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. 2024 May 8:14:1384778.
doi: 10.3389/fonc.2024.1384778. eCollection 2024.

Correlation study of serum lipid levels and lipid metabolism-related genes in cervical cancer

Lin Cheng  1 , Zhuo Li  2 , Qingmei Zheng #  1 , Qin Yao #  1
Affiliations

Correlation study of serum lipid levels and lipid metabolism-related genes in cervical cancer

Lin Cheng et al. Front Oncol. .

Abstract

Objective: Lipid metabolism plays an important role in cancer. The aim of this study was to investigate the relationship between lipid metabolism and the development of cervical cancer, and to explore the prognostic significance of lipid metabolism-related genes in patients with cervical cancer.

Methods: Initially, we retrospectively collected data from 1589 cervical cancer patients treated at the Affiliated Hospital of Qingdao University, with 1589 healthy individuals from the physical examination center serving as the control group. The correlation between their serum lipid levels and cervical cancer was analyzed. Subsequently, leveraging public databases, we conducted comprehensive studies on lipid metabolism-related genes. Additionally, we analyzed RNA expression profiling and clinical information sourced from TCGA and GTEx databases. Finally, we established a prognostic model integrating 9 genes associated with lipid metabolism and generated a nomogram model using R. GO and KEGG were performed to explore the functions and pathways of lipid metabolism-related genes.

Results: Our findings revealed that patients with cervical cancer exhibited dyslipidemia, characterized by elevated levels of TC, TG, and LDL-C, alongside reduced HDL-C levels compared to controls (P<0.05). Interestingly, compared with early-stage patients, advanced patients had lower HDL-C level and higher LDL-C level. Regression analysis further highlighted high TC, TG, and LDL-C as significant risk factors for cervical cancer. Then a total of 188 lipid metabolism-related genes were identified and a prognostic signature based on 9 genes was established and validated. The results of the GO and KEGG functional analysis indicated that the lipid metabolism-related genes are primarily concentrated on pathways associated with fatty acid metabolism.

Conclusion: Our study underscores the varying degrees of dyslipidemia observed in patients with cervical cancer, emphasizing the relevance of serum lipids in disease development. Our prognostic riskScore model predicted the overall survival time of patients based on 9 genes associated with lipid metabolism. These 9 genes may be tumor biomarkers and new targets for the treatment of cervical cancer.

Keywords: cervical cancer; genes; lipid metabolism; prognostic model; serum lipid profile.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flowchart and Venn diagrams. (A) Study flowchart (B) Blue represents the DEGs in CC; Yellow represents LMRGs; the intermediate intersection section is the LMR-DEGs.
Figure 2
Figure 2
Functional enrichment analysis of expression profile data of patients. (A) GO annotation. (B) The KEGG pathway analysis.
Figure 3
Figure 3
Screening of LMR-DEGs with prognostic significance in training set. (A) Trajectories of variable coefficients in LASSO regression. (B) LASSO regression cross-validation parameter selection process. (C) Forest plot of 9 LMR-DEGs by Multivariate Cox regression analysis.
Figure 4
Figure 4
LMR-DEGs-based risk models have favorable predictive ability in the training and testing sets. Kaplan-Meier analysis in training set (A) and testing set (C). ROC analysis in training set (B) and testing set (D), AUCs were assessed for 1-, 3-, 5-year survival.
Figure 5
Figure 5
Survival analysis of total CC patients. (A) Kaplan-Meier analysis in total patients; (B) ROC curve analysis in total patients, AUCs were assessed for 1-, 3-, 5-year survival; (C, D) The description of risk scores, patient status and survival time.
Figure 6
Figure 6
Independent prognostic analysis and construction of the nomogram. Univariate (A) and Multivariate (B) Cox analysis suggested that the riskScore and FIGO stage were independent risk factors for predicting prognosis. (C) A nomogram for estimating the prognosis of patients with CC. (D) Correction chart for 1-, 3-, 5-year predicted survival.
Figure 7
Figure 7
Expression of 9 LMR-DEGs at the protein level.
Figure 8
Figure 8
Survival analysis of 9 LMR-DEGs. (A-I) representing MSMO1, NCOR2, PLIN2, PTGDS, TNF, GLTP, RARRES3, SPTSSA, CEBPA.
Figure 9
Figure 9
The heatmap reflecting 9 LMR-DEGs expression and clinicopathologic characters associated with CC in high- and low-risk CC patients. **P<0.01.
Figure 10
Figure 10
Functional enrichment analysis of 9 LMR-DEGs. (A) GO annotation. (B) The KEGG pathway analysis.
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