A risk model of gene signatures for predicting platinum response and survival in ovarian cancer

Abstract

Background: Ovarian cancer (OC) is the deadliest tumor in the female reproductive tract. And increased resistance to platinum-based chemotherapy represents the major obstacle in the treatment of OC currently. Robust and accurate gene expression models are crucial tools in distinguishing platinum therapy response and evaluating the prognosis of OC patients.

Methods: In this study, 230 samples from The Cancer Genome Atlas (TCGA) OV dataset were subjected to mRNA expression profiling, single nucleotide polymorphism (SNP), and copy number variation (CNV) analysis comprehensively to screen out the differentially expressed genes (DEGs). An SVM classifier and a prognostic model were constructed using the Random Forest algorithm and LASSO Cox regression model respectively via R. The Gene Expression Omnibus (GEO) database was applied as the validation set.

Results: Forty-eight differentially expressed genes (DEGs) were figured out through integrated analysis of gene expression, single nucleotide polymorphism (SNP), and copy number variation (CNV) data. A 10-gene classifier was constructed which could discriminate platinum-sensitive samples precisely with an AUC of 0.971 in the training set and of 0.926 in the GEO dataset (GSE638855). In addition, 8 optimal genes were further selected to construct the prognostic risk model whose predictions were consistent with the actual survival outcomes in the training cohort (p = 9.613e-05) and validated in GSE638855 (p = 0.04862). PNLDC1, SLC5A1, and SYNM were then identified as hub genes that were associated with both platinum response status and prognosis, which was further validated by the Fudan University Shanghai cancer center (FUSCC) cohort.

Conclusion: These findings reveal a specific risk model that could serve as effective biomarkers to identify patients' platinum response status and predict survival outcomes for OC patients. PNLDC1, SLC5A1, and SYNM are the hub genes that may serve as potential biomarkers in OC treatment.

Keywords: Biomarkers; Ovarian cancer; Platinum response; Prognostic model.

Fig. 1

Flowchart of this study

Fig. 2

Identification of DEGs. A The CN signal of the TCGA samples. The horizontal axis represents the detection area on each chromosome, the vertical axis represents the 230 ovarian cancer samples included in the analysis. 1–22 and X, Y indicates the chromosome number, and blue indicates log2 (CN) < 0, while red indicates log2 (CN) level > 0. B Volcano plots of the DEGs in gene expression. C Volcano plots of the DEGs in CN signals. D The Venn diagram of the DEGs and 108 genes as overlapping genes in both expression and CN signal levels. E Gene Ontology (GO) functional enrichment analysis of the 48 DEGs in the biological process subsection of GO (BP); molecular function subsection of GO (MF); a cellular component subsection of GO (CC)

Fig. 3

Construction and evaluation of the SVM classifier. A OOB error calculated by the RandomForest algorithm and 10 genes were selected optimally when the OOB error is the smallest. B The receiver operating characteristic (ROC) curve (area under the curve (AUC) of the TCGA training set (solid line) and GSE63885 validation set (dotted line)

Fig. 4

Survival analysis of the DEGs and clinical factors. A The Venn diagram of DEGs related to prognosis. B The K-M curve of the overall survival of the patients with different platinum response statuses

Fig. 5

Construction and validation of the prognostic risk model. A. Cross-validation likelihood filters the lambda parameter (20.88803) when cvl takes the maximum value (− 771.2244). B Based on the L1-penalized regularized regression algorithm, the optimal prognostic gene coefficient distribution line for Cox-PH model screening. C Prognostic prediction by the prognostic risk model in the TCGA training dataset. D Prognostic prediction by the prognostic risk model in the GSE63885 validation set. E The correlation between the K-M curve of the platinum response status and the prognostic model prediction

Fig. 6

Screening and validation of the hub genes. A The intersection of the genes between the SVM classifier and the prognostic risk model. B The expression of PNLDC1, SLC5A1, and SYNM in the training TCGA database. C The expression of PNLDC1, SLC5A1, and SYNM in the validation set GSE63885. D Representative immunohistochemistry images of PNLDC1, SLC5A1, and SYNM. E The expression of PNLDC1, SLC5A1, and SYNM in the FUSCC cohort

Fig. 7

Survival analysis of the hub genes. A PFS analysis of PNLDC1. B PFS analysis of SLC5A1. C PFS analysis of SYNM