Construction of a prognostic 6-gene signature for breast cancer based on multi-omics and single-cell data
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ABSTRACT:
SUBMITTER: Xing Z
PROVIDER: S-EPMC10698552 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:Early identification of gastric cancer (GC) is associated with a superior survival rate compared to advanced GC. However, the poor specificity and sensitivity of traditional biomarkers suggest the importance of identifying more effective biomarkers. This study aimed to identify novel biomarkers for the prognosis of GC and construct a risk score (RS) signature based on these biomarkers, with to validation of its predictive performance. We used multi-omics data from The Cancer Genome Atlas to analyze the significance of differences in each omics data and combined the data using Fisher's method. Hub genes were subsequently subjected to univariate Cox and LASSO regression analyses and used to construct the RS signature. The RS of each patient was calculated, and the patients were divided into two subgroups according to the RS. The RS signature was validated in two independent datasets from the Gene Expression Omnibus and subsequent analyses were subsequently conducted. Five immune-related genes strongly linked to the prognosis of GC patients were obtained, namely CGB5, SLC10A2, THPO, PDGFRB, and APOD. The results revealed significant differences in overall survival between the two subgroups (p < 0.001) and indicated the high accuracy of the RS signature. When validated in two independent datasets, the results were consistent with those in the training dataset (p = 0.003 and p = 0.001). Subsequent analyses revealed that the RS signature is independent and has broad applicability among various GC subtypes. In conclusion, we used multi-omics data to obtain five immune-related genes comprising the RS signature, which can independently and effectively predict the prognosis of GC patients with high accuracy.
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Project description:Breast cancer is a cancer of high complexity and heterogeneity, with differences in prognosis and survival among patients of different subtypes. Copy number variations (CNVs) within enhancers are crucial drivers of tumorigenesis by influencing expression of their targets. In this study, we performed an integrative approach to identify CNA-driven enhancers and their effect on expression of target genes in four breast cancer subtypes by integrating expression data, copy number data and H3K27ac data. We identified 672, 555, 531, 361 CNA-driven enhancer-gene pairs and 280, 189, 113 and 98 CNA-driven enhancer-lncRNA pairs in the Basal-like, Her2, LumA and LumB subtypes, respectively. We then reconstructed a CNV-driven enhancer-lncRNA-mRNA regulatory network in each subtype. Functional analysis showed CNA-driven enhancers play an important role in the progression of breast cancer subtypes by influencing P53 signaling pathway, PPAR signaling pathway, systemic lupus erythematosus and MAPK signaling pathway in the Basal-like, Her2, LumA and LumB subtypes, respectively. We characterized the potentially prognostic value of target genes of CNV-driven enhancer and lncRNA-mRNA pairs in the subtype-specific network. We identified MUM1 and AC016876.1 as prognostic biomarkers in LumA and Basal-like subtypes, respectively. Higher expression of MUM1 with an amplified enhancer exhibited poorer prognosis in LumA patients. Lower expression of AC016876.1 with a deleted enhancer exhibited poorer survival outcomes of Basal-like patients. We also identified enhancer-related lncRNA-mRNA pairs as prognostic biomarkers, including AC012313.2-MUM1 in the LumA, AC026471.4-PLK5 in the LumB, AC027307.2-OAZ1 in the Basal-like and AC022431.1-HCN2 in the Her2 subtypes. Finally, our results highlighted target genes of CNA-driven enhancers and enhancer-related lncRNA-mRNA pairs could act as prognostic markers and potential therapeutic targets in breast cancer subtypes.
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Project description:PurposeTo identify a gene signature for the prognosis of breast cancer using high-throughput analysis.MethodsRNASeq, single nucleotide polymorphism (SNP), copy number variation (CNV) data and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA), and randomly divided into training set or verification set. Genes related to breast cancer prognosis and differentially expressed genes (DEGs) with CNV or SNP were screened from training set, then integrated together for feature selection of identify robust biomarkers using RandomForest. Finally, a gene-related prognostic model was established and its performance was verified in TCGA test set, Gene Expression Omnibus (GEO) validation set and breast cancer subtypes.ResultsA total of 2287 prognosis-related genes, 131 genes with amplified copy numbers, 724 gens with copy number deletions, and 280 genes with significant mutations screened from Genomic Variants were closely correlated with the development of breast cancer. A total of 120 candidate genes were obtained by integrating genes from Genomic Variants and those related to prognosis, then 6 characteristic genes (CD24, PRRG1, IQSEC3, MRGPRX, RCC2, and CASP8) were top-ranked by RandomForest for feature selection, noticeably, several of these have been previously reported to be associated with the progression of breast cancer. Cox regression analysis was performed to establish a 6-gene signature, which can stratify the risk of samples from training set, test set and external validation set, moreover, the five-year survival AUC of the model in the training set and validation set was both higher than 0.65. Thus, the 6-gene signature developed in the current study could serve as an independent prognostic factor for breast cancer patients.ConclusionThis study constructed a 6-gene signature as a novel prognostic marker for predicting the survival of breast cancer patients, providing new diagnostic/prognostic biomarkers and therapeutic targets for breast cancer patients.
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Project description:There is an unmet clinical need to identify patients with early-stage non-small cell lung cancer (NSCLC) who are likely to develop recurrence and to predict their therapeutic responses. Our previous study developed a qRT-PCR-based seven-gene microfluidic assay to predict the recurrence risk and the clinical benefits of chemotherapy. This study showed it was feasible to apply this seven-gene panel in RNA sequencing profiles of The Cancer Genome Atlas (TCGA) NSCLC patients (n = 923) in randomly partitioned feasibility-training and validation sets (p < 0.05, Kaplan-Meier analysis). Using Boolean implication networks, DNA copy number variation-mediated transcriptional regulatory network of the seven-gene signature was identified in multiple NSCLC cohorts (n = 371). The multi-omics network genes, including PD-L1, were significantly correlated with immune infiltration and drug response to 10 commonly used drugs for treating NSCLC. ZNF71 protein expression was positively correlated with epithelial markers and was negatively correlated with mesenchymal markers in NSCLC cell lines in Western blots. PI3K was identified as a relevant pathway of proliferation networks involving ZNF71 and its isoforms formulated with CRISPR-Cas9 and RNA interference (RNAi) profiles. Based on the gene expression of the multi-omics network, repositioning drugs were identified for NSCLC treatment.
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