, the patients in the high-risk group had a shorter overall survival than the low-risk group. The ROC curve and nomogram confirmed this model as one independent and predominant predictor. GSEA analysis showed metabolic and immune-related pathways enriched in the risk model. Most of the immune cells and immune checkpoints were positively correlated with the risk model, mainly active in the high-risk group. For the prediction of potential therapeutic drugs, 16 compounds in the high-risk group and 2 compounds in the low-risk group exhibited higher sensitivity. Conclusions Our results supported the necroptosis-related lncRNA signature could independently predict prognosis of EC patients, and provided theoretical basis for improving the clinical treatment of EC. Supplementary Information The online version contains supplementary material available at 10.1186/s12876-022-02421-8.

Dataset Information

Construction of a pancreatic cancer prediction model for oxidative stress-related lncRNA.

Publications

Construction of a pancreatic cancer prediction model for oxidative stress-related lncRNA.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets