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CC BY 4.0 · Glob Med Genet 2023; 10(04): 285-300
DOI: 10.1055/s-0043-1776386
Original Articles

The Evaluation of Prognostic Value and Immune Characteristics of Ferroptosis-Related Genes in Lung Squamous Cell Carcinoma

Jialin Su   *
1   Thoracic Medicine Department 1, Hunan Cancer Hospital, Changsha, Hunan Province, People's Republic of China
2   School of Life and Health Sciences, Hunan University of Science and Technology, Xiangtan, People's Republic of China
,
Shuhua Tan   *
2   School of Life and Health Sciences, Hunan University of Science and Technology, Xiangtan, People's Republic of China
,
Houwu Gong
3   College of Computer Science and Electronic Engineering, Hunan University, Changsha, People's Republic of China
,
Yongzhong Luo
1   Thoracic Medicine Department 1, Hunan Cancer Hospital, Changsha, Hunan Province, People's Republic of China
,
Tianli Cheng
1   Thoracic Medicine Department 1, Hunan Cancer Hospital, Changsha, Hunan Province, People's Republic of China
,
Hua Yang
1   Thoracic Medicine Department 1, Hunan Cancer Hospital, Changsha, Hunan Province, People's Republic of China
,
Xiaoping Wen
1   Thoracic Medicine Department 1, Hunan Cancer Hospital, Changsha, Hunan Province, People's Republic of China
,
Zhou Jiang
1   Thoracic Medicine Department 1, Hunan Cancer Hospital, Changsha, Hunan Province, People's Republic of China
,
Yuning Li
2   School of Life and Health Sciences, Hunan University of Science and Technology, Xiangtan, People's Republic of China
,
Lemeng Zhang
1   Thoracic Medicine Department 1, Hunan Cancer Hospital, Changsha, Hunan Province, People's Republic of China
› Institutsangaben Funding This study was supported by grants from the National Natural Science Foundation of Hunan Province (2020JJ4418, 2020RC3067), clinical medical technology innovation-guided project (2020SK51112); HUI LAN PUBLIC FOUNDATION (HL-HS2020-1); and the science and technology innovation Program of Hunan Province (2023SK4024).
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Abstract

Background The purpose of our study was to construct a prognostic model based on ferroptosis-related gene signature to improve the prognosis prediction of lung squamous carcinoma (LUSC).

Methods The mRNA expression profiles and clinical data of LUSC patients were downloaded. LUSC-related essential differentially expressed genes were integrated for further analysis. Prognostic gene signatures were identified through random forest regression and univariate Cox regression analyses for constructing a prognostic model. Finally, in a preliminary experiment, we used the reverse transcription-quantitative polymerase chain reaction assay to verify the relationship between the expression of three prognostic gene features and ferroptosis.

Results Fifty-six ferroptosis-related essential genes were identified by using integrated analysis. Among these, three prognostic gene signatures (HELLS, POLR2H, and POLE2) were identified, which were positively affected by LUSC prognosis but negatively affected by immune cell infiltration. Significant overexpression of immune checkpoint genes occurred in the high-risk group. In preliminary experiments, we confirmed that the occurrence of ferroptosis can reduce three prognostic gene signature expression.

Conclusions The three ferroptosis-related genes could predict the LUSC prognostic risk of antitumor immunity.

Keywords

lung squamous carcinoma - ferroptosis - prognostic model - immune infiltration - antitumor immunity

Author Contributions

L.M. and Y.L. contributed to manuscript preparation. L.Z. and S.T. contributed to the study design, manuscript writing, and data analysis. H.G. and J.S. contributed to clinical data collection. J.S., S.T., H.Y., X.W., and Z.J. contributed to molecular experiments and data statistics analysis. All authors read and approved the final manuscript.


Institutional Review Board Statement

Not applicable.


Informed Consent Statement

Not applicable.


Data Availability Statement

The raw data, supporting the conclusions of this manuscript, will be made available by contacting the corresponding author.


Ethics Approval and Informed Consent

This article does not contain any studies with human participants performed by any of the authors. The data from TCGA are publicly available. Thus, the present study was exempted from the approval of local ethics committees. The current research follows the TCGA data access policies and publication guidelines. All data submitted to the TCGA database have been ethically approved. The TCGA data citation guidelines and licenses have been followed.


* The authors contribute to the manuscript equally.


Supplementary Material



Publikationsverlauf

Artikel online veröffentlicht:
30. Oktober 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Denisov EV, Schegoleva AA, Gervas PA. et al. Premalignant lesions of squamous cell carcinoma of the lung: the molecular make-up and factors affecting their progression. Lung Cancer 2019; 135: 21-28
    CrossrefPubMedGoogle Scholar
  • 2 Friedlaender A, Banna G, Malapelle U, Pisapia P, Addeo A. Next generation sequencing and genetic alterations in squamous cell lung carcinoma: where are we today?. Front Oncol 2019; 9: 166
    CrossrefPubMedGoogle Scholar
  • 3 Cardona AF, Ricaurte L, Zatarain-Barrón ZL, Arrieta O. Squamous cell lung cancer: genomic evolution and personalized therapy. Salud Publica Mex 2019; 61 (03) 329-338
    CrossrefPubMedGoogle Scholar
  • 4 Socinski MA, Obasaju C, Gandara D. et al. Current and emergent therapy options for advanced squamous cell lung cancer. J Thorac Oncol 2018; 13 (02) 165-183
    CrossrefPubMedGoogle Scholar
  • 5 Chen Y, Zitello E, Guo R, Deng Y. The function of LncRNAs and their role in the prediction, diagnosis, and prognosis of lung cancer. Clin Transl Med 2021; 11 (04) e367
    CrossrefPubMedGoogle Scholar
  • 6 Ruiz-Cordero R, Devine WP. Targeted therapy and checkpoint immunotherapy in lung cancer. Surg Pathol Clin 2020; 13 (01) 17-33
    CrossrefPubMedGoogle Scholar
  • 7 Wang Y, Wei Z, Pan K, Li J, Chen Q. The function and mechanism of ferroptosis in cancer. Apoptosis 2020; 25 (11-12): 786-798
    CrossrefPubMedGoogle Scholar
  • 8 Xu T, Ding W, Ji X. et al. Molecular mechanisms of ferroptosis and its role in cancer therapy. J Cell Mol Med 2019; 23 (08) 4900-4912
    CrossrefPubMedGoogle Scholar
  • 9 Mou Y, Wang J, Wu J. et al. Ferroptosis, a new form of cell death: opportunities and challenges in cancer. J Hematol Oncol 2019; 12 (01) 34
    CrossrefPubMedGoogle Scholar
  • 10 Liang JY, Wang DS, Lin HC. et al. A novel ferroptosis-related gene signature for overall survival prediction in patients with hepatocellular carcinoma. Int J Biol Sci 2020; 16 (13) 2430-2441
    CrossrefPubMedGoogle Scholar
  • 11 Yu H, Han Z, Xu Z, An C, Xu L, Xin H. RNA sequencing uncovers the key long non-coding RNAs and potential molecular mechanism contributing to XAV939-mediated inhibition of non-small cell lung cancer. Oncol Lett 2019; 17 (06) 4994-5004
    PubMedGoogle Scholar
  • 12 Doll S, Freitas FP, Shah R. et al. FSP1 is a glutathione-independent ferroptosis suppressor. Nature 2019; 575 (7784): 693-698
    CrossrefPubMedGoogle Scholar
  • 13 Li C, Wang X, Qin R, Zhong Z, Sun C. Identification of a ferroptosis gene set that mediates the prognosis of squamous cell carcinoma of the head and neck. Front Genet 2021; 12: 698040
    CrossrefPubMedGoogle Scholar
  • 14 Li H, Zhang X, Yi C. et al. Ferroptosis-related gene signature predicts the prognosis in Oral squamous cell carcinoma patients. BMC Cancer 2021; 21 (01) 835
    CrossrefPubMedGoogle Scholar
  • 15 Han F, Li W, Chen T. et al. Ferroptosis-related genes for predicting prognosis of patients with laryngeal squamous cell carcinoma. Eur Arch Otorhinolaryngol 2021; 278 (08) 2919-2925
    CrossrefPubMedGoogle Scholar
  • 16 Wang GX, Tu HC, Dong Y. et al. ΔNp63 inhibits oxidative stress-induced cell death, including ferroptosis, and cooperates with the bcl-2 family to promote clonogenic survival. Cell Rep 2017; 21 (10) 2926-2939
    CrossrefPubMedGoogle Scholar
  • 17 Goldman M, Craft B, Hastie M. et al. The UCSC Xena platform for public and private cancer genomics data visualization and interpretation. Nat Biotechnol 2019; x: x
    Google Scholar
  • 18 Harrow J, Frankish A, Gonzalez JM. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res 2012; 22 (09) 1760-1774
    CrossrefPubMedGoogle Scholar
  • 19 Smyth GK. Limma: linear models for microarray data. In: Bioinformatics and computational biology solutions using R and Bioconductor. 2013
    Google Scholar
  • 20 Shi B, Ding J, Qi J, Gu Z. Characteristics and prognostic value of potential dependency genes in clear cell renal cell carcinoma based on a large-scale CRISPR-Cas9 and RNAi screening database DepMap. Int J Med Sci 2021; 18 (09) 2063-2075
    CrossrefPubMedGoogle Scholar
  • 21 Ho KH, Huang TW, Liu AJ, Shih CM, Chen KC. Cancer essential genes stratified lung adenocarcinoma patients with distinct survival outcomes and identified a subgroup from the terminal respiratory unit type with different proliferative signatures in multiple cohorts. Cancers (Basel) 2021; 13 (09) x
    Google Scholar
  • 22 Zhou N, Bao J. FerrDb: a manually curated resource for regulators and markers of ferroptosis and ferroptosis-disease associations. Database (Oxford) 2020; 2020: x
    CrossrefPubMedGoogle Scholar
  • 23 Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 2012; 16 (05) 284-287
    CrossrefPubMedGoogle Scholar
  • 24 Breiman L, Cutler A. randomForest: Breiman and Cutler's random forests for classification and regression. 2008
    PubMed
  • 25 Lin H, Zelterman D. Modeling Survival Data: Extending the Cox Model. Taylor & Francis Group; 2000
    Google Scholar
  • 26 Li T, Fan J, Wang B. et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res 2017; 77 (21) e108-e110
    CrossrefPubMedGoogle Scholar
  • 27 Uhlen M, Zhang C, Lee S. et al. A pathology atlas of the human cancer transcriptome. Science 2017; 357 (6352): eaan2507
    CrossrefPubMedGoogle Scholar
  • 28 Newman AM, Liu CL, Green MR. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 2015; 12 (05) 453-457
    CrossrefPubMedGoogle Scholar
  • 29 Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res 2018; 28 (11) 1747-1756
    CrossrefPubMedGoogle Scholar
  • 30 Li Y, Gu J, Xu F, Zhu Q, Ge D, Lu C. Transcriptomic and functional network features of lung squamous cell carcinoma through integrative analysis of GEO and TCGA data. Sci Rep 2018; 8 (01) 15834
    CrossrefPubMedGoogle Scholar
  • 31 Latunde-Dada GO. Ferroptosis: role of lipid peroxidation, iron and ferritinophagy. Biochim Biophys Acta, Gen Subj 2017; 1861 (08) 1893-1900
    CrossrefPubMedGoogle Scholar
  • 32 Han K, Wang J, Qian K, Zhao T, Liu X, Zhang Y. Construction of a prognostic model for non-small-cell lung cancer based on ferroptosis-related genes. Biosci Rep 2021; 41 (05) BSR20210527
    CrossrefPubMedGoogle Scholar
  • 33 Li S, Liu Y, Li J, Zhao X, Yu D. Mechanisms of ferroptosis and application to head and neck squamous cell carcinoma treatments. DNA Cell Biol 2021; 40 (06) 720-732
    CrossrefPubMedGoogle Scholar
  • 34 Diao X, Guo C, Liu L, Wang G, Li S. Identification and validation of an individualized prognostic signature of lung squamous cell carcinoma based on ferroptosis-related genes. Thorac Cancer 2021; 12 (23) 3236-3247
    CrossrefPubMedGoogle Scholar
  • 35 Shi ZZ, Tao H, Fan ZW, Song SJ, Bai J. Prognostic and immunological role of key genes of ferroptosis in pan-cancer. Front Cell Dev Biol 2021; 9: 748925
    CrossrefPubMedGoogle Scholar
  • 36 Cui J, Wang J, Shen Y, Lin D. Suppression of HELLS by miR-451a represses mTOR pathway to hinder aggressiveness of SCLC. Genes Genomics 2021; 43 (02) 105-114
    CrossrefPubMedGoogle Scholar
  • 37 Yano M, Ouchida M, Shigematsu H. et al. Tumor-specific exon creation of the HELLS/SMARCA6 gene in non-small cell lung cancer. Int J Cancer 2004; 112 (01) 8-13
    CrossrefPubMedGoogle Scholar
  • 38 Zhu W, Li LL, Songyang Y, Shi Z, Li D. Identification and validation of HELLS (helicase, lymphoid-specific) and ICAM1 (intercellular adhesion molecule 1) as potential diagnostic biomarkers of lung cancer. PeerJ 2020; 8: e8731
    CrossrefPubMedGoogle Scholar
  • 39 Li J, Wang J, Yu J. et al. Knockdown of POLE2 expression suppresses lung adenocarcinoma cell malignant phenotypes in vitro. Oncol Rep 2018; 40 (05) 2477-2486
    PubMedGoogle Scholar
  • 40 Dong A, Wang ZW, Ni N, Li L, Kong XY. Similarity and difference of pathogenesis among lung cancer subtypes suggested by expression profile data. Pathol Res Pract 2021; 220: 153365
    CrossrefPubMedGoogle Scholar
  • 41 Zhao J, Bao W, Cai W. Immune infiltration landscape in lung squamous cell carcinoma implications. Biomed Res Int 2020; 2020: 5981870
    CrossrefPubMedGoogle Scholar
  • 42 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 (01) 1731943
    CrossrefPubMedGoogle Scholar
  • 43 Wan L, Chen X, Deng J. et al. Plasma exosome-derived B-cell translation gene 1: a predictive marker for the prognosis in patients with non-small cell lung cancer. J Cancer 2021; 12 (05) 1538-1547
    CrossrefPubMedGoogle Scholar
  • 44 Song Z, Chen X, Shi Y. et al. Evaluating the potential of T cell receptor repertoires in predicting the prognosis of resectable non-small cell lung cancers. Mol Ther Methods Clin Dev 2020; 18: 73-83
    CrossrefPubMedGoogle Scholar
  • 45 Cao L, Che X, Qiu X. et al. M2 macrophage infiltration into tumor islets leads to poor prognosis in non-small-cell lung cancer. Cancer Manag Res 2019; 11: 6125-6138
    CrossrefPubMedGoogle Scholar
  • 46 Arrieta O, Aviles-Salas A, Orozco-Morales M. et al. Association between CD47 expression, clinical characteristics and prognosis in patients with advanced non-small cell lung cancer. Cancer Med 2020; 9 (07) 2390-2402
    CrossrefPubMedGoogle Scholar
  • 47 Yang QF, Wu D, Wang J. et al. Development and validation of an individualized immune prognostic model in stage I-III lung squamous cell carcinoma. Sci Rep 2021; 11 (01) 12727
    CrossrefPubMedGoogle Scholar
  • 48 Liu T, Luo H, Zhang J, Hu X, Zhang J. Molecular identification of an immunity- and ferroptosis-related gene signature in non-small cell lung cancer. BMC Cancer 2021; 21 (01) 783
    CrossrefPubMedGoogle Scholar
  • 49 Koutsoukos K, Mountzios G. Novel therapies for advanced squamous cell carcinoma of the lung. Future Oncol 2016; 12 (05) 659-667
    CrossrefPubMedGoogle Scholar
  • 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 (04) 1027-1035
    CrossrefPubMedGoogle Scholar