Identification of a Prognostic Gene Signature Based on Lipid Metabolism-Related Genes in Esophageal Squamous Cell Carcinoma

References

• Zhang Z, Hu X, Qiu D, Sun Y, Lei L. Development and validation of a necroptosis-related prognostic model in head and neck squamous cell carcinoma. J Oncol. 2022;2022:8402568.
   PubMed Web of Science ®Google Scholar
• Wu Z, Huang X, Cai M, et al. Novel necroptosis-related gene signature for predicting the prognosis of pancreatic adenocarcinoma. Aging. 2022;14(2):869–891. doi:10.18632/aging.203846
   PubMedGoogle Scholar
• Chen J, Wang H, Zhou L, et al. A necroptosis-related gene signature for predicting prognosis, immune landscape, and drug sensitivity in hepatocellular carcinoma. Cancer Med. 2022;11(24):5079–5096. doi:10.1002/cam4.4812
   PubMed Web of Science ®Google Scholar
• Wu J, Li L, Zhang H, et al. A risk model developed based on tumor microenvironment predicts overall survival and associates with tumor immunity of patients with lung adenocarcinoma. Oncogene. 2021;40(26):4413–4424. doi:10.1038/s41388-021-01853-y
   PubMed Web of Science ®Google Scholar
• Yang S, Cheng Y, Wang X, et al. Identification of the immune cell infiltration landscape in hepatocellular carcinoma to predict prognosis and guide immunotherapy. Front Genet. 2021;12:777931. doi:10.3389/fgene.2021.777931
   PubMed Web of Science ®Google Scholar
• Thrift AP. Global burden and epidemiology of Barrett oesophagus and oesophageal cancer. Nat Rev Gastroenterol Hepatol. 2021;18(6):432–443. doi:10.1038/s41575-021-00419-3
   PubMed Web of Science ®Google Scholar
• Yang Y-M, Hong P, Xu WW, et al. Advances in targeted therapy for esophageal cancer. Signal Transduct Target Ther. 2020;5(1):229. doi:10.1038/s41392-020-00323-3
   PubMed Web of Science ®Google Scholar
• Smyth EC, Gambardella V, Cervantes A, et al. Checkpoint inhibitors for gastroesophageal cancers: dissecting heterogeneity to better understand their role in first-line and adjuvant therapy. Ann Oncol. 2021;32(5):590–599. doi:10.1016/j.annonc.2021.02.004
   PubMed Web of Science ®Google Scholar
• Liu Z, Chen Z, Wang J, et al. Mouse avatar models of esophageal squamous cell carcinoma proved the potential for EGFR-TKI Afatinib and uncovered Src family kinases involved in acquired resistance. J Hematol Oncol. 2018;11(1):109. doi:10.1186/s13045-018-0651-z
   PubMedGoogle Scholar
• Bian X, Liu R, Meng Y, et al. Lipid metabolism and cancer. J Exp Med. 2021;218(1). doi:10.1084/jem.20201606
   Web of Science ®Google Scholar
• Zhou X, Huang F, Ma G, et al. Dysregulated ceramides metabolism by fatty acid 2-hydroxylase exposes a metabolic vulnerability to target cancer metastasis. Signal Transduct Target Ther. 2022;7(1):370. doi:10.1038/s41392-022-01199-1
   PubMed Web of Science ®Google Scholar
• Liu Y, Li C, Fang L, et al. Lipid metabolism–related lncRNA SLC25A21-AS1 promotes the progression of oesophageal squamous cell carcinoma by regulating the NPM1/c-Myc axis and SLC25A21 expression. Clin Transl Med. 2022;12(6):e944. doi:10.1002/ctm2.944
   PubMed Web of Science ®Google Scholar
• Tao M, Luo J, Gu T, et al. LPCAT1 reprogramming cholesterol metabolism promotes the progression of esophageal squamous cell carcinoma. Cell Death Dis. 2021;12(9):845. doi:10.1038/s41419-021-04132-6
   PubMed Web of Science ®Google Scholar
• Zhu Q, Huang L, Yang Q, et al. Metabolomic analysis of exosomal-markers in esophageal squamous cell carcinoma. Nanoscale. 2021;13(39):16457–16464. doi:10.1039/D1NR04015D
   PubMed Web of Science ®Google Scholar
• Yin X, Wu Q, Hao Z, Chen L. Identification of novel prognostic targets in glioblastoma using bioinformatics analysis. Biomed Eng Online. 2022;21(1):26. doi:10.1186/s12938-022-00995-8
   PubMed Web of Science ®Google Scholar
• Huang X, Liu S, Wu L, Jiang M, Hou Y. High throughput single cell RNA sequencing, bioinformatics analysis and applications. Adv Exp Med Biol. 2018;1068:33–43. doi:10.1007/978-981-13-0502-3_4
   PubMed Web of Science ®Google Scholar
• Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. doi:10.1093/nar/gkv007
   PubMed Web of Science ®Google Scholar
• Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–287. doi:10.1089/omi.2011.0118
   PubMed Web of Science ®Google Scholar
• Engebretsen S, Bohlin J. Statistical predictions with glmnet. Clin Epigenetics. 2019;11(1):123. doi:10.1186/s13148-019-0730-1
   PubMed Web of Science ®Google Scholar
• Liu TT, Li R, Huo C, et al. Identification of CDK2-related immune forecast model and ceRNA in lung adenocarcinoma, a pan-cancer analysis. Front Cell Dev Biol. 2021;9:682002. doi:10.3389/fcell.2021.682002
   PubMed Web of Science ®Google Scholar
• Sachs MC. plotROC: a tool for plotting ROC curves. J Stat Softw. 2017;79(Code Snippet 2):2. doi:10.18637/jss.v0
   PubMedGoogle Scholar
• Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinform. 2013;14(1):7. doi:10.1186/1471-2105-14-7
   PubMed Web of Science ®Google Scholar
• Zhou Y, Che Y, Fu Z, Zhang H, Wu H. Triple-negative breast cancer analysis based on metabolic gene classification and immunotherapy. Front Public Health. 2022;10:902378. doi:10.3389/fpubh.2022.902378
   PubMed Web of Science ®Google Scholar
• Geeleher P, Cox N, Huang RS, Barbour JD. pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One. 2014;9(9):e107468. doi:10.1371/journal.pone.0107468
   PubMed Web of Science ®Google Scholar
• Li Q, Li M, Zheng K, Tang S, Ma S. Expression pattern analysis and drug differential sensitivity of cancer-associated fibroblasts in triple-negative breast cancer. Transl Oncol. 2021;14(1):100891. doi:10.1016/j.tranon.2020.100891
   PubMed Web of Science ®Google Scholar
• Pan X, Jin X, Wang J, Hu Q, Dai B. Placenta inflammation is closely associated with gestational diabetes mellitus. Am J Transl Res. 2021;13(5):4068–4079.
   PubMed Web of Science ®Google Scholar
• Codipilly DC, Wang KK. Squamous Cell Carcinoma of the Esophagus. Gastroenterol Clin North Am. 2022;51(3):457–484. PMID: 36153105. doi:10.1016/j.gtc.2022.06.005
   PubMed Web of Science ®Google Scholar
• Testa U, Castelli G, Pelosi E. The molecular characterization of genetic abnormalities in esophageal squamous cell carcinoma may foster the development of targeted therapies. Curr Oncol. 2023;30(1):610–640. PMID: 36661697; PMCID: PMC9858483. doi:10.3390/curroncol30010048
   PubMed Web of Science ®Google Scholar
• Martin SA, Brash AR, Murphy RC. The discovery and early structural studies of arachidonic acid. J Lipid Res. 2016;57(7):1126–1132. doi:10.1194/jlr.R068072
   PubMed Web of Science ®Google Scholar
• Wu B, Bai C, Du Z, et al. The arachidonic acid metabolism protein-protein interaction network and its expression pattern in esophageal diseases. Am J Transl Res. 2018;10(3):907–924.
   PubMed Web of Science ®Google Scholar
• Zhi H, Zhang J, Hu G, et al. The deregulation of arachidonic acid metabolism-related genes in human esophageal squamous cell carcinoma. Int J Cancer. 2003;106(3):327–333. doi:10.1002/ijc.11225
   PubMed Web of Science ®Google Scholar
• Zhang X, Peng L, Luo Y, et al. Dissecting esophageal squamous-cell carcinoma ecosystem by single-cell transcriptomic analysis. Nat Commun. 2021;12(1):5291. doi:10.1038/s41467-021-25539-x
   PubMed Web of Science ®Google Scholar
• Zheng Y, Chen Z, Han Y, et al. Immune suppressive landscape in the human esophageal squamous cell carcinoma microenvironment. Nat Commun. 2020;11(1):6268. doi:10.1038/s41467-020-20019-0
   PubMed Web of Science ®Google Scholar
• Chen Z, Zhao M, Liang J, et al. Dissecting the single-cell transcriptome network underlying esophagus non-malignant tissues and esophageal squamous cell carcinoma. EBioMedicine. 2021;69:103459. doi:10.1016/j.ebiom.2021.103459
   PubMed Web of Science ®Google Scholar