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Correlation analysis of lipid metabolism genes with the immune microenvironment in gastric cancer and the construction of a novel gene signature

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Abstract

Background

Lipid metabolism reprogramming plays an important role in cell growth, proliferation, angiogenesis and invasion of cancer. However, the prognostic value of lipid metabolism during gastric cancer (GC) progression and the relationship with the immune microenvironment are still unclear. The aim of this study was to clarify the correlation between lipid metabolism genes and GC immunity.

Method

We obtained 350 patients from The Cancer Genome Atlas (TCGA) and 355 patients from Gene Expression Omnibus (GEO) databases. Lipid metabolism-related gene datasets were obtained from the Reactome and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Molecular subtypes were obtained by Consensus clustering, and subtype immune status was analyzed using ESTIMATE, TIMER and microenvironmental cell population counter (MCP Counter) algorithm for immune analysis. Functional analyses included the application of Gene Set Enrichment Analysis (GSEA), KEGG, gene ontology (GO), and Protein–Protein Interaction Networks (PPI) to evaluate the molecular mechanisms of different subtypes. Weighted gene co-expression network analysis (WGCNA) was used to identify genes associated with immunity. The LASSO algorithm and multivariate Cox regression analysis were used to construct prognostic risk models.

Result

Based on the lipid metabolism genes found in GC, patients with GC can be divided into two subgroups with significantly different survival. The subgroup with a better prognosis presented higher immune scores and immune infiltrating cell abundance. 1170 immune-related genes were screened by WGCNA, and further screening by PPI network analysis revealed that PTPRC, CD4, ITGB2 and LCP2 were closely associated with immune cells. Combined with the TIDE score results, it was found that the population with high expression of the above genes might be more sensitive to immunotherapy. In addition, a survival prediction model for GC was developed based on five survival-related lipid metabolism genes, PIAS4, PLA2R1, PRKACA, SLCO1A2 and STARD4. The ROC analysis over time showed that the risk prediction score model had good stability.

Conclusion

Lipid metabolism gene expression is correlated with the immune microenvironment in GC patients and can accurately predict their prognosis. Studies on lipid metabolism and GC immunity can help to screen the population for immunotherapy benefits.

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Data availability

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

References

  1. Smyth EC, Nilsson M, Grabsch HI, van Grieken NC, Lordick F. Gastric cancer. Lancet. 2020;396(10251):635–48. https://doi.org/10.1016/S0140-6736(20)31288-5.

    Article  CAS  PubMed  Google Scholar 

  2. Karimi P, Islami F, Anandasabapathy S, Freedman ND, Kamangar F. Gastric cancer: descriptive epidemiology, risk factors, screening, and prevention. Cancer Epidemiol Biomark Prev. 2014;23(5):700–13. https://doi.org/10.1158/1055-9965.EPI-13-1057.

    Article  Google Scholar 

  3. Talebi A, Mohammadnejad A, Akbari A, Pourhoseingholi MA, Doosti H, Moghimi-Dehkordi B, et al. Survival analysis in gastric cancer: a multi-center study among Iranian patients. Bmc Surg. 2020;20(1):152. https://doi.org/10.1186/s12893-020-00816-6.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Feng RM, Zong YN, Cao SM, Xu RH. Current cancer situation in China: good or bad news from the 2018 global cancer statistics? Cancer Commun (Lond). 2019;39(1):22. https://doi.org/10.1186/s40880-019-0368-6.

    Article  PubMed  Google Scholar 

  5. Wagner AD, Syn NL, Moehler M, Grothe W, Yong WP, Tai BC, et al. Chemotherapy for advanced gastric cancer. Cochrane Database Syst Rev. 2017;8:D4064. https://doi.org/10.1002/14651858.CD004064.pub4.

    Article  Google Scholar 

  6. DeBerardinis RJ, Chandel NS. Fundamentals of cancer metabolism. Sci Adv. 2016;2(5): e1600200. https://doi.org/10.1126/sciadv.1600200.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lunt SY, Vander HM. Aerobic glycolysis: meeting the metabolic requirements of cell proliferation. Annu Rev Cell Dev Biol. 2011;27:441–64. https://doi.org/10.1146/annurev-cellbio-092910-154237.

    Article  CAS  PubMed  Google Scholar 

  8. Luo X, Cheng C, Tan Z, Li N, Tang M, Yang L, et al. Emerging roles of lipid metabolism in cancer metastasis. Mol Cancer. 2017;16(1):76. https://doi.org/10.1186/s12943-017-0646-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Huang C, Freter C. Lipid metabolism, apoptosis and cancer therapy. Int J Mol Sci. 2015;16(1):924–49. https://doi.org/10.3390/ijms16010924.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Merino SM, Gomez DCM, Moreno RJ, Falagan MS, Sanchez MR, Casado E, et al. Lipid metabolism and lung cancer. Crit Rev Oncol Hematol. 2017;112:31–40. https://doi.org/10.1016/j.critrevonc.2017.02.001.

    Article  Google Scholar 

  11. Liu Q, Luo Q, Halim A, Song G. Targeting lipid metabolism of cancer cells: a promising therapeutic strategy for cancer. Cancer Lett. 2017;401:39–45. https://doi.org/10.1016/j.canlet.2017.05.002.

    Article  CAS  PubMed  Google Scholar 

  12. Iannelli F, Lombardi R, Milone MR, Pucci B, De Rienzo S, Budillon A, et al. Targeting mevalonate pathway in cancer treatment: repurposing of statins. Recent Pat Anticancer Drug Discov. 2018;13(2):184–200. https://doi.org/10.2174/1574892812666171129141211.

    Article  CAS  PubMed  Google Scholar 

  13. Raud B, McGuire PJ, Jones RG, Sparwasser T, Berod L. Fatty acid metabolism in CD8(+) T cell memory: challenging current concepts. Immunol Rev. 2018;283(1):213–31. https://doi.org/10.1111/imr.12655.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Lochner M, Berod L, Sparwasser T. Fatty acid metabolism in the regulation of T cell function. Trends Immunol. 2015;36(2):81–91. https://doi.org/10.1016/j.it.2014.12.005.

    Article  CAS  PubMed  Google Scholar 

  15. Wang Y, Wang D, Yang L, Zhang Y. Metabolic reprogramming in the immunosuppression of tumor-associated macrophages. Chin Med J (Engl). 2022. https://doi.org/10.1097/CM9.0000000000002426.

    Article  PubMed  Google Scholar 

  16. LaPensee CR, Lin G, Dent AL, Schwartz J. Deficiency of the transcriptional repressor B cell lymphoma 6 (Bcl6) is accompanied by dysregulated lipid metabolism. PLoS ONE. 2014;9(6): e97090. https://doi.org/10.1371/journal.pone.0097090.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Zhang Q, Wang H, Mao C, Sun M, Dominah G, Chen L, et al. Fatty acid oxidation contributes to IL-1beta secretion in M2 macrophages and promotes macrophage-mediated tumor cell migration. Mol Immunol. 2018;94:27–35. https://doi.org/10.1016/j.molimm.2017.12.011.

    Article  CAS  PubMed  Google Scholar 

  18. Rao E, Singh P, Zhai X, Li Y, Zhu G, Zhang Y, et al. Inhibition of tumor growth by a newly-identified activator for epidermal fatty acid binding protein. Oncotarget. 2015;6(10):7815–27. https://doi.org/10.18632/oncotarget.3485.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Jin H, He Y, Zhao P, Hu Y, Tao J, Chen J, et al. Targeting lipid metabolism to overcome EMT-associated drug resistance via integrin beta3/FAK pathway and tumor-associated macrophage repolarization using legumain-activatable delivery. Theranostics. 2019;9(1):265–78. https://doi.org/10.7150/thno.27246.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Munir R, Lisec J, Swinnen JV, Zaidi N. Lipid metabolism in cancer cells under metabolic stress. Br J Cancer. 2019;120(12):1090–8. https://doi.org/10.1038/s41416-019-0451-4.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Yoon SJ, Park J, Shin Y, Choi Y, Park SW, Kang SG, et al. Deconvolution of diffuse gastric cancer and the suppression of CD34 on the BALB/c nude mice model. BMC Cancer. 2020;20(1):314. https://doi.org/10.1186/s12885-020-06814-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Yasrebi H. SurvJamda: an R package to predict patients’ survival and risk assessment using joint analysis of microarray gene expression data. Bioinformatics. 2011;27(8):1168–9. https://doi.org/10.1093/bioinformatics/btr103.

    Article  CAS  PubMed  Google Scholar 

  23. Wilkerson MD, Hayes DN. Consensus cluster plus: a class discovery tool with confidence assessments and item tracking. Bioinformatics. 2010;26(12):1572–3. https://doi.org/10.1093/bioinformatics/btq170.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Zeng D, Ye Z, Shen R, Yu G, Wu J, Xiong Y, et al. IOBR: multi-omics immuno-oncology biological research to decode tumor microenvironment and signatures. Front Immunol. 2021;12: 687975. https://doi.org/10.3389/fimmu.2021.687975.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucl Acids Res. 2020;48(W1):W509–14. https://doi.org/10.1093/nar/gkaa407.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Becht E, Giraldo NA, Lacroix L, Buttard B, Elarouci N, Petitprez F, et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genom Biol. 2016;17(1):218. https://doi.org/10.1186/s13059-016-1070-5.

    Article  CAS  Google Scholar 

  27. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7. https://doi.org/10.1089/omi.2011.0118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1–22.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 2011;12:77. https://doi.org/10.1186/1471-2105-12-77.

    Article  Google Scholar 

  30. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. Ca Cancer J Clin. 2022;72(1):7–33. https://doi.org/10.3322/caac.21708.

    Article  PubMed  Google Scholar 

  31. Fu Y, Zou T, Shen X, Nelson PJ, Li J, Wu C, et al. Lipid metabolism in cancer progression and therapeutic strategies. Medcomm (2020). 2021;2(1):27–59. https://doi.org/10.1002/mco2.27.

    Article  CAS  PubMed  Google Scholar 

  32. Wang H, Wu X, Chen Y. Stromal-immune score-based gene signature: a prognosis stratification tool in gastric cancer. Front Oncol. 2019;9:1212. https://doi.org/10.3389/fonc.2019.01212.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 2017;77(21):e108–10. https://doi.org/10.1158/0008-5472.CAN-17-0307.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102(43):15545–50. https://doi.org/10.1073/pnas.0506580102.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinform. 2013;14:7. https://doi.org/10.1186/1471-2105-14-7.

    Article  Google Scholar 

  36. Maan M, Peters JM, Dutta M, Patterson AD. Lipid metabolism and lipophagy in cancer. Biochem Biophys Res Commun. 2018;504(3):582–9. https://doi.org/10.1016/j.bbrc.2018.02.097.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Nieman KM, Kenny HA, Penicka CV, Ladanyi A, Buell-Gutbrod R, Zillhardt MR, et al. Adipocytes promote ovarian cancer metastasis and provide energy for rapid tumor growth. Nat Med. 2011;17(11):1498–503. https://doi.org/10.1038/nm.2492.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Zhang C, Liao Y, Liu P, Du Q, Liang Y, Ooi S, et al. FABP5 promotes lymph node metastasis in cervical cancer by reprogramming fatty acid metabolism. Theranostics. 2020;10(15):6561–80. https://doi.org/10.7150/thno.44868.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Chien W, Lee KL, Ding LW, Wuensche P, Kato H, Doan NB, et al. PIAS4 is an activator of hypoxia signalling via VHL suppression during growth of pancreatic cancer cells. Br J Cancer. 2013;109(7):1795–804. https://doi.org/10.1038/bjc.2013.531.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Huna A, Griveau A, Vindrieux D, Jaber S, Flaman JM, Goehrig D, et al. PLA2R1 promotes DNA damage and inhibits spontaneous tumor formation during aging. Cell Death Dis. 2021;12(2):190. https://doi.org/10.1038/s41419-021-03468-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Moody SE, Schinzel AC, Singh S, Izzo F, Strickland MR, Luo L, et al. PRKACA mediates resistance to HER2-targeted therapy in breast cancer cells and restores anti-apoptotic signaling. Oncogene. 2015;34(16):2061–71. https://doi.org/10.1038/onc.2014.153.

    Article  CAS  PubMed  Google Scholar 

  42. Zhang M, Xiang Z, Wang F, Shan R, Li L, Chen J, et al. STARD4 promotes breast cancer cell malignancy. Oncol Rep. 2020;44(6):2487–502. https://doi.org/10.3892/or.2020.7802.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Jiang Y, Zhang Q, Hu Y, Li T, Yu J, Zhao L, et al. Immuno score signature: a prognostic and predictive tool in gastric cancer. Ann Surg. 2018;267(3):504–13. https://doi.org/10.1097/SLA.0000000000002116.

    Article  PubMed  Google Scholar 

  44. Yamashita K, Iwatsuki M, Harada K, Eto K, Hiyoshi Y, Ishimoto T, et al. Prognostic impacts of the combined positive score and the tumor proportion score for programmed death ligand-1 expression by double immunohistochemical staining in patients with advanced gastric cancer. Gastric Cancer. 2020;23(1):95–104. https://doi.org/10.1007/s10120-019-00999-9.

    Article  CAS  PubMed  Google Scholar 

  45. Matsuoka T, Yashiro M. Biomarkers of gastric cancer: Current topics and future perspective. World J Gastroenterol. 2018;24(26):2818–32. https://doi.org/10.3748/wjg.v24.i26.2818.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Not applicable.

Funding

This study was supported by the National Natural Science Foundation of China (No. 82174197, 81973609, 81973782, 81704031); Natural Science Foundation of Jiangsu Province, China (No. BK20211392); Graduate Research and Innovation Projects of Jiangsu Province (SJCX22-0749, SJCX21-0692).

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Author notes

  1. Yaqi Li and Xu Chen have contributed equally to this work.

Authors and Affiliations

  1. Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu, China

    Yaqi Li, Xu Chen, Ruijuan Zhang, Menglin Chen, Junyu Shen, Jian Wu & Qingmin Sun

  2. No.1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China

    Yaqi Li, Xu Chen, Ruijuan Zhang, Menglin Chen & Junyu Shen

  3. Department of Oncology and Endocrinology, Xiangcheng Hospital of Traditional Chinese Medicine, Suzhou, 215155, China

    Jichao Yu

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  1. Yaqi Li
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Contributions

YQL, XC, RJZ, MLC, JYS, JW, JCY and QMS: jointly designed the study, and XC wrote the manuscript. JCY and QMS: supervised the study. All authors contributed to the data collection, analysis and interpretation, manuscript writing and revision. All authors read and approved the final manuscript.

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Correspondence to Jichao Yu or Qingmin Sun.

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Supplementary Information

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12094_2022_3038_MOESM1_ESM.tif

Supplementary file1 A-D: The consensus matrix heat map classified GC patients into 3 to 6 clusters: (A) K=3; (B) K=4; (C) K=5 and (D)K=6 (TIF 11061 KB)

12094_2022_3038_MOESM2_ESM.tif

Supplementary file2 (A) Heatmap shows the expression of the top 50 differentially expressed genes in the two subgroups. (B) Heatmap illustrating the results of GSVA. (C-D) A scale-first network is constructed by choosing the most appropriate soft-thresholding power (TIF 18073 KB)

12094_2022_3038_MOESM3_ESM.tif

Supplementary file3 (A-F) KEGG enrichment results of B cells (A); T_cell_CD4 (B); T_cell_CD8 (C); DCs (D); neutrophils (E) and macrophages (F). (G-L) GO enrichment results of B cells (G); T_cell_CD4 (H); T_cell_CD8 (I); DCs (J); neutrophils (K) and macrophages (L) (TIF 13415 KB)

12094_2022_3038_MOESM4_ESM.tif

Supplementary file4 (A-B) Pearson’s correlation was used to calculate the correlations of gene expression with chemokines (A), receptors (B). (C) TIDE scores for TYROBP genes (TIF 14936 KB)

12094_2022_3038_MOESM5_ESM.tif

Supplementary file5 (A-F) Survival curves of patients regrouped according to gastric adenocarcinoma patients (A), patients with adenocarcinoma of the intestine (B), age>65 (C), age<65 (D), male risk score (E) and female risk score (F). (G-J) Survival curves of patients regrouped according to stage (G), sex (H), age (I) and primary site (J) (TIF 12957 KB)

12094_2022_3038_MOESM6_ESM.tif

Supplementary file6 (A-C) One-year, three-year and five-year DCA curves of the training cohort. (D-F) One-year, three-year and five-year DCA curves of the verification cohorts (TIF 8364 KB)

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Li, Y., Chen, X., Zhang, R. et al. Correlation analysis of lipid metabolism genes with the immune microenvironment in gastric cancer and the construction of a novel gene signature. Clin Transl Oncol 25, 1315–1331 (2023). https://doi.org/10.1007/s12094-022-03038-9

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