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BMC Cancer
Published in: BMC Cancer 1/2024

Open Access 01-12-2024 | Esophageal Cancer | Research

Metabolic subtypes and immune landscapes in esophageal squamous cell carcinoma: prognostic implications and potential for personalized therapies

Authors: Xiao-wan Yu, Pei-wei She, Fang-chuan Chen, Ya-yu Chen, Shuang Zhou, Xi-min Wang, Xiao-rong Lin, Qiao-ling Liu, Zhi-jun Huang, Yu Qiu

Published in: BMC Cancer | Issue 1/2024

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Abstract

Background

This study aimed to identify metabolic subtypes in ESCA, explore their relationship with immune landscapes, and establish a metabolic index for accurate prognosis assessment.

Methods

Clinical, SNP, and RNA-seq data were collected from 80 ESCA patients from the TCGA database and RNA-seq data from the GSE19417 dataset. Metabolic genes associated with overall survival (OS) and progression-free survival (PFS) were selected, and k-means clustering was performed. Immune-related pathways, immune infiltration, and response to immunotherapy were predicted using bioinformatic algorithms. Weighted gene co-expression network analysis (WGCNA) was conducted to identify metabolic genes associated with co-expression modules. Lastly, cell culture and functional analysis were performed using patient tissue samples and ESCA cell lines to verify the identified genes and their roles.

Results

Molecular subtypes were identified based on the expression profiles of metabolic genes, and univariate survival analysis revealed 163 metabolic genes associated with ESCA prognosis. Consensus clustering analysis classified ESCA samples into three distinct subtypes, with MC1 showing the poorest prognosis and MC3 having the best prognosis. The subtypes also exhibited significant differences in immune cell infiltration, with MC3 showing the highest scores. Additionally, the MC3 subtype demonstrated the poorest response to immunotherapy, while the MC1 subtype was the most sensitive. WGCNA analysis identified gene modules associated with the metabolic index, with SLC5A1, NT5DC4, and MTHFD2 emerging as prognostic markers. Gene and protein expression analysis validated the upregulation of MTHFD2 in ESCA. MTHFD2 promotes the progression of ESCA and may be a potential therapeutic target for ESCA.

Conclusion

The established metabolic index and identified metabolic genes offer potential for prognostic assessment and personalized therapeutic interventions for ESCA, underscoring the importance of targeting metabolism-immune interactions in ESCA. MTHFD2 promotes the progression of ESCA and may be a potential therapeutic target for ESCA.
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Literature
1.
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.PubMedCrossRef
2.
3.
Fatehi Hassanabad A, Chehade R, Breadner D, Raphael J. Esophageal carcinoma: Towards targeted therapies. Cell Oncol (Dordr). 2020;43(2):195–209.PubMedCrossRef
4.
Shah MA, Kennedy EB, Catenacci DV, Deighton DC, Goodman KA, Malhotra NK, et al. Treatment of locally advanced esophageal carcinoma: ASCO guideline. J Clin Oncol. 2020;38(23):2677–94.PubMedCrossRef
5.
Song Y, Li L, Ou Y, Gao Z, Li E, Li X, et al. Identification of genomic alterations in oesophageal squamous cell cancer. Nature. 2014;509(7498):91–5.ADSPubMedCrossRef
6.
Inada M, Nishimura Y, Ishikawa K, Nakamatsu K, Wada Y, Uehara T, et al. Comparing the 7th and 8th editions of the American Joint Committee on Cancer/Union for International Cancer Control TNM staging system for esophageal squamous cell carcinoma treated by definitive radiotherapy. Esophagus. 2019;16(4):371–6.PubMedCrossRef
7.
Weiser MR, Gonen M, Chou JF, Kattan MW, Schrag D. Predicting survival after curative colectomy for cancer: individualizing colon cancer staging. J Clin Oncol. 2011;29(36):4796–802.PubMedPubMedCentralCrossRef
8.
Punt CJ, Koopman M, Vermeulen L. From tumour heterogeneity to advances in precision treatment of colorectal cancer. Nat Rev Clin Oncol. 2017;14(4):235–46.PubMedCrossRef
9.
Minnier J, Pennock ND, Guo Q, Schedin P, Harrington CA. RNA-Seq and expression arrays: selection guidelines for genome-wide expression profiling. Methods Mol Biol. 2018;1783:7–33.PubMedCrossRef
10.
Cao S, Wang JR, Ji S, Yang P, Dai Y, Guo S, et al. Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression. Nat Biotechnol. 2022;40(11):1624–33.PubMedPubMedCentralCrossRef
11.
Zhang X, Wu H, Niu J, Hu Y, Zhang W, Chang J, et al. A novel mitochondria-related gene signature in esophageal carcinoma: prognostic, immune, and therapeutic features. Funct Integr Genomics. 2023;23(2):109.PubMedCrossRef
12.
Xue W, Zheng Y, Shen Z, Li L, Fan Z, Wang W, et al. Involvement of long non-coding RNAs in the progression of esophageal cancer. Cancer Commun (Lond). 2021;41(5):371–88.PubMedCrossRef
13.
Zachariadis V, Cheng H, Andrews N, Enge M. A highly scalable method for joint whole-genome sequencing and gene-expression profiling of single cells. Mol Cell. 2020;80(3):541–53 (e5).PubMedCrossRef
14.
15.
Sniegowski T, Korac K, Bhutia YD, Ganapathy V. SLC6A14 and SLC38A5 drive the glutaminolysis and serine-Glycine-One-Carbon pathways in cancer. Pharmaceuticals (Basel). 2021;14(3):216.PubMedCrossRef
16.
Xu X, Li J, Sun X, Guo Y, Chu D, Wei L, et al. Tumor suppressor NDRG2 inhibits glycolysis and glutaminolysis in colorectal cancer cells by repressing c-Myc expression. Oncotarget. 2015;6(28):26161–76.PubMedPubMedCentralCrossRef
17.
GongSun X, Zhao Y, Jiang B, Xin Z, Shi M, Song L, et al. Inhibition of MUC1-C regulates metabolism by AKT pathway in esophageal squamous cell carcinoma. J Cell Physiol. 2019;234(7):12019–28.PubMedCrossRef
18.
He Y, Hua R, Li B, Gu H, Sun Y, Li Z. Loss of FBP1 promotes proliferation, migration, and invasion by regulating fatty acid metabolism in esophageal squamous cell carcinoma. Aging. 2020;13(4):4986–98.PubMedPubMedCentralCrossRef
19.
Zhang X, Lan L, Niu L, Lu J, Li C, Guo M, et al. Oxidative stress regulates cellular bioenergetics in esophageal squamous cell carcinoma cell. Biosci Rep. 2017;37(6):BSR20171006.PubMedPubMedCentralCrossRef
20.
Liu Y, Wang L, Liu H, Li C, He J. The prognostic significance of metabolic syndrome and a related six-lncRNA signature in esophageal squamous cell carcinoma. Front Oncol. 2020;10: 61.PubMedPubMedCentralCrossRef
21.
Sako A, Kitayama J, Kaisaki S, Nagawa H. Hyperlipidemia is a risk factor for lymphatic metastasis in superficial esophageal carcinoma. Cancer Lett. 2004;208(1):43–9.PubMedCrossRef
22.
Wen YS, Huang C, Zhang X, Qin R, Lin P, Rong T, et al. Impact of metabolic syndrome on the survival of Chinese patients with resectable esophageal squamous cell carcinoma. Dis Esophagus. 2016;29(6):607–13.PubMedCrossRef
23.
Domblides C, Lartigue L, Faustin B. Metabolic stress in the immune function of T cells, macrophages and dendritic cells. Cells. 2018;7:7–68.CrossRef
24.
25.
Kareva I. Metabolism and gut microbiota in cancer immunoediting, CD8/treg ratios, immune cell homeostasis, and cancer (Immuno)therapy: concise review. Stem Cells. 2019;37(10):1273–80.PubMedCrossRef
26.
Dugnani E, Pasquale V, Bordignon C, Canu A, Piemonti L, Monti P. Integrating T cell metabolism in cancer immunotherapy. Cancer Lett. 2017;411:12–8.PubMedCrossRef
27.
Peters CJ, Rees JR, Hardwick RH, Hardwick JS, Vowler SL, Ong CA, et al. A 4-gene signature predicts survival of patients with resected adenocarcinoma of the esophagus, junction, and gastric cardia. Gastroenterology. 2010;139(6):1995–2004 (e15).PubMedCrossRef
28.
Liberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739–40.PubMedPubMedCentralCrossRef
29.
Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics. 2010;26(12):1572–3.PubMedPubMedCentralCrossRef
30.
Yoshihara K, Shahmoradgoli M, Martinez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612.ADSPubMedCrossRef
31.
32.
Racle J, Gfeller D. EPIC: a tool to estimate the proportions of different cell types from bulk gene expression data. Methods Mol Biol. 2020;2120:233–48.PubMedCrossRef
33.
Dienstmann R, Villacampa G, Sveen A, Mason MJ, Niedzwiecki D, Nesbakken A, et al. Relative contribution of clinicopathological variables, genomic markers, transcriptomic subtyping and microenvironment features for outcome prediction in stage II/III colorectal cancer. Ann Oncol. 2019;30(10):1622–9.PubMedPubMedCentralCrossRef
34.
35.
36.
37.
Zheng W, Chen C, Yu J, Jin C, Han T. An energy metabolism-based eight-gene signature correlates with the clinical outcome of esophagus carcinoma. BMC Cancer. 2021;21(1):345.PubMedPubMedCentralCrossRef
38.
de Andrade Barreto E, de Souza Santos PT, Bergmann A, de Oliveira IM, Wernersbach Pinto L, Blanco T, et al. Alterations in glucose metabolism proteins responsible for the Warburg effect in esophageal squamous cell carcinoma. Exp Mol Pathol. 2016;101(1):66–73.PubMedCrossRef
39.
Patel CH, Leone RD, Horton MR, Powell JD. Targeting metabolism to regulate immune responses in autoimmunity and cancer. Nat Rev Drug Discov. 2019;18(9):669–88.PubMedCrossRef
40.
Biswas SK. Metabolic reprogramming of immune cells in cancer progression. Immunity. 2015;43(3):435–49.PubMedCrossRef
41.
Sinkala M, Mulder N, Patrick Martin D. Metabolic gene alterations impact the clinical aggressiveness and drug responses of 32 human cancers. Commun Biol. 2019;2:414.PubMedPubMedCentralCrossRef
42.
Turley SJ, Cremasco V, Astarita JL. Immunological hallmarks of stromal cells in the tumour microenvironment. Nat Rev Immunol. 2015;15(11):669–82.PubMedCrossRef
43.
Zeng Z, Li J, Zhang J, Li Y, Liu X, Chen J, et al. Immune and stromal scoring system associated with tumor microenvironment and prognosis: a gene-based multi-cancer analysis. J Transl Med. 2021;19(1):330.PubMedPubMedCentralCrossRef
44.
Koepsell H. The Na(+)-D-glucose cotransporters SGLT1 and SGLT2 are targets for the treatment of diabetes and cancer. Pharmacol Ther. 2017;170:148–65.PubMedCrossRef
45.
Pallmann N, Deng K, Livgard M, Tesikova M, Jin Y, Frengen NS, et al. Stress-mediated reprogramming of prostate cancer one-carbon cycle drives disease progression. Cancer Res. 2021;81(15):4066–78.PubMedCrossRef
46.
Li G, Wu J, Li L, Jiang P. p53 deficiency induces MTHFD2 transcription to promote cell proliferation and restrain DNA damage. Proc Natl Acad Sci U S A. 2021;118:28.
47.
Shang M, Yang H, Yang R, Chen T, Fu Y, Li Y, et al. The folate cycle enzyme MTHFD2 induces cancer immune evasion through PD-L1 up-regulation. Nat Commun. 2021;12(1):1940.ADSPubMedPubMedCentralCrossRef
48.
Cheng WC, Chang CY, Lo CC, Hsieh CY, Kuo TT, Tseng GC, et al. Identification of theranostic factors for patients developing metastasis after surgery for early-stage lung adenocarcinoma. Theranostics. 2021;11(8):3661–75.PubMedPubMedCentralCrossRef
49.
Wang J, Luo J, Sun Z, Sun F, Kong Z, Yu J. Identification of MTHFD2 as a novel prognosis biomarker in esophageal carcinoma patients based on transcriptomic data and methylation profiling. Med (Baltim). 2020;99(37): e22194.CrossRef
50.
51.
Moiso E, Provero P. Cancer metabolic subtypes and their association with molecular and clinical features. Cancers (Basel). 2022;14(9):2145.PubMedCrossRef
52.
Wolpaw AJ, Dang CV. Exploiting metabolic vulnerabilities of cancer with precision and accuracy. Trends Cell Biol. 2018;28(3):201–12.PubMedCrossRef
Metadata
Title
Metabolic subtypes and immune landscapes in esophageal squamous cell carcinoma: prognostic implications and potential for personalized therapies
Authors
Xiao-wan Yu
Pei-wei She
Fang-chuan Chen
Ya-yu Chen
Shuang Zhou
Xi-min Wang
Xiao-rong Lin
Qiao-ling Liu
Zhi-jun Huang
Yu Qiu
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2024
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-024-11890-x

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