Introduction
Ovarian cancer (OC) is a gynecological tumor with high morbidity and mortality and about 150,000 women die of OC each year [
1]. The occurrence and development of OC is a multi-system, multi-step cellular biochemical process, which is regulated by a variety of cytokines and signaling pathways [
2]. Due to the lack of typical clinical symptoms in the early stages of OC, 75% of OC patients are diagnosed at an advanced stage, and more than 70% of patients relapse after treatment [
3]. Therefore, how to diagnose early, effectively treat and improve the prognosis of OC patients is an urgent problem to be solved.
Tumor cells are mainly metabolized by glycolysis regardless of the presence of oxygen. 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]. In recent years, 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]. For instance, lncRNA ANRIL up-regulates the expression of glucose transporter 1(GLUT1) and LDHA, thereby increasing glucose uptake and promoting the progression of nasopharyngeal carcinoma [
9]. LINC00092 directly binds to PFKFB2 to enhance glycolysis and ultimately promote tumor genesis and development [
10]. In bladder cancer, lncRNA UCA1 is overexpressed and promotes glycolysis by upregulation of hexokinase 2 (HK2), and also promotes aerobic glycolysis [
11]. However, lncRNAs involved in the glycolysis reprogramming of OC remain unclear.
Therefore, in our study, we found five glycolysis-related lncRNAs (GRLs) with significant prognostic value from TCGA dataset. A GRL-signature with prognostic value was developed. In addition, we identified differences in enrichment pathways, immune microenvironment, immune checkpoints, m6A regulatory factors, and ferroptosis-related genes between risk groups. The networks based on co-expression, ceRNA, cis and trans interaction provided insights into the regulatory mechanisms of GRLs.
Material and methods
Data downloading and pretreatment
We downloaded the clinical data with RNA sequencing profiles of OC patients from TCGA dataset [
12]. The Ensemble expression matrix was transformed into Gene Symbol expression matrix and compared it with the position of lncRNA chromosome in GENCODE database to identify lncRNAs [
13]. A total of 274 glycolysis-related genes (GRGs) were obtained from MSigDB database [
14]. We screened differentially expressed GRGs and annotated lncRNAs using limma package (
P < 0.05, |logFC| > 1) [
15]. Pearson correlation coefficients between differential GRGs and lncRNAs were computed to filtrate GRLs (|r| > 0.4,
P < 0.05) using cor function of R.
Development of the signature
After screening prognostic GRLs through Univariate Cox regression (
P < 0.05) [
16], the LASSO Cox regression [
17] from glmnet package of R [
18] and 20 times cross-validation analysis was employed to filtrate optimal combination of GRL markers. A risk score model for OC patients was constructed based on following formula:
$$\mathrm{Risk}\ \mathrm{score}=\sum {\upbeta}_{\mathrm{lncRNA}}\times {\mathrm{Exp}}_{\mathrm{lncRNA}}$$
In the risk score (RS) formula, βlncRNA meant the regression coefficient of each lncRNA calculated in the multivariate Cox regression analysis and ExplncRNA represented the expression value of each lncRNA in the sample. Whereafter, the RS of each OC patient was calculated and the calculated median RS was used as the critical value to further divided the OC patients into high-risk and low-risk groups (low-risk group<median, high-risk group ≥ median). Furthermore, in order to verify the accuracy of the signature in predicting the prognosis, all samples (total set, TS) were randomly and evenly divided into two validation sets (VS1 and VS2).
We used the timeROC package in the R language to draw ROC curves to evaluate the predictive ability to predict 1, 2, 3, and 5 years of survival. Besides, a visual nomogram was constructed and verified by the calibration curve to determine the accuracy of the risk model for serving as an independent prognostic factor.
Characteristics and application of the signature
To explore the relationship and degree of correlation between potential GRGs and biological pathways in OC, biofunctional analysis was performed on potential GRGs using DAVID [
19,
20].
Considering that hypoxic microenvironment is closely related to aerobic glycolysis of OC, we downloaded a collection of hypoxia-related genes (HRGs), HALLMARK HYPOXIA, from MSigDB. The enrichment fraction of hypoxia pathway in different samples was calculated based on ssGSEA arithmetic from GSVA of R to obtain hypoxia scores [
21]. Simultaneously, the expression levels of immune checkpoint genes (CD274, CD47, HAVCR2, LAG3, SIRPA, TNFRSF4 and VTCN1), m6A regulators [
22], and ferroptosis-related genes (FRGs) [
23] were extracted to contrast their expression differences in risk groups by intergroup T test.
The immune microenvironment is also closely related to the occurrence and development of OC. According to the expression data of the OC samples, the immune and stromal scores were estimated by ESTIMATE to represent the presence of stroma and immune cells [
24]. Based on ssGSEA [
25], the enrichment fraction of 28 immune cells was calculated to represent the relative abundance of each TME-infiltrated cell in OC samples. Moreover, three algorithms CIBERSORT [
26,
27], MCPcounter [
28], and xCell [
29], were wielded to compare the difference in the proportion of various immune cells in different risk groups.
In order to explore the relevant regulatory mechanism of this risk prediction model, we established networks based on co-expression, ceRNA, cis and trans interaction. GRG-GRL co-expression (|r| > 0.4,
P < 0.05) network was constructed and visualized by Cytoscape [
30]. The targeted glycolysis-related mRNAs by corresponding miRNAs were speculated by miRWalk [
31]. Further, we synthesized the results of six commonly used databases (miRWalk, Microt4, miRanda, miRDB, RNA22 and Targetscan) to obtain the miRNA-GRG relationship pair if the predicted miRNA-GRG relationship pair appeared in ≥5 databases. The miRNAs targeted by corresponding GRLs of the risk model were speculated by miranda (v3.3a, Score > =140, Energy<= − 20) [
32]. GRLs and GRGs regulated by the same miRNA with positive co-expression relationship were defined as ceRNAs mutually. Based on previous literature, we predicted cis [
33] and trans [
34] interaction between GRLs and GRGs.
Moreover, the potential response to immune checkpoint blockade (ICB) was predicted with TIDE algorithm [
35]. We extracted chemotherapy drugs from GDSC database [
36] and evaluated the IC50 level by using pRRophetic [
37].
Statistical analysis
R packages (v4.0.2) and GraphPad Prism (v8.0) were used for statistical analysis. T test was used for inter-group comparison. Pearson correlation analysis was conducted to analyze the correlation between GRGs and lncRNAs. Univariate and multivariate Cox regression analysis was conducted to analyze the related factors affecting the overall survival of OC patients. P < 0.05 was considered statistically significant.
Discussion
The fate of tumor cells is directly related to their energy metabolism [
38]. Tumor cells prefer glycolysis as an inefficient metabolic mode, which provides new ideas and methods for clinical treatment of tumors [
39]. The reasons are as follows [
40]: glycolysis can provide the energy needed for tumor cell proliferation; It can maintain a low pH tumor microenvironment, which is conducive to tumor cell proliferation, drug resistance, invasion and metastasis; A large number of nucleic acid precursors can be produced in preparation for proliferation. For ovarian cancer and other tumors with high proliferation, invasion, metastasis and chemotherapy resistance, it is of great significance to explore the regulation of its glycolytic pathway. The aim of studying the glycolysis pathway of OC is to develop ideal targeted drugs. However, the mechanism of action of some drugs targeting the glycolytic pathway of OC is still not clear, so the in-depth study of their molecular mechanism is still of great significance.
As is known to all that lncRNAs have been proved to play an important role in the occurrence and development of tumors. In recent years, lncRNAs have been reported to regulate the energy metabolism of tumors and thus affect the malignant behavior of tumors, which also partially reveals the molecular mechanism of glycolysis reprogramming. For example, lncRNA AGPG increases the stability of PFKFB3 by inhibiting ubiquitination at Lys302 and subsequent proteasomal-dependent degradation of PFKFB3 and activates glycolytic flux, causing metabolic reprogramming in esophageal cancer cells [
41]. In addition, PFKFB3 can also be phosphorylated by lncRNA YIYA, increasing the conversion of fructuce-6-phosphate to fructuce-2, 6-Bisphosphate, and promoting the reprogramming and growth of glucose metabolism in breast cancer [
42]. However, researches of GRLs are still scarce in OC.
In order to verify the importance of glycolysis-related lncRNAs (GRLs) in ovarian cancer progress, GRL-related prognostic and diagnostic model were developed. The gene expression level of 535 GRLs were in investigated in OC and normal tissues. The significance of these GRLs related to survival rates were then studied and 35 GRLs were discovered significantly prognostic. In our study, we identified and validated a signature containing five GRLs with prognostic value. A total of 21 microenvironment cells, four immune checkpoint genes (CD274, LAG3, VTCN1, and CD47), 16 m6A regulators, and 126 FRGs showed different levels between the two groups. It has been reported that the hypoxic and acidic microenvironment induced by tumor glycolysis can cause metabolization-mediated T cell dysfunction, which may be one of the mechanisms of tumor cell metabolic reprogramming mediated immune escape [
43,
44]. It has also been found glycolysis of tumors can induce tumor immunosuppression and immune escape [
45]. Therefore, tumor immunotherapy strategies based on metabolic regulation can improve the effectiveness of immunotherapy [
45]. Many studies have found that m6A regulatory factors can regulate the expression of enzymes related to the glucose metabolism pathway, thus affecting the glycolysis of tumors [
46‐
48]. All these provide a reference for us to study the specific mechanism of glycolysis in tumor. The occurrence and development of malignant tumors is sophisticated and we hope to explore the molecular mechanism of glycolysis (via m6A modification, ferroptosis or immune) to promote the efficacy of immunotherapy with further research.
Our study still has some limitations. Firstly, due to the limited number of OC samples that can annotate lncRNA expression, more patients with homologous information were needed to incorporate into study and prove the credibility of our study. Secondly, we explored the functions of these five lncRNAs only through bioinformatics analysis, and experimental data were needed to support these conclusions. Despite these limitations, our study used two validation sets, ROC, and nomogram to demonstrate the effectiveness of the risk model for prognostic prediction.
Conclusions
In summary, our identified and validated risk model based on five glycolysis -related lncRNAs is an independent prognostic factor for OC patients. Through comprehensive analyses, the GRL-model provides insights into clinical applications for OC.
Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit
http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (
http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.