Abstract
BackgroundNot only glycolysis but also lncRNAs play a significant role in the growth, proliferation, invasion and metastasis of of ovarian cancer (OC). However, researches about glycolysis -related lncRNAs (GRLs) remain unclear in OC. Herein, we first constructed a GRL-based risk model for patients with OC.MethodsThe processed RNA sequencing (RNA-seq) profiles with clinicopathological data were downloaded from TCGA and glycolysis-related genes (GRGs) were obtained from MSigDB. Pearson correlation coefficient between glycolysis-related genes (GRGs) and annotated lncRNAs (|r| > 0.4 and p < 0.05) were calculated to identify GRLs. After screening prognostic GRLs, a risk model based on five GRLs was constructed using Univariate and Cox regression. The identified risk model was validated by two validation sets. Further, the differences in clinicopathology, biological function, hypoxia score, immune microenvironment, immune checkpoint, immune checkpoint blockade, chemotherapy drug sensitivity, N6-methyladenosine (m6A) regulators, and ferroptosis-related genes between risk groups were explored by abundant algorithms. Finally, we established networks based on co-expression, ceRNA, cis and trans interaction.ResultsA total of 535 GRLs were gained and 35 GRLs with significant prognostic value were identified. The prognostic signature containing five GRLs was constructed and validated and can predict prognosis. The nomogram proved the accuracy of the model for predicting prognosis. After computing hypoxia score of each sample by ssGSEA, we found patients with higher risk scores exhibited higher hypoxia score and high hypoxia score was a risk factor. It was revealed that a total of 21 microenvironment cells (such as Central memory CD4 T cell, Neutrophil, Regulatory T cell and so on) and Stromal score had significant differences between the two groups. Four immune checkpoint genes (CD274, LAG3, VTCN1, and CD47) showed disparate expression levels in the two groups. Besides, 16 m6A regulators and 126 ferroptosis-related genes were expressed higher in the low-risk group. GSEA revealed that the risk groups were associated with tumor-related pathways. The two risk groups were confirmed to be sensitive to several chemotherapeutic agents and patients in the low-risk group were more sensitive to ICB therapy. The networks based on co-expression, ceRNA, cis and trans interaction provided insights into the regulatory mechanisms of GRLs.ConclusionsOur identified and validated risk model based on five GRLs is an independent prognostic factor for OC patients. Through comprehensive analyses, findings of our study uncovered potential biomarker and therapeutic target for the risk model based on the GRLs.
Highlights
Ovarian cancer (OC) is a gynecological tumor with high morbidity and mortality and about 150,000 women die of ovarian cancer (OC) each year [1]
62 Gene ontology (GO) BP and 33 KEGG pathways were enriched based on the 116 Glycolysis-related gene (GRG) (Additional file 1: Table S1)
The identified GRGs were associated with several important biological processes in tumor genesis and development observably, such as, response to hypoxia, AMPK signaling pathway, HIF-1 signaling pathway, and so on
Summary
Ovarian cancer (OC) is a gynecological tumor with high morbidity and mortality and about 150,000 women die of OC each year [1]. Large amounts of glucose are consumed with the production of lactic acid. This phenomenon is called aerobic glycolysis or Warburg effect [4]. Long non-coding RNA (lncRNA) is defined as a large class of non- protein-coding, regulatory RNAs with molecules longer than 200 nucleotides, which play key roles in tumorigenesis and progression [5, 6]. More and more studies have shown that lncRNA plays a key regulatory role in tumor metabolism and is involved in glucose metabolism pathway [7, 8]. Glycolysis and lncRNAs play a significant role in the growth, proliferation, invasion and metastasis of of ovarian cancer (OC). Researches about glycolysis -related lncRNAs (GRLs) remain unclear in OC. We first constructed a GRL-based risk model for patients with OC
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