Dataset Information

Enhancing prognostic accuracy in head and neck squamous cell carcinoma chemotherapy via a lipid metabolism-related clustered polygenic model.

ABSTRACT:

Objective

Systemic chemotherapy is the first-line therapeutic option for head and neck squamous cell carcinoma (HNSCC), but it often fails. This study aimed to develop an effective prognostic model for evaluating the therapeutic effects of systemic chemotherapy.

Methods

This study utilized CRISPR/cas9 whole gene loss-of-function library screening and data from The Cancer Genome Atlas (TCGA) HNSCC patients who have undergone systemic therapy to examine differentially expressed genes (DEGs). A lipid metabolism-related clustered polygenic model called the lipid metabolism related score (LMRS) model was established based on the identified functionally enriched DEGs. The prediction efficiency of the model for survival outcome, chemotherapy, and immunotherapy response was evaluated using HNSCC datasets, the GEO database and clinical samples.

Results

Screening results from the study demonstrated that genes those were differentially expressed were highly associated with lipid metabolism-related pathways, and patients receiving systemic therapy had significantly different prognoses based on lipid metabolism gene characteristics. The LMRS model, consisting of eight lipid metabolism-related genes, outperformed each lipid metabolism gene-based model in predicting outcome and drug response. Further validation of the LMRS model in HNSCCs confirmed its prognostic value.

Conclusion

In conclusion, the LMRS polygenic prognostic model is helpful to assess outcome and drug response for HNSCCs and could assist in the timely selection of the appropriate treatment for HNSCC patients. This study provides important insights for improving systemic chemotherapy and enhancing patient outcomes.

SUBMITTER: Miao X

PROVIDER: S-EPMC10422777 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Enhancing prognostic accuracy in head and neck squamous cell carcinoma chemotherapy via a lipid metabolism-related clustered polygenic model.

Miao Xiangwan X Wang Hao H Fan Cui C Song QianQian Q Ding Rui R Wu Jichang J Hu Haixia H Chen Kaili K Ji Peilin P Wen Qing Q Shi Minmin M Ye Bin B Fu Da D Xiang Mingliang M

Cancer cell international 20230811 1

<h4>Objective</h4>Systemic chemotherapy is the first-line therapeutic option for head and neck squamous cell carcinoma (HNSCC), but it often fails. This study aimed to develop an effective prognostic model for evaluating the therapeutic effects of systemic chemotherapy.<h4>Methods</h4>This study utilized CRISPR/cas9 whole gene loss-of-function library screening and data from The Cancer Genome Atlas (TCGA) HNSCC patients who have undergone systemic therapy to examine differentially expressed gene ...[more]

PMID: 37568192

Similar Datasets

Project description:Simple summaryClinically, aberrant lipid metabolism is responsible for overweight and/or obesity. Overweight is considered as an independent factor of cancer risk in 2019. Therefore, lipid metabolic reprogramming is an emerging hallmark of malignancy. It is an urgent need to comprehensively understand the relationship among lipid metabolism and HNSCC and identify a valuable biomarker for predicting prognosis of HNSCC patients. Three new findings were found in this study. Firstly, we identified the lipid-related differentially expressed genes (DEGs) by using the GEO microarrays and TCGA dataset. A novel lipid-related mRNA prognostic signature (LRPS, consisting of ADCY2, LIPE and OLR1) was developed, which could predict the survival and prognosis of HNSCC patients as an independent effective prognostic factor. Secondly, we found that the LRPS could indicate the type of infiltrated immune cells in HNSCC tumor microenvironment. Thirdly, we verified that the LPPS score could interpret the TP53 status of HNSCC. Our new findings indicated that LRPS has a potential to be a promising indicator of overall survival, TP53 status, and immune characteristics in HNSCC, and perhaps can monitor and guide the treatment efficacy and prognosis of HNSCC in the future.BackgroundHead and neck squamous cell carcinoma (HNSCC) is characterized by a high frequency of lymph node metastasis and a high mortality. Lipid metabolic reprogramming is an emerging carcinogen as its role in fulfilling cancer growth and spread. However, little is known about the correlation between lipid metabolism and HNSCC.Materials and methodsExpressions of lipid-related genes were obtained from the Cancer Genome Atlas (TCGA) and Gene expression Omnibus (GEO) databases for differential and functional analyses. A total number of 498 patients from TCGA with complete information were included to identify a lipid-related prognostic signature (LRPS), based on ADCY2, LIPE, and OLR1, by using univariate and multivariate Cox regression analyses. LRPS-high and LRPS-low groups were accordingly divided to pathway and cell enrichment analyses.ResultsLRS-low patients had a better overall survival and relapse - free survival than LRS-high ones in HNSCC. The LRPS-high group was significantly related to perineural invasion of cancer, cancer-related pathways, high TP53 mutation rate, high proportion of natural killer T cells (NKT), dendritic cells, monocytes, Treg, and M1 and M2 macrophage infiltration in HNSCC tumor tissues. Conversely, the LRPS-low group correlated with DNA damage-related and T-cell-regulated pathways, low frequency of mutated TP53, and high infiltration of B cells and CD4+ effector cells including Th1 and Th2.ConclusionLRPS has a potential to be a promising indicator of overall survival, prognosis, TP53 status, and immune characteristics in HNSCC.

Dataset Information

Enhancing prognostic accuracy in head and neck squamous cell carcinoma chemotherapy via a lipid metabolism-related clustered polygenic model.

Objective

Methods

Results

Conclusion

Publications

Enhancing prognostic accuracy in head and neck squamous cell carcinoma chemotherapy via a lipid metabolism-related clustered polygenic model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets