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. 2022 Jul;11(7):1380-1393.
doi: 10.21037/tlcr-22-408.

Identification and validation of a ferroptosis-related prognostic risk-scoring model and key genes in small cell lung cancer

Suyang Li #  1 , Guihuan Qiu #  1 , Jun Wu  2 , Jiaxin Ying  3 , Haiyi Deng  1 , Xiaohong Xie  1 , Xinqing Lin  1 , Zhanhong Xie  1 , Yinyin Qin  1 , Yansheng Wang  1 , Xiaodong Ma  2 , Luka Brcic  4 , Mariano Provencio  5 , Yangchao Chen  6 , Chengzhi Zhou  1 , Ming Liu  1
Affiliations

Identification and validation of a ferroptosis-related prognostic risk-scoring model and key genes in small cell lung cancer

Suyang Li et al. Transl Lung Cancer Res. 2022 Jul.

Abstract

Background: Small cell lung cancer (SCLC) is an aggressive lung malignancy with high relapse rates and poor survival outcomes. Ferroptosis is a recently identified type of cell death caused by excessive intracellular iron accumulation and lipid peroxidation, which may mediate tumor-infiltrating immune cells to influence anti-cancer immunity. But prognostic value of ferroptosis-related genes and its relationship with the treatment response of immunotherapies in SCLC have not been elucidated.

Methods: The RNA-sequencing and clinical data of SCLC patients were downloaded from the cBioPortal database. A ferroptosis-related prognostic risk-scoring model was constructed based on univariable and multivariable Cox-regression analysis. Kaplan-Meier (K-M) survival curves and receiver operating characteristics (ROC) curves were constructed to assess the sensitivity and specificity of the risk-scoring model. And the correlations between ferroptosis-related prognostic genes and immune microenvironment were explored. The IC50 values of anti-cancer drugs were downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC) database and the correlation analysis with the key gene thioredoxin-interacting protein (TXNIP) was performed. In addition, immunohistochemistry (IHC) staining was employed to detect the expression of TXNIP in 20 SCLC patients who received first-line chemo-immunotherapy. Immunotherapeutic response according to iRECIST (Response Evaluation Criteria in Solid Tumours for immunotherapy trials) were recorded.

Results: We constructed a risk-score successfully dividing patients in the low- and high-risk groups (with better and worse prognosis, respectively). The area under the curve (AUC) of this risk-scoring model was 0.812, showing it had good utility in predicting the prognosis of SCLC. Moreover, ferroptosis-related genes were associated with the degree of immune infiltration of SCLC. Most importantly, we found that the TXNIP expression was highly correlated with the degree of immune invasion and the efficacy of chemotherapy in combination with immunotherapy in SCLC patients.

Conclusions: The ferroptosis-related prognostic risk-scoring model proposed in this study can potentially predict the prognosis of SCLC patients. TXNIP may serve as a potential biomarker to predict the prognosis and efficacy of chemotherapy combined with immunotherapy in SCLC patients.

Keywords: Ferroptosis; immunotherapy; risk-scoring model; small cell lung cancer (SCLC); thioredoxin-interacting protein (TXNIP).

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-22-408/coif). LB received grants from Takeda, AstraZeneca, BMS and Roche; he also received payment for lectures and participated in advisory boards form Invitae, Eli-Lilly, AstraZeneca, Roche, MSD, Merck, BMS, Pfizer, Novartis, Takeda, Janssen; support for attending meeting from Pfizer. He is Int. Secretary-Austrian Society of Pathology; PPS Membership and Awards Committee; Member of the Mesothelioma Committee of IASLC. Mariano Provencio received consulting fees from BMS, MSD, Lilly, Takeda, Janssen; grants from BMS, Lilly, MSD and Takeda; support for attending meetings from MSD and AZ and received payment honoraria for lectures from BMS, MSD, AZ, Takeda. The other authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
GO and KEGG analyses for 77 ferroptosis-related genes in SCLC. (A) GO analysis. (B) KEGG analysis. X-axis: the count ratio of 77 ferroptosis-related genes enriched in the pathway; Y-axis: the name of the pathway. TORC2, target of rapamycin complex 2; TOR, target of rapamycin; BP, biological process; CC, cellular component; MF, molecular function; MAPK, mitogen-activated protein kinase; NOD, nucleotide-binding oligomerization domain; mTOR, mammalian target of rapamycin; TNF, tumor necrosis factor; PD-L1, programmed cell death ligand 1; PD-1, programmed cell death receptor-1; IL-17, interleukin-17; TCA, tricarboxylic acid; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; SCLC, small cell lung cancer.
Figure 2
Figure 2
Construction of the Risk-Scoring Model. The risk-score distributions (A), survival status (B) and expression levels of the 5 identified ferroptosis-associated genes (C) in the high-risk and low-risk groups divided using the median risk-score in the SCLC patients. SCLC, small cell lung cancer.
Figure 3
Figure 3
The independent predictability of the Risk-Scoring Model. (A) Kaplan-Meier curves result. (B) The AUC values of the risk factors. (C) The AUC for the prediction of 1-, 2-, 3-year survival rate of SCLC patients. (D) Univariate Cox analysis for the risk-score. (E) Multivariate Cox analysis for the risk-score. AUC, area under the curve; SCLC, small cell lung cancer.
Figure 4
Figure 4
The generality of the risk-scoring model. Stratified analyses based on clinical variables, including (A) age, (B) gender, (C) T stage, (D) N stage, (E) clinical stage, (F) chemotherapy, and (G) M stage.
Figure 5
Figure 5
A nomogram according to both prognostic ferroptosis-related genes and clinical characteristics. Pr(futime), probabilities of survival time, used for survival models only. *, P<0.05; **, P<0.01; ***, P<0.001.
Figure 6
Figure 6
Immune cell infiltration analysis. Correlation analysis of immune cell infiltration level with expression levels of prognostic ferroptosis-related genes (A) and TXNIP gene (B). TXNIP, thioredoxin-interacting protein; NK, natural killer cells; DC, dendritic cells; MAIT, mucosal-associated invariant T cells; NKT, natural killer T cells; Corr, correlation coefficient.
Figure 7
Figure 7
Survival and anti-cancer drug sensitivity analysis of TXNIP. (A) SCLC patients were divided into the TXNIP-low group and the TXNIP-high group. (B) Comparison of survival curves between the TXNIP-low and TXNIP-high group. (C) Volcano map exhibiting differences of anti-cancer drugs’ IC50 values in TXNIP-high group and TXNIP-low group of SCLC cell lines. IC50 values of AMG-319 (D) and Topotecan (E) in the TXNIP-high group and the TXNIP-low group. *, P<0.05; ***, P<0.001; ****, P<0.0001. TXNIP, thioredoxin-interacting protein; SCLC, small cell lung cancer; IC50, 50% inhibition concentration; AMG-319, an inhibitor of phosphoinositide-3 kinase; Topotecan, a topoisomerase I inhibitor.
Figure 8
Figure 8
Analysis of TXNIP and immunotherapy efficacy. The expression levels of TXNIP (A) and PD-L1 (B) in the response and non-response groups. (C) Immunohistochemical staining of the tumor specimen from patients in response group and non-response group. *, P<0.05. ns, no significance; SCLC, small cell lung cancer; TXNIP, thioredoxin-interacting protein; PD-L1, programmed cell death ligand 1.
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