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Full Length Article | Open Access

Hallmark guided identification and characterization of a novel immune-relevant signature for prognostication of recurrence in stage Ⅰ–Ⅲ lung adenocarcinoma

Yongqiang Zhanga,b,1Zhao Yanga,1Yuqin Tangc,1Chengbin GuodDanni LindLinling ChengdXun Hue,f()Kang Zhanga,d()Gen Lib()
West China School of Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, Guangdong 510620, China
State Key Laboratory of Southwestern Chinese Medicine Resources, School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
Center for Biomedicine and Innovations, Faculty of Medicine, Macau University of Science and Technology, Macau 999078, China
Clinical Research Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310003, China
Biorepository, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China

1 These authors contributed equally to this work.

Peer review under responsibility of Chongqing Medical University.

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Abstract

The high risk of postoperative mortality in lung adenocarcinoma (LUAD) patients is principally driven by cancer recurrence and low response rates to adjuvant treatment. Here, A combined cohort containing 1,026 stage Ⅰ–Ⅲ patients was divided into the learning (n = 678) and validation datasets (n = 348). The former was used to establish a 16-mRNA risk signature for recurrence prediction with multiple statistical algorithms, which was verified in the validation set. Univariate and multivariate analyses confirmed it as an independent indicator for both recurrence-free survival (RFS) and overall survival (OS). Distinct molecular characteristics between the two groups including genomic alterations, and hallmark pathways were comprehensively analyzed. Remarkably, the classifier was tightly linked to immune infiltrations, highlighting the critical role of immune surveillance in prolonging survival for LUAD. Moreover, the classifier was a valuable predictor for therapeutic responses in patients, and the low-risk group was more likely to yield clinical benefits from immunotherapy. A transcription factor regulatory protein–protein interaction network (TF-PPI-network) was constructed via weighted gene co-expression network analysis (WGCNA) concerning the hub genes of the signature. The constructed multidimensional nomogram dramatically increased the predictive accuracy. Therefore, our signature provides a forceful basis for individualized LUAD management with promising potential implications.

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 A Cancer J Clin. 2021;71(3):209-249.

2

Bender E. Epidemiology: the dominant malignancy. Nature. 2014;513(7517):S2-S3.

3

Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646-674.

4

Marusyk A, Janiszewska M, Polyak K. Intratumor heterogeneity: the Rosetta stone of therapy resistance. Cancer Cell. 2020;37(4):471-484.

5

Travis WD, Brambilla E, Noguchi M, et al. International association for the study of lung cancer/American thoracic society/European respiratory society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol. 2011;6(2):244-285.

6

de Nonneville A, Finetti P, Adelaide J, et al. A tyrosine kinase expression signature predicts the post-operative clinical outcome in triple negative breast cancers. Cancers. 2019;11(8):1158.

7

Shimizu H, Nakayama KI. A 23 gene-based molecular prognostic score precisely predicts overall survival of breast cancer patients. EBioMedicine. 2019;46:150-159.

8

Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351(27):2817-2826.

9

Long J, Chen P, Lin J, et al. DNA methylation-driven genes for constructing diagnostic, prognostic, and recurrence models for hepatocellular carcinoma. Theranostics. 2019;9(24):7251-7267.

10

Hoshida Y, Villanueva A, Kobayashi M, et al. Gene expression in fixed tissues and outcome in hepatocellular carcinoma. N Engl J Med. 2008;359(19):1995-2004.

11

Lin T, Gu J, Qu K, et al. A new risk score based on twelve hepatocellular carcinoma-specific gene expression can predict the patients' prognosis. Aging. 2018;10(9):2480-2497.

12

Zhang Y, Tang Y, Guo C, Li G. Integrative analysis identifies key mRNA biomarkers for diagnosis, prognosis, and therapeutic targets of HCV-associated hepatocellular carcinoma. Aging. 2021;13(9):12865-12895.

13

Tang Y, Zhang Y, Hu X. Identification of potential hub genes related to diagnosis and prognosis of hepatitis B virus-related hepatocellular carcinoma via integrated bioinformatics analysis. BioMed Res Int. 2020;2020:4251761.

14

Zhou Z, Mo S, Dai W, et al. Prognostic nomograms for predicting cause-specific survival and overall survival of stage Ⅰ-Ⅲ colon cancer patients: a large population-based study. Cancer Cell Int. 2019;19:355.

15

Jiang H, Du J, Gu J, Jin L, Pu Y, Fei B. A 65-gene signature for prognostic prediction in colon adenocarcinoma. Int J Mol Med. 2018;41(4):2021-2027.

16

Okayama H, Schetter AJ, Ishigame T, et al. The expression of four genes as a prognostic classifier for stage Ⅰ lung adenocarcinoma in 12 independent cohorts. Cancer Epidemiol Biomarkers Prev. 2014;23(12):2884-2894.

17

Larsen JE, Pavey SJ, Passmore LH, Bowman RV, Hayward NK, Fong KM. Gene expression signature predicts recurrence in lung adenocarcinoma. Clin Cancer Res. 2007;13(10):2946-2954.

18

Zhang C, Zhang Z, Zhang G, et al. Clinical significance and inflammatory landscapes of a novel recurrence-associated immune signature in early-stage lung adenocarcinoma. Cancer Lett. 2020;479:31-41.

19

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.

20

Shi R, Bao X, Unger K, et al. Identification and validation of hypoxia-derived gene signatures to predict clinical outcomes and therapeutic responses in stage Ⅰ lung adenocarcinoma patients. Theranostics. 2021;11(10):5061-5076.

21

Sun J, Zhao T, Zhao D, et al. Development and validation of a hypoxia-related gene signature to predict overall survival in early-stage lung adenocarcinoma patients. Ther Adv Med Oncol. 2020;12:1758835920937904.

22

Xu F, Lin H, He P, et al. A TP53-associated gene signature for prediction of prognosis and therapeutic responses in lung squamous cell carcinoma. Oncoimmunology. 2020;9(1):1731943.

23

Li Y, Gu J, Xu F, et al. Molecular characterization, biological function, tumor microenvironment association and clinical significance of m6A regulators in lung adenocarcinoma. Briefings Bioinf. 2021;22(4):bbaa225.

24

Cai J, Tong Y, Huang L, et al. Identification and validation of a potent multi-mRNA signature for the prediction of early relapse in hepatocellular carcinoma. Carcinogenesis. 2019;40(7):840-852.

25

Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545-15550.

26

Okayama H, Kohno T, Ishii Y, et al. Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas. Cancer Res. 2012;72(1):100-111.

27

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.

28

Sato M, Larsen JE, Lee W, et al. Human lung epithelial cells progressed to malignancy through specific oncogenic manipulations. Mol Cancer Res. 2013;11(6):638-650.

29

Botling J, Edlund K, Lohr M, et al. 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.

30

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):882-883.

31

Liberzon A, Birger C, Thorvaldsdottir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015;1(6):417-425.

32

Zhang Y, Yang M, Ng DM, et al. Multi-omics data analyses construct TME and identify the immune-related prognosis signatures in human LUAD. Mol Ther Nucleic Acids. 2020;21:860-873.

33

Liu J, Nie S, Wu Z, et al. Exploration of a novel prognostic risk signatures and immune checkpoint molecules in endometrial carcinoma microenvironment. Genomics. 2020;112(5):3117-3134.

34

Wang S, Zhang Q, Yu C, Cao Y, Zuo Y, Yang L. Immune cell infiltration-based signature for prognosis and immunogenomic analysis in breast cancer. Briefings Bioinf. 2021;22(2):2020-2031.

35

Brooks JM, Menezes AN, Ibrahim M, et al. Development and validation of a combined hypoxia and immune prognostic classifier for head and neck cancer. Clin Cancer Res. 2019;25(17):5315-5328.

36

Wang Z, Zhu J, Liu Y, et al. Development and validation of a novel immune-related prognostic model in hepatocellular carcinoma. J Transl Med. 2020;18(1):67.

37

Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45(W1):W98-W102.

38

Hao X, Luo H, Krawczyk M, et al. DNA methylation markers for diagnosis and prognosis of common cancers. Proc Natl Acad Sci U S A. 2017;114(28):7414-7419.

39

Zhang YQ, Wang WY, Xue JX, et al. microRNA expression profile on solid subtype of invasive lung adenocarcinoma reveals a panel of four miRNAs to Be associated with poor prognosis in Chinese patients. J Cancer. 2016;7(12):1610-1620.

40

Zhu CQ, Tsao MS. Prognostic markers in lung cancer: is it ready for prime time? Transl Lung Cancer Res. 2014;3(3):149-158.

41

Mathe EA, Patterson AD, Haznadar M, et al. Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer. Cancer Res. 2014;74(12):3259-3270.

42

Zhang C, He Z, Cheng L, Cao J. Investigation of prognostic markers of lung adenocarcinoma based on tumor metabolism-related genes. Front Genet. 2021;12:760506.

43

Wang Y, Yang Z. A Gleason score-related outcome model for human prostate cancer: a comprehensive study based on weighted gene co-expression network analysis. Cancer Cell Int. 2020;20:159.

44

Millstein J, Budden T, Goode EL, et al. Prognostic gene expression signature for high-grade serous ovarian cancer. Ann Oncol. 2020;31(9):1240-1250.

45

Kang H, Chen IM, Wilson CS, et al. Gene expression classifiers for relapse-free survival and minimal residual disease improve risk classification and outcome prediction in pediatric B-precursor acute lymphoblastic leukemia. Blood. 2010;115(7):1394-1405.

46

Liu J, Nie S, Li S, et al. Methylation-driven genes and their prognostic value in cervical squamous cell carcinoma. Ann Transl Med. 2020;8(14):868.

47

Wu J, Jin S, Gu W, et al. Construction and validation of a 9-gene signature for predicting prognosis in stage III clear cell renal cell carcinoma. Front Oncol. 2019;9:152.

48

Xiao H, Wang B, Xiong HX, et al. A novel prognostic index of hepatocellular carcinoma based on immunogenomic landscape analysis. J Cell Physiol. 2021;236(4):2572-2591.

49

Liu Y, Qi J, Dou Z, et al. Systematic expression analysis of WEE family kinases reveals the importance of PKMYT1 in breast carcinogenesis. Cell Prolif. 2020;53(2):e12741.

50

Tang Y, Guo C, Yang Z, Wang Y, Zhang Y, Wang D. Identification of a tumor immunological phenotype-related gene signature for predicting prognosis, immunotherapy efficacy, and drug candidates in hepatocellular carcinoma. Front Immunol. 2022;13:862527.

51

Chen PH, Bendris N, Hsiao YJ, et al. Crosstalk between CLCb/Dyn1-mediated adaptive clathrin-mediated endocytosis and epidermal growth factor receptor signaling increases metastasis. Dev Cell. 2017;40(3):278-288.e5.

52

Wilson BJ, Allen JL, Caswell PT. Vesicle trafficking pathways that direct cell migration in 3D matrices and in vivo. Traffic. 2018;19(12):899-909.

53

Majeed SR, Vasudevan L, Chen CY, et al. Clathrin light chains are required for the gyrating-clathrin recycling pathway and thereby promote cell migration. Nat Commun. 2014;5:3891.

54

Wong YF, Cheung TH, Lo KWK, et al. Identification of molecular markers and signaling pathway in endometrial cancer in Hong Kong Chinese women by genome-wide gene expression profiling. Oncogene. 2007;26(13):1971-1982.

55

Shi W, Ye Z, Zhuang L, et al. Olfactomedin 1 negatively regulates NF-κB signalling and suppresses the growth and metastasis of colorectal cancer cells. J Pathol. 2016;240(3):352-365.

56

Khan J, Wei JS, Ringner M, et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med. 2001;7(6):673-679.

57

Wu L, Chang W, Zhao J, et al. Development of autoantibody signatures as novel diagnostic biomarkers of non-small cell lung cancer. Clin Cancer Res. 2010;16(14):3760-3768.

58

Gong R, Lin W, Gao A, et al. Forkhead box C1 promotes metastasis and invasion of non-small cell lung cancer by binding directly to the lysyl oxidase promoter. Cancer Sci. 2019;110(12):3663-3676.

59

Zhang M, Zhu K, Pu H, et al. An immune-related signature predicts survival in patients with lung adenocarcinoma. Front Oncol. 2019;9:1314.

60

Neri S, Miyashita T, Hashimoto H, et al. Fibroblast-led cancer cell invasion is activated by epithelial-mesenchymal transition through platelet-derived growth factor BB secretion of lung adenocarcinoma. Cancer Lett. 2017;395:20-30.

61

Jiao Y, Sun KK, Zhao L, Xu JY, Wang LL, Fan SJ. Suppression of human lung cancer cell proliferation and metastasis in vitro by the transducer of ErbB-2.1 (TOB1). Acta Pharmacol Sin. 2012;33(2):250-260.

62

Matsuoka R, Shiba-Ishii A, Nakano N, et al. Heterotopic production of ceruloplasmin by lung adenocarcinoma is significantly correlated with prognosis. Lung Cancer. 2018;118:97-104.

63

Takai D, Yagi Y, Wakazono K, et al. Silencing of HTR1B and reduced expression of EDN1 in human lung cancers, revealed by methylation-sensitive representational difference analysis. Oncogene. 2001;20(51):7505-7513.

64

Zhang Y, Fan Q, Guo Y, Zhu K. Eight-gene signature predicts recurrence in lung adenocarcinoma. Cancer Biomarkers. 2020;28(4):447-457.

65

Han Q, Cheng P, Yang H, Liang H, Lin F. Altered expression of microRNA-365 is related to the occurrence and development of non-small-cell lung cancer by inhibiting TRIM25 expression. J Cell Physiol. 2019;234(12):22321-22330.

66

Comtesse N, Keller A, Diesinger I, et al. Frequent overexpression of the genes FXR1, CLAPM1 and EIF4G located on amplicon 3q26-27 in squamous cell carcinoma of the lung. Int J Cancer. 2007;120(12):2538-2544.

67

Dou N, Yang D, Yu S, Wu B, Gao Y, Li Y. SNRPA enhances tumour cell growth in gastric cancer through modulating NGF expression. Cell Prolif. 2018;51(5):e12484.

68

Yin L, Cai Z, Zhu B, Xu C. Identification of key pathways and genes in the dynamic progression of HCC based on WGCNA. Genes. 2018;9(2):92.

69

Guo C, Tang Y, Zhang Y, Li G. Mining TCGA data for key biomarkers related to immune microenvironment in endometrial cancer by immune score and weighted correlation network analysis. Front Mol Biosci. 2021;8:645388.

70

She Y, Jin Z, Wu J, et al. Development and validation of a deep learning model for non-small cell lung cancer survival. JAMA Netw Open. 2020;3(6):e205842.

71

Liang W, Zhang L, Jiang G, et al. Development and validation of a nomogram for predicting survival in patients with resected non-small-cell lung cancer. J Clin Oncol. 2015;33(8):861-869.

72

Keam B, Kim DW, Park JH, et al. Nomogram predicting clinical outcomes in non-small cell lung cancer patients treated with epidermal growth factor receptor tyrosine kinase inhibitors. Cancer Treat Res. 2014;46(4):323-330.

Genes & Diseases
Pages 1657-1674
Cite this article:
Zhang Y, Yang Z, Tang Y, et al. Hallmark guided identification and characterization of a novel immune-relevant signature for prognostication of recurrence in stage Ⅰ–Ⅲ lung adenocarcinoma. Genes & Diseases, 2023, 10(4): 1657-1674. https://doi.org/10.1016/j.gendis.2022.07.005
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