Dataset Information

Exploration of a predictive model based on genes associated with fatty acid metabolism and clinical treatment for head and neck squamous cell carcinoma.

ABSTRACT:

Background

Head and neck squamous cell carcinoma (HNSCC) is one of the most prevalent malignant tumors of the head and neck and presents high risks of recurrence and poor prognosis postoperatively. The aim of this study was to establish a predictive model based on fatty acid metabolism (FAM) genes to forecast the prognosis of HNSCC patients and the subsequent treatment strategies.

Methods

We accessed the TCGA and GEO databases for HNSCC genes and clinical data. The FAM risk score model was created and validated using a combination of univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Combining risk scores and clinical characteristics, a nomogram was established and assessed. Subsequently, the function, gene mutation, immune difference, and chemotherapeutic drug sensitivity of the groups with high- and low-risk scores were analyzed. Consequently, the mode's validity was evaluated comprehensively by combining single gene analysis.

Results

The FAM risk score model for predicting HNSCC prognosis had certain validity. Patients in the high- and low-risk groups had genetic mutations, and the prognosis was the poorest for the high-risk groups with high genetic mutations. The patients with low-risk scores were suitable for immunotherapy since they had increased infiltration of immune cells. In contrast, the patients in the other groups were more suitable for chemotherapy.

Conclusion

The results of this study demonstrated that the FAM risk score model may predict the prognosis of HSNCC and has a certain therapeutic guidance value.

SUBMITTER: Yuan Z

PROVIDER: S-EPMC9701875 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

Exploration of a predictive model based on genes associated with fatty acid metabolism and clinical treatment for head and neck squamous cell carcinoma.

Yuan Zhechen Z Huang Juntao J Teh Bing Mei BM Hu Shiyu S Hu Yi Y Shen Yi Y

Journal of clinical laboratory analysis 20220930 11

<h4>Background</h4>Head and neck squamous cell carcinoma (HNSCC) is one of the most prevalent malignant tumors of the head and neck and presents high risks of recurrence and poor prognosis postoperatively. The aim of this study was to establish a predictive model based on fatty acid metabolism (FAM) genes to forecast the prognosis of HNSCC patients and the subsequent treatment strategies.<h4>Methods</h4>We accessed the TCGA and GEO databases for HNSCC genes and clinical data. The FAM risk score ...[more]

PMID: 36181275

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Project description:Head and neck squamous cell carcinoma (HNSCC) is the sixth most frequent cancer in humans globally. In addition to smoking and drinking, genetic and epigenetic changes also play a big role in how HNSCC starts and grows. MicroRNAs are short, non-coding RNAs that control cell differentiation and apoptosis by interfering with gene expression. In addition, microRNAs in HNSCC have been shown to affect the clinical behaviors of HNSCC in amazing ways. Moreover, metabolic reprogramming is a key part of cancer and is needed for cancer to turn into a tumor and grow. But it is still not clear what effect microRNAs related to fatty acid metabolism have on the prognosis of HNSCC patients. We downloaded the data of HNSCC patients from the TCGA database and obtained the genes associated with fatty acid metabolism according to the GSEA database. Then, the microRNAs associated with fatty acid metabolism genes were matched. Finally, fatty acid metabolism gene-associated microRNAs for calculating risk scores and then building multifactorial Cox regression models in patients with HNSCC. Heatmap analysis showed that microRNAs involved in fatty acid metabolism were significantly different in HNSCC patients than in healthy controls. A total of 27 microRNAs associated with fatty acid metabolism were screened by univariate Cox analysis (p < 0.05). Using lasso regression, 18 microRNAs substantially linked with the prognosis of HNSCC patients were identified and included in risk scores. The ROC curves demonstrate that risk scores derived from microRNAs involved in fatty acid metabolism can accurately predict the prognosis of HNSCC patients at 1, 3, and 5 years. Moreover, we discovered that 11 microRNAs included in the risk score properly distinguished the prognosis of HNSCC patients. This paper indicated that microRNAs involved with fatty acid metabolism are strongly linked to the prognosis of HNSCC patients. It also indicated that reprogramming of fatty acid metabolism in tumor tissues may play an important role in HNSCC cancer.

Project description:BackgroundGlucose metabolism (GM) plays a crucial role in cancer cell proliferation, tumor growth, and survival. However, the identification of glucose metabolism-related genes (GMRGs) for effective prediction of prognosis in head and neck squamous cell carcinoma (HNSC) is still lacking.MethodsWe conducted differential analysis between HNSC and Normal groups to identify differentially expressed genes (DEGs). Key module genes were obtained using weighted gene co-expression network analysis (WGCNA). Intersection analysis of DEGs, GMRGs, and key module genes identified GMRG-DEGs. Univariate and multivariate Cox regression analyses were performed to screen prognostic-associated genes. Independent prognostic analysis of clinical traits and risk scores was implemented using Cox regression. Gene set enrichment analysis (GSEA) was used to explore functional pathways and genes between high- and low-risk groups. Immune infiltration analysis compared immune cells between the two groups in HNSC samples. Drug prediction was performed using the Genomics of Drug Sensitivity in Cancer (GDSC) database. Quantitative real-time fluorescence PCR (qRT-PCR) validated the expression levels of prognosis-related genes in HNSC patients.ResultsWe identified 4973 DEGs between HNSC and Normal samples. Key gene modules, represented by black and brown module genes, were identified. Intersection analysis revealed 76 GMRG-DEGs. Five prognosis-related genes (MTHFD2, CDKN2A, TPM2, MPZ, and DNMT1) were identified. A nomogram incorporating age, lymph node status (N), and risk score was constructed for survival prediction in HNSC patients. Immune infiltration analysis showed significant differences in five immune cell types (Macrophages M0, memory B cells, Monocytes, Macrophages M2, and Dendritic resting cells) between the high- and low-risk groups. GDSC database analysis identified 53 drugs with remarkable differences between the groups, including A.443654 and AG.014699. DNMT1 and MTHFD2 were up-regulated, while MPZ was down-regulated in HNSC.ConclusionOur study highlights the significant association of five prognosis-related genes (MTHFD2, CDKN2A, TPM2, MPZ, and DNMT1) with HNSC. These findings provide further evidence of the crucial role of GMRGs in HNSC.

Project description:BackgroundImmune checkpoint blockade (ICB) therapies have redefined human cancer treatment, including for head and neck squamous cell carcinoma (HNSCC). However, clinical responses to various immune checkpoint inhibitors are often accompanied by immune-related adverse events (irAEs). Therefore, it is crucial to obtain a comprehensive understanding of the association between different immune tumor microenvironments (TMEs) and the immunotherapeutic response.MethodsThe research data were obtained from The Cancer Genome Atlas (TCGA) database. We applied RNA-seq genomic data from tumor biopsies to assess the immune TME in HNSCC. As the TME is a heterogeneous system that is highly associated with HNSCC progression and clinical outcome, we relied on the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm to calculate immune and stromal scores that were evaluated based on the immune or stromal components in the TME. Then, the Tumor Immune Dysfunction and Exclusion algorithm (TIDE) was used to predict the benefits of ICB to each patient. Finally, we identified specific prognostic tumor-infiltrating immune cells (TIICs) by quantifying the cellular composition of the immune response in HNSCC and its association to survival outcome, using the CIBERSORT algorithm.ResultsUtilizing the HNSCC cohort of the TCGA database and TIDE and ESTIMATE algorithm-derived immune scores, we obtained a list of microenvironment-associated lncRNAs that predicted different clinical outcomes in HNSCC patients. We validated these correlations in a different HNSCC cohort available from the TCGA database and provided insight into the prediction of response to ICB therapies in HNSCC.ConclusionsThis study confirmed that CD8+ T cells were significantly associated with better survival in HNSCC and verified that the top five significantly mutated genes (SMGs) in the TCGA HNSCC cohort were TP53, TTN, FAT1, CDKN2A, and MUC16. A high level of CD8+ T cells and high immune and stroma scores corresponded to a better survival probability in HNSCC.

Dataset Information

Exploration of a predictive model based on genes associated with fatty acid metabolism and clinical treatment for head and neck squamous cell carcinoma.

Background

Methods

Results

Conclusion

Publications

Exploration of a predictive model based on genes associated with fatty acid metabolism and clinical treatment for head and neck squamous cell carcinoma.

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