Identification of Novel Stemness-based Subtypes and Construction of a Prognostic Risk Model for Patients with Lung Squamous Cell Carcinoma

  • Authors: Shen F.1, Li F.2, Ma Y.3, Song X.1, Guo W.1
  • Affiliations:
    1. Department of Respiratory Medicine, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital,, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University
    2. Department of thoracic surgery, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital,, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University,
    3. Department of thoracic surgery, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital,, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University
  • Issue: Vol 19, No 3 (2024)
  • Pages: 400-416
  • Section: Medicine
  • URL: https://rjsocmed.com/1574-888X/article/view/645774
  • DOI: https://doi.org/10.2174/1574888X18666230714142835
  • ID: 645774

Cite item

Full Text

Abstract

Background:Although cancer stem cells (CSCs) contribute to tumorigenesis, progression, and drug resistance, stemness-based classification and prognostic signatures of lung squamous cell carcinoma (LUSC) remain unclarified. This study attempted to identify stemness-based subtypes and develop a prognostic risk model for LUSC.

Methods:Based on RNA-seq data from The Cancer Genome Atlas (TCGA), Gene-Expression Omnibus (GEO) and Progenitor Cell Biology Consortium (PCBC), mRNA expression-based stemness index (mRNAsi) was calculated by one-class logistic regression (OCLR) algorithm. A weighted gene coexpression network (WGCNA) was employed to identify stemness subtypes. Differences in mutation, clinical characteristics, immune cell infiltration, and antitumor therapy responses were determined. We constructed a prognostic risk model, followed by validations in GEO cohort, pan-cancer and immunotherapy datasets.

Results:LUSC patients with subtype C2 had a better prognosis, manifested by higher mRNAsi, higher tumor protein 53 (TP53) and Titin (TTN) mutation frequencies, lower immune scores and decreased immune checkpoints. Patients with subtype C2 were more sensitive to Imatinib, Pyrimethamine, and Paclitaxel therapy, whereas those with subtype C1 were more sensitive to Sunitinib, Saracatinib, and Dasatinib. Moreover, we constructed stemness-based signatures using seven genes (BMI1, CCDC51, CTNS, EIF1AX, FAM43A, THBD, and TRIM68) and found high-risk patients had a poorer prognosis in the TCGA cohort. Similar results were found in the GEO cohort. We verified the good performance of risk scores in prognosis prediction and therapy responses.

Conclusion:The stemness-based subtypes shed novel insights into the potential roles of LUSC-stemness in tumor heterogeneity, and our prognostic signatures offer a promising tool for prognosis prediction and guide therapeutic decisions in LUSC.

About the authors

Fangfang Shen

Department of Respiratory Medicine, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital,, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University

Email: info@benthamscience.net

Feng Li

Department of thoracic surgery, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital,, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University,

Email: info@benthamscience.net

Yong Ma

Department of thoracic surgery, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital,, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University

Email: info@benthamscience.net

Xia Song

Department of Respiratory Medicine, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital,, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University

Author for correspondence.
Email: info@benthamscience.net

Wei Guo

Department of Respiratory Medicine, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital,, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University

Author for correspondence.
Email: info@benthamscience.net

References

  1. Sung H, Ferlay J, Siegel RL, 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. doi: 10.3322/caac.21660 PMID: 33538338
  2. Xia C, Dong X, Li H, et al. Cancer statistics in China and United States, 2022: Profiles, trends, and determinants. Chin Med J 2022; 135(5): 584-90. doi: 10.1097/CM9.0000000000002108 PMID: 35143424
  3. Chen Z, Fillmore CM, Hammerman PS, Kim CF, Wong KK. Non-small-cell lung cancers: A heterogeneous set of diseases. Nat Rev Cancer 2014; 14(8): 535-46. doi: 10.1038/nrc3775 PMID: 25056707
  4. Santos ES, Rodriguez E. Treatment considerations for patients with advanced squamous cell carcinoma of the lung. Clin Lung Cancer 2022; 23(6): 457-66. doi: 10.1016/j.cllc.2022.06.002 PMID: 35872084
  5. Wang BY, Huang JY, Chen HC, et al. The comparison between adenocarcinoma and squamous cell carcinoma in lung cancer patients. J Cancer Res Clin Oncol 2020; 146(1): 43-52. doi: 10.1007/s00432-019-03079-8 PMID: 31705294
  6. Miller KD, Nogueira L, Mariotto AB, et al. Cancer treatment and survivorship statistics, 2019. CA Cancer J Clin 2019; 69(5): 363-85. doi: 10.3322/caac.21565 PMID: 31184787
  7. Liu W, Du Y, Wen R, Yang M, Xu J. Drug resistance to targeted therapeutic strategies in non-small cell lung cancer. Pharmacol Ther 2020; 206: 107438. doi: 10.1016/j.pharmthera.2019.107438 PMID: 31715289
  8. Najafi M, Farhood B, Mortezaee K. Cancer stem cells (CSCs) in cancer progression and therapy. J Cell Physiol 2019; 234(6): 8381-95. doi: 10.1002/jcp.27740 PMID: 30417375
  9. Walcher L, Kistenmacher AK, Suo H, et al. Cancer stem cells—origins and biomarkers: Perspectives for targeted personalized therapies. Front Immunol 2020; 11: 1280. doi: 10.3389/fimmu.2020.01280 PMID: 32849491
  10. Agliano A, Calvo A, Box C. Eds. The challenge of targeting cancer stem cells to halt metastasis Seminars in Cancer Biology. Elsevier 2017.
  11. Ye Z, Zheng M, Zeng Y, et al. Bioinformatics analysis reveals an association between cancer cell stemness, gene mutations, and the immune microenvironment in stomach adenocarcinoma. Front Genet 2020; 11: 595477. doi: 10.3389/fgene.2020.595477 PMID: 33362856
  12. Noureen N, Wu S, Lv Y, et al. Integrated analysis of telomerase enzymatic activity unravels an association with cancer stemness and proliferation. Nat Commun 2021; 12(1): 139. doi: 10.1038/s41467-020-20474-9 PMID: 33420056
  13. Pan S, Zhan Y, Chen X, Wu B, Liu B. Identification of biomarkers for controlling cancer stem cell characteristics in bladder cancer by network analysis of transcriptome data stemness indices. Front Oncol 2019; 9: 613. doi: 10.3389/fonc.2019.00613 PMID: 31334127
  14. Zhang C, Chen T, Li Z, et al. Depiction of tumor stemlike features and underlying relationships with hazard immune infiltrations based on large prostate cancer cohorts. Brief Bioinform 2021; 22(3): bbaa211. doi: 10.1093/bib/bbaa211 PMID: 32856039
  15. Wang W, Xu C, Ren Y, Wang S, Liao C, Fu X. A novel cancer stemness-related signature for predicting prognosis in patients with colon adenocarcinoma. Stem Cells Int 2021; 2021: 7036059. doi: 10.1155/2021/7036059
  16. Tian Y, Wang J, Qin C, et al. Identifying 8-mRNAsi based signature for predicting survival in patients with head and neck squamous cell carcinoma via machine learning. Front Genet 2020; 11: 566159. doi: 10.3389/fgene.2020.566159 PMID: 33329703
  17. Feng T, Wu T, Zhang Y, et al. Stemness analysis uncovers that the peroxisome proliferator-activated receptor signaling pathway can mediate fatty acid homeostasis in sorafenib-resistant hepatocellular carcinoma cells. Front Oncol 2022; 12: 912694. doi: 10.3389/fonc.2022.912694 PMID: 35957896
  18. Liao Y, Xiao H, Cheng M, Fan X. Bioinformatics analysis reveals biomarkers with cancer stem cell characteristics in lung squamous cell carcinoma. Front Genet 2020; 11: 427. doi: 10.3389/fgene.2020.00427 PMID: 32528520
  19. Salomonis N, Dexheimer PJ, Omberg L, et al. Integrated genomic analysis of diverse induced pluripotent stem cells from the progenitor cell biology consortium. Stem Cell Reports 2016; 7(1): 110-25. doi: 10.1016/j.stemcr.2016.05.006 PMID: 27293150
  20. Der SD, Sykes J, Pintilie M, et al. Validation of a histology-independent prognostic gene signature for early-stage, non-small-cell lung cancer including stage IA patients. J Thorac Oncol 2014; 9(1): 59-64. doi: 10.1097/JTO.0000000000000042 PMID: 24305008
  21. Rousseaux S, Debernardi A, Jacquiau B, Vitte A-L, Vesin A, Nagy-Mignotte H. Ectopic activation of germline and placental genes identifies aggressive metastasis-prone lung cancers. Sci Transl Med 2013; 5(186): 186ra66. doi: 10.1126/scitranslmed.3005723
  22. Micke P. Biomarker discovery in non-small cell lung cancer: Integrating gene expression profiling, meta-analysis and tissue microarray validation. Clin Cancer Res 2013; 19(1): 194-204.
  23. Xie Y, Xiao G, Coombes KR, et al. Robust gene expression signature from formalin-fixed paraffin-embedded samples predicts prognosis of non-small-cell lung cancer patients. Clin Cancer Res 2011; 17(17): 5705-14. doi: 10.1158/1078-0432.CCR-11-0196 PMID: 21742808
  24. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 2012; 28(6): 882-3. doi: 10.1093/bioinformatics/bts034 PMID: 22257669
  25. Sokolov A, Carlin DE, Paull EO, Baertsch R, Stuart JM. Pathway-based genomics prediction using generalized elastic net. PLOS Comput Biol 2016; 12(3): e1004790. doi: 10.1371/journal.pcbi.1004790 PMID: 26960204
  26. Gao J, Aksoy BA, Dogrusoz U, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 2013; 6(269): pl1. doi: 10.1126/scisignal.2004088 PMID: 23550210
  27. Yoshihara K, Shahmoradgoli M, Martínez E, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun 2013; 4(1): 2612. doi: 10.1038/ncomms3612 PMID: 24113773
  28. Kassambara A, Kosinski M, Biecek P, Fabian S. Survminer: Drawing Survival Curves using ‘ggplot2’ R package version 03 2017. Available from https://cran.r-project.org/web/packages/survminer/survminer.pdf
  29. Langfelder P, Horvath S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics 2008; 9(1): 559. doi: 10.1186/1471-2105-9-559 PMID: 19114008
  30. Liao Y, Wang J, Jaehnig EJ, Shi Z, Zhang B. WebGestalt 2019: Gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res 2019; 47(W1): W199-205. doi: 10.1093/nar/gkz401 PMID: 31114916
  31. Wilkerson M, Waltman P, Wilkerson MM. ConsensusClusterPlus: ConsensusClusterPlus R package version 1220 2013. Available from https://bioconductor.riken.jp/packages/3.1/bioc/html/ConsensusClusterPlus.html
  32. Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Yang T-HO. The immune landscape of cancer. Immunity 2018; 48(4): 812-830.e14. doi: 10.1016/j.immuni.2018.03.023
  33. Charoentong P, Finotello F, Angelova M, et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep 2017; 18(1): 248-62. doi: 10.1016/j.celrep.2016.12.019 PMID: 28052254
  34. Liu Y, He M, Wang D, Diao L, Liu J, Tang L. HisgAtlas 1.0: A human immunosuppression gene database. Database 2017; 2017: bax094.
  35. Jiang P, Gu S, Pan D, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med 2018; 24(10): 1550-8. doi: 10.1038/s41591-018-0136-1 PMID: 30127393
  36. Geeleher P, Cox N, Huang RS. 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 PMID: 25229481
  37. Hastie T, Qian J, Tay K. An Introduction to glmnet. CRAN R Repositary 2021.
  38. He L, Jin M, Jian D, et al. Identification of four immune subtypes in locally advanced rectal cancer treated with neoadjuvant chemotherapy for predicting the efficacy of subsequent immune checkpoint blockade. Front Immunol 2022; 13: 955187. doi: 10.3389/fimmu.2022.955187 PMID: 36238279
  39. Riaz N, Havel JJ, Makarov V, Desrichard A, Urba WJ, Sims JS, et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 2017; 171(4): 934-49. e16. doi: 10.1016/j.cell.2017.09.028
  40. Qi H, Li W, Zhang J, et al. Glioma-associated oncogene homolog 1 stimulates FOXP3 to promote non-small cell lung cancer stemness. Am J Transl Res 2020; 12(5): 1839-50. PMID: 32509180
  41. Huang X, Bi N, Wang J, Ren H, Pan D, Lu X. Chidamide and radiotherapy synergistically induce cell apoptosis and suppress tumor growth and cancer stemness by regulating the MiR-375-EIF4G3 axis in lung squamous cell carcinomas. J Oncol 2021; 2021: 4936207. doi: 10.1155/2021/4936207
  42. Jiang W, Xie N, Xu C. Characterization of a prognostic model for lung squamous cell carcinoma based on eight stemness index-related genes. BMC Pulm Med 2022; 22(1): 224. doi: 10.1186/s12890-022-02011-0 PMID: 35676660
  43. Zhang A, Miao K, Sun H, Deng CX. Tumor heterogeneity reshapes the tumor microenvironment to influence drug resistance. Int J Biol Sci 2022; 18(7): 3019-33. doi: 10.7150/ijbs.72534 PMID: 35541919
  44. Wu F, Fan J, He Y, et al. Single-cell profiling of tumor heterogeneity and the microenvironment in advanced non-small cell lung cancer. Nat Commun 2021; 12(1): 2540. doi: 10.1038/s41467-021-22801-0 PMID: 33953163
  45. Wang JC, Xu Y, Huang ZM, Lu XJ. T cell exhaustion in cancer: Mechanisms and clinical implications. J Cell Biochem 2018; 119(6): 4279-86. doi: 10.1002/jcb.26645 PMID: 29274296
  46. Ruffo E, Wu RC, Bruno TC, Workman CJ, Vignali DA. Eds Lymphocyte-activation gene 3 (LAG3): The next immune checkpoint receptor Seminars in immunology. Elsevier 2019.
  47. Niu B, Zhou F, Su Y, et al. Different expression characteristics of LAG3 and PD-1 in sepsis and their synergistic effect on T cell exhaustion: A new strategy for immune checkpoint blockade. Front Immunol 2019; 10: 1888. doi: 10.3389/fimmu.2019.01888 PMID: 31440257
  48. Yu X, Huang X, Chen X, Liu J, Wu C, Pu Q. Eds Characterization of a novel anti-human lymphocyte activation gene 3 (LAG-3) antibody for cancer immunotherapy MAbs. Taylor & Francis 2019.
  49. Zhang LL, Kan M, Zhang MM, et al. Multiregion sequencing reveals the intratumor heterogeneity of driver mutations in TP53-driven non-small cell lung cancer. Int J Cancer 2017; 140(1): 103-8. doi: 10.1002/ijc.30437 PMID: 27646734
  50. Cheng X, Yin H, Fu J, et al. Aggregate analysis based on TCGA: TTN missense mutation correlates with favorable prognosis in lung squamous cell carcinoma. J Cancer Res Clin Oncol 2019; 145(4): 1027-35. doi: 10.1007/s00432-019-02861-y PMID: 30810839
  51. Xue D, Lin H, Lin L, Wei Q, Yang S, Chen X. TTN/TP53 mutation might act as the predictor for chemotherapy response in lung adenocarcinoma and lung squamous carcinoma patients. Transl Cancer Res 2021; 10(3): 1284-94. doi: 10.21037/tcr-20-2568 PMID: 35116455
  52. Nguyen L, W M Martens J, Van Hoeck A, Cuppen E. Pan-cancer landscape of homologous recombination deficiency. Nat Commun 2020; 11(1): 5584. doi: 10.1038/s41467-020-19406-4 PMID: 33149131
  53. Jia L, Zhang W, Wang C-Y. BMI1 inhibition eliminates residual cancer stem cells after PD1 blockade and activates antitumor immunity to prevent metastasis and relapse. Cell Stem Cell 2020; 27(2): 238-53.e6. doi: 10.1016/j.stem.2020.06.022
  54. D’Agostino S, Lanzillotta D, Varano M, et al. The receptor protein tyrosine phosphatase PTPRJ negatively modulates the CD98hc oncoprotein in lung cancer cells. Oncotarget 2018; 9(34): 23334-48. doi: 10.18632/oncotarget.25101 PMID: 29805737
  55. Simões-Pereira J, Moura MM, Marques IJ, et al. The role of EIF1AX in thyroid cancer tumourigenesis and progression. J Endocrinol Invest 2019; 42(3): 313-8. doi: 10.1007/s40618-018-0919-8 PMID: 29968046
  56. Li Y, Guo L, Ying S, Feng GH, Zhang Y. Transcriptional repression of p21 by EIF1AX promotes the proliferation of breast cancer cells. Cell Prolif 2020; 53(10): e12903. doi: 10.1111/cpr.12903 PMID: 32926483
  57. Dong H, Li Y, Zhou J, Song J. MiR-18a-5p promotes proliferation, migration, and invasion of endometrial cancer cells by targeting THBD. Crit Rev Eukaryot Gene Expr 2021; 31(2): 63-73.
  58. Tan Z, Liu X, Yu E, et al. Lentivirus-mediated RNA interference of tripartite motif 68 inhibits the proliferation of colorectal cancer cell lines SW1116 and HCT116 in vitro. Oncol Lett 2017; 13(4): 2649-55. doi: 10.3892/ol.2017.5787 PMID: 28454446
  59. Gupta A, Shukla N, Nehra M, et al. A pilot study on the whole exome sequencing of prostate cancer in the indian phenotype reveals distinct polymorphisms. Front Genet 2020; 11: 874. doi: 10.3389/fgene.2020.00874 PMID: 33193569
  60. Zhang B, He Y, Ma G, et al. Identification of stemness index-related long noncoding RNA SNHG12 in human bladder cancer based on WGCNA. Mol Cell Probes 2022; 66: 101867. doi: 10.1016/j.mcp.2022.101867 PMID: 36183925

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c) 2024 Bentham Science Publishers