Characteristics of Fatty Acid Metabolism in Lung Adenocarcinoma to Guide Clinical Treatment

Huang, Dejing; Tang, Enyu; Zhang, Tianze; Xu, Guangquan

doi:10.3389/fimmu.2022.916284

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…These genes also have the potential to act as signals and receptors that can influence cancer development and progression. The principle of immunotherapy is to inhibit cancer development by activating the natural immune molecular components of the tumour microenvironment 29 . Eosinophils indirectly generate type 1 T cell responses through the GM-CSF-IRF5 signalling axis, which has anti-tumour properties 30 .…”

Section: Discussionmentioning

confidence: 99%

Construction and validation of a prognostic model for colon adenocarcinoma based on bile acid metabolism-related genes

Luo

Zhou²,

Chang

et al. 2023

Sci Rep

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Colon adenocarcinoma (COAD), one of the common clinical cancers, exhibits high morbidity and mortality, and its pathogenesis and treatment are still underdeveloped. Numerous studies have demonstrated the involvement of bile acids in tumour development, while the potential role of their metabolism in the tumor microenvironment (TME) has not been explored. A collection of 481 genes related to bile acid metabolism were obtained, and The Cancer Genome Atlas-based COAD risk model was developed using the least absolute shrinkage selection operator (LASSO) regression analysis. The Gene Expression Omnibus dataset was used to validate the results. The predictive performance of the model was verified using column line plots, principal component analysis and receiver operating characteristic curves. Then, we analysed the differences between the high- and low-risk groups from training set based on clinical characteristics, immune cell infiltration, immune-related functions, chemotherapeutic drug sensitivity and immunotherapy efficacy. Additionally, we constructed a protein–protein interaction network to screen for target genes, which were further investigated in terms of differential immune cell distribution. A total of 234 bile acids-related differentially expressed genes were obtained between normal and tumour colon tissues. Among them, 111 genes were upregulated and 123 genes were down-regulated in the tumour samples. Relying on the LASSO logistic regression algorithm, we constructed a model of bile acid risk score, comprising 12 genes: CPT2, SLCO1A2, CD36, ACOX1, CDKN2A, HADH, GABRD, LEP, TIMP1, MAT1A, SLC6A15 and PPARGC1A. This model was validated in the GEO-COAD set. Age and risk score were observed to be independent prognostic factors in patients with COAD. Genes related to bile acid metabolism in COAD were closely related to bile secretion, intestinal transport, steroid and fatty acid metabolism. Furthermore, the high-risk group was more sensitive to Oxaliplatin than the low-risk group. Finally, the three target genes screened were closely associated with immune cells. We identified a set of 12 genes (CPT2, SLCO1A2, CD36, ACOX1, CDKN2A, HADH, GABRD, LEP, TIMP1, MAT1A, SLC6A15, and PPARGC1A) associated with bile acid metabolism and developed a bile acid risk score model using LASSO regression analysis. The model demonstrated good predictive performance and was validated using an independent dataset. Our findings revealed that the bile acid risk score were independent prognostic factors in COAD patients.

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Section: Discussionmentioning

confidence: 99%

Construction and validation of a prognostic model for colon adenocarcinoma based on bile acid metabolism-related genes

Luo

Zhou²,

Chang

et al. 2023

Sci Rep

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“…According to the previous studies, 15 , 20 we obtained 309 FAMGs to construct this analysis (Appendix S1 ). The differentially expressed fatty acid metabolism‐related genes (DEFAMGs) between normal and HNSCC samples were identified according to the criteria of |logFC| >0.585 and false discovery rate <0.05.…”

Section: Methodsmentioning

confidence: 99%

“…According to the previous studies, 15,20 All subjects were divided into high-and low-risk groups according to the median risk scores after each sample's risk score was calculated with the formula. To confirm the value of the above risk score model to predict patient prognosis, the log-rank test and Kaplan-Meier survival curves were used to compare the overall survival (OS) of the patients in both groups.…”

Section: Construction and Validation Of The Prognostic Modelmentioning

confidence: 99%

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

Yuan

Huang

Teh

et al. 2022

Clinical Laboratory Analysis

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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.

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“…Biomedical research has led to demands for tools and strategies so that information can be obtained from genome-wide expression data. Several studies have used statistical methods to analyze clinical data to find differences and correlations between LUAD and characteristic genes (22)(23)(24). Furthermore, classification systems are built through multiple machine learning and computational procedures (25).…”

Section: Introductionmentioning

confidence: 99%

Construction of a multiple-class classifier based on mRNAs and lncRNA FAM66A and PSORS1C3 for predicting distant metastasis in lung adenocarcinoma

Lin¹,

Zhi²,

Zheng³

et al. 2022

Ann Transl Med

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Background: There are several mechanisms believed to be essential for the development of distant metastasis in lung adenocarcinoma (LUAD), but the prediction of distant metastasis is still a challenge. The purpose of the present study was to examine the specific changes in RNA expression, including long noncoding RNAs (lncRNAs) in distant metastasis patients.Methods: We compared differentially expressed genes involved in distant metastasis from otherwise nonmetastasis and healthy adults using a gene expression profile. We first ranked gene sets (or gene signatures) that identify each class. An advanced multiple-class classifier was built based on the gene sets. Our classifier consisted of 282 genes and could predict cancer and distant metastasis with error rates of approximately 0.01 and 0.2, respectively. Then, gene networks were built to undermine gene relations to each class.Results: Cytochrome P450 family 4 subfamily F member 12 (CYP4F12) was the first gene in the ranking of the distant metastasis case. Down syndrome cell adhesion molecule (DSCAM) was the top gene in the rank list of the non-metastasis case. Solute carrier family 6 member 4 (SLC6A4) was associated with normal tissues. LncRNA family with sequence similarity 66 member A (FAM66A) and lncRNA PSORS1C3 were found to be associated with tumor metastasis.Conclusions: Our classifier could successfully predict distant metastasis in LUAD patients. LncRNA FAM66A and lncRNA PSORS1C3 in our model could play a role in cancer development.

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Characteristics of Fatty Acid Metabolism in Lung Adenocarcinoma to Guide Clinical Treatment

Cited by 6 publications

References 32 publications

Construction and validation of a prognostic model for colon adenocarcinoma based on bile acid metabolism-related genes

Construction and validation of a prognostic model for colon adenocarcinoma based on bile acid metabolism-related genes

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

Construction of a multiple-class classifier based on mRNAs and lncRNA FAM66A and PSORS1C3 for predicting distant metastasis in lung adenocarcinoma

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