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The correlation between tuberculosis pathway genes and the prognosis and immune microenvironment of lung squamous cell carcinoma

Published on Oct. 07, 2023Total Views: 1361 times Total Downloads: 421 times Download Mobile

Author: Ni-Li JIANG 1 Die ZHANG 2 Zeng-Jing LIU 3 Yan-Ling HU 1, 2, 3

Affiliation: 1. Life Science Institutes, Guangxi Medical University, Nanning 530022, China 2. Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-constructed by the Province and Ministry, Guangxi Medical University, Nanning 530021, China 3. School of Information and Management, Guangxi Medical University, Nanning 530022, China

Keywords: Tuberculosis Lung squamous cell carcinoma Gene Immune TCGA Prognosis Nomogram

DOI: 10.12173/j.issn.1004-4337.202304112

Reference: Jiang NL, Zhang D, Liu ZJ, Hu YL. The correlation between tuberculosis pathway genes and the prognosis and immune microenvironment of lung squamous cell carcinoma[J]. Journal of Mathematical Medicine, 2023, 36(9): 641-649. DOI: 10.12173/j.issn.1004-4337.202304112[Article in Chinese]

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Abstract

Objective  To explore the relationship between differential expression of genes related to the tuberculosis pathway and the prognosis and tumor immune microenvironment of lung squamous cell carcinoma (LUSC), and identify predictive genes and immune factors for tuberculosis combined with LUSC, and to find new therapeutic targets.

Methods  The pathway (map05152) genes related to the tuberculosis were obtained based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The gene expression profiles of 374 LUSC patients from The Cancer Genome Atlas (TCGA) database were enrolled and normalized. The COX proportional hazards model was constructed and validated by receiver operating characteristic (ROC) curve. The Nomogram was used to analyze the prognosis of LUSC patients in different risk subgroups. Finally, the differences between tumor microenvironment and single sample immune infiltration enrichment caused by tuberculosis pathway genes in different risk subgroups were analyzed.

Results  The CASP9, FADD, PLK3 genes were correlated with the prognosis of LUSC. Patients with high risk scores were more likely to experience differences in immune cell infiltration and enrichment, especially, these important immune cells: central memory CD4 T, effector memory CD4 T, gamma delta T, natural killer, and natural killer T presented a low enrichment state.

Conclusion  CASP9, FADD and PLK3 genes can be used to evaluate the prognosis of LUSC patients and identify the changes in tumor immune microenvironment based on risk scores. Potential therapeutic targets could be identified for LUSC and tuberculosis patients by targeting immune cells with enriched differences in high risk group.

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1.Islami F, Goding Sauer A, Miller KD, et al. Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States[J]. CA Cancer J Clin, 2018, 68(1): 31-54. DOI: 10.3322/caac.21440.

2.Siegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2021[J]. CA Cancer J Clin, 2021, 71(1): 7-33. DOI: 10.3322/caac.21654.

3.National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening[J]. N Engl J Med, 2011, 365(5): 395-409. DOI: 10.1056/NEJMoa1102873.

4.de Koning HJ, van der Aalst CM, de Jong PA, et al. Reduced lung-cancer mortality with volume ct screening in a randomized trial[J]. N Engl J Med, 2020, 382(6): 503-513. DOI: 10.1056/NEJMoa1911793.

5.Furin J, Cox H, Pai M. Tuberculosis[J]. Lancet, 2019, 393(10181): 1642-1656. DOI: 10.1016/s0140-6736(19)30308-3.

6.Thandra KC, Barsouk A, Saginala K, et al. Epidemiology of lung cancer[J]. Contemp Oncol (Pozn), 2021, 25(1): 45-52. DOI: 10.5114/wo.2021.103829.

7.Ang L, Ghosh P, Seow WJ. Association between previous lung diseases and lung cancer risk: a systematic review and meta-analysis[J]. Carcinogenesis, 2021, 42(12): 1461-1474. DOI: 10.1093/carcin/bgab082.

8.Abudureheman M, Simayi R, Aimuroula H, et al. Association of mycobacterium tuberculosis L-formmpb64 gene and lung cancer[J]. Eur Rev Med Pharmacol Sci, 2019, 23(1): 113-120. DOI: 10.26355/eurrev_201901_16755.

9.魏静, 陶媛美慧, 付英梅, 等.肺结核与肺癌相互影响的研究进展[J]. 实用肿瘤学杂志, 2018, 32(4): 340-343. [Wei J, Tao YMH, Fu YM, et al. Research progress in the interaction between pulmonary tuberculosis and lung cancer[J]. Practical Oncology Journal, 2018, 32(4): 340-343.] DOI: 10.11904 /j.issn.1002-3070.2018.04.011.

10.Sharma N, Shariq M, Quadir N, et al. Mycobacterium

tuberculosis protein PE6 (Rv0335c), a novel TLR4 agonist, evokes an inflammatory response and modulates the cell death pathways in macrophages to enhance intracellular survival[J]. Front Immunol, 2021, 12: 696491. DOI: 10.3389/fimmu.2021.696491.

11.Berry MP, Graham CM, McNab FW, et al. An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis[J]. Nature, 2010, 466(7309): 973-977. DOI: 10.1038/nature09247.

12.Leu JS, Chen ML, Chang SY, et al. SP110b controls host immunity and susceptibility to tuberculosis[J]. Am J Respir Crit Care Med, 2017, 195(3): 369-382. DOI: 10.1164/rccm.201601-0103OC.

13.Hernández Del Pino RE, Pellegrini JM, Rovetta AI, et al. Restimulation-induced T-cell death through NTB-A/SAP signaling pathway is impaired in tuberculosis patients with depressed immune responses[J]. Immunol Cell Biol, 2017, 95(8): 716-728. DOI: 10.1038/icb.2017.42.

14.He Q, Yang J, Jin Y. Immune infiltration and clinical significance analyses of the coagulation-related genes in hepatocellular carcinoma[J]. Brief Bioinform, 2022, 23(4): bbac291. DOI: 10.1093/bib/bbac291.

15.Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge[J]. Contemp Oncol (Pozn), 2015, 19(1A): A68-A77. DOI: 10.5114/wo.2014.47136.

16.Zhao Y, Li MC, Konaté MM, et al. TPM, FPKM, or normalized counts? A comparative study of quantification measures for the analysis of RNA-seq data from the NCI patient-derived models repository[J]. J Transl Med, 2021, 19(1): 269. DOI: 10.1186/s12967-021-02936-w.

17.Lee SY, Choi YY, Choi JE, et al. Polymorphisms in the caspase genes and the risk of lung cancer[J]. J Thorac Oncol, 2010, 5(8): 1152-1158. DOI: 10.1097/JTO.0b013e3181e04543.

18.Zhang ZY, Xuan Y, Jin XY, et al. A literature-based systematic HuGE review and meta-analysis show that CASP gene family polymorphisms are associated with risk of lung cancer[J]. Genet Mol Res, 2013, 12(3): 3057-3069. DOI: 10.4238/2013.January.4.22.

19.Allavena G, Cuomo F, Baumgartner G, et al. Suppressed translation as a mechanism of initiation of CASP8 (caspase 8)-dependent apoptosis in autophagy-deficient NSCLC cells under nutrient limitation[J]. Autophagy, 2018, 14(2): 252-268. DOI: 10.1080/15548627.2017.1405192.

20.Liu Y, Li X, Zhou X, et al. FADD as a key molecular player in cancer progression[J]. Mol Med, 2022, 28(1): 132. DOI: 10.1186/s10020-022-00560-y.

21.Mouasni S, Tourneur L. FADD at the crossroads between cancer and inflammation[J]. Trends Immunol, 2018, 39(12): 1036-1053. DOI: 10.1016/j.it.2018.10.005.

22.Deng S, Lu X, Zhang Z, et al. Identification and assessment of PLK1/2/3/4 in lung adenocarcinoma and lung squamous cell carcinoma: evidence from methylation profile[J]. J Cell Mol Med, 2021, 25(14): 6652-6663. DOI: 10.1111/jcmm.16668.

23.Vaughan CA, Singh S, Subler MA, et al. The oncogenicity of tumor-derived mutant p53 is enhanced by the recruitment of PLK3[J]. Nat Commun, 2021, 12(1): 704. DOI: 10.1038/s41467-021-20928-8.

24.崔逸爽, 刘雪静, 洪紫谦, 等. 基于生物信息学技术分析肺鳞癌预后风险模型及中药预测研究[J]. 中华中医药学刊: 1-20. [Cui YS, Liu XJ, Hong ZQ, et al. Identification of a risk model for predicting the prognosis of lung squamous cell carcinoma based on TCGA and GEO databases[J]. Chinese Archives of Traditional Chinese Medicine: 1-20.] https://kns.cnki.net/kcms/detail/21.1546.R.20230517.1353.002.html.

25.亢春彦, 王丹丹, 王风翔, 等. 基于TCGA数据库下吸烟史肺鳞癌患者DNA甲基化谱的生物信息学分析[J]. 中国老年学杂志, 2023, 43(13): 3118-3122. [Kang CY, Wang DD, Wang FX, et al. Bioinformatics analysis of DNA methylation profiles in lung squamous cell carcinoma patients with smoking history based on the TCGA database[J]. Chinese Journal of Gerontology, 2023, 43(13): 3118-3122.] DOI: 10 3969/j.Issn 1005.9202.2023.13.012.

26.Poli A, Michel T, Thérésine M, et al. CD56bright natural killer (NK) cells: an important NK cell subset[J]. Immunology, 2009, 126(4): 458-465. DOI: 10.1111/j.1365-2567.2008.03027.x.

27.许文, 陈威巍. CD56bright自然杀伤细胞亚群在人免疫缺陷病毒/丙型肝炎病毒共感染中的研究进展[J]. 医学研究生学报, 2012, 25(1): 103-106. [Xu W, Chen WW. Role of CD56bright natural killer cells in HIV/HCV coinfection[J]. Journal of Medical Postgraduates, 2012, 25(1): 103-106.] DOI: 10.3969/j.issn.1008-8199.2012.01.026.

28.Narni-Mancinelli E, Vivier E. Advancing natural killer therapies against cancer[J]. Cell, 2022, 185(9): 1451-1454. DOI: 10.1016/j.cell.2022.04.006.

29.Ugel S, De Sanctis F, Mandruzzato S, et al. Tumor-induced myeloid deviation: when myeloid-derived suppressor cells meet tumor-associated macrophages[J]. J Clin Invest, 2015, 125(9): 3365-3376. DOI: 10.1172/JCI80006.

30.Zhang S, Ma X, Zhu C, et al. The role of myeloid-derived suppressor cells in patients with solid tumors: a meta-analysis[J]. PloS one, 2016, 11(10): e0164514. DOI: 10.1371/journal.pone.0164514.

31.Shinnakasu R, Kurosaki T. Regulation of memory B and plasma cell differentiation[J]. Curr Opin Immunol, 2017, 45: 126-131. DOI: 10.1016/j.coi.2017.03.003.

32.MacLean AJ, Richmond N, Koneva L, et al. Secondary influenza challenge triggers resident memory B cell migration and rapid relocation to boost antibody secretion at infected sites[J]. Immunity, 2022, 55(4): 718-733.e8. DOI: 10.1016/j.immuni.2022.03.003.

33.Goel RR, Apostolidis SA, Painter MM, et al. Distinct antibody and memory B cell responses in SARS-CoV-2 naïve and recovered individuals following mRNA vaccination[J]. Sci Immunol, 2021, 6(58): eabi6950. DOI: 10.1126/sciimmunol.abi6950.

34.Nguyen QP, Deng TZ, Witherden DA, et al. Origins of CD4+ circulating and tissue-resident memory T-cells[J]. Immunology, 2019, 157(1): 3-12. DOI: 10.1111/imm.13059.

35.Pepper M, Jenkins MK. Origins of CD4(+) effector and central memory T cells[J]. Nat Immunol, 2011, 12(6): 467-471. DOI: 10.1038/ni.2038.

36.Wu J, Zhang T, Xiong H, et al. Tumor-infiltrating CD4+ central memory T cells correlated with favorable prognosis in oral squamous cell carcinoma[J]. J Inflamm Res, 2022, 15: 141-152. DOI: 10.2147/JIR.S343432.

37.Ida S, Takahashi H, Kawabata-Iwakawa R, et al. Tissue-resident memory T cells correlate with the inflammatory tumor microenvironment and improved prognosis in head and neck squamous cell carcinoma[J]. Oral Oncol, 2021, 122: 105508. DOI: 10.1016/j.oraloncology.2021.105508.

38.Kim YH, Zhu L, Pyaram K, et al. PLZF-expressing CD4 T cells show the characteristics of terminally differentiated effector memory CD4 T cells in humans[J]. Eur J Immunol, 2018, 48(7): 1255-1257. DOI: 10.1002/eji.201747426.

39.Qi C, Wang Y, Li P, et al. Gamma delta T cells and their pathogenic role in psoriasis[J]. Front Immunol, 2021, 12: 627139. DOI: 10.3389/fimmu.2021.627139.

40.Wo J, Zhang F, Li Z, et al. The role of gamma-delta T cells in diseases of the central nervous system[J]. Front Immunol, 2020, 11: 580304. DOI: 10.3389/fimmu.2020.580304.

41.Iyoda T, Yamasaki S, Ueda S, et al. Natural killer T and natural killer cell-based immunotherapy strategies targeting cancer[J]. Biomolecules, 2023, 13(2): 348. DOI: 10.3390/biom13020348.

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