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. 2022 Oct 12;11(1):70.
doi: 10.1186/s40164-022-00327-5.

Identification of adenoid subtype characterized with immune-escaped phenotype in lung squamous carcinoma based on transcriptomics

Jie Mei #  1   2   3 , Yun Cai #  3 , Ofek Mussafi #  4 , Mingfeng Zheng  1 , Yongrui Xu  1 , Ruo Chen  1 , Guanyu Jiang  1 , Wenjun Mao  5 , Wei Xia  6 , Yuan Wan  7
Affiliations

Identification of adenoid subtype characterized with immune-escaped phenotype in lung squamous carcinoma based on transcriptomics

Jie Mei et al. Exp Hematol Oncol. .

Abstract

Non-small cell lung cancer (NSCLC) is a heterogeneous disease, and its demarcation contributes to various therapeutic outcomes. However, a small subset of tumors shows different molecular features that are in contradiction with pathological classification. Unsupervised clustering was performed to subtype NSCLC using the transcriptome data from the TCGA database. Next, immune microenvironment features of lung adenocarcinoma (LUAD), lung squamous carcinoma (LUSC), and lung adenoid squamous carcinoma (LASC) were characterized. In addition, diagnostic biomarkers to demarcate LASC among LUSC were screened using weighted gene co-expression network analysis (WGCNA) and validated by the in-house cohort. LASC was identified as a novel subtype with adenoid transcriptomic features in LUSC, which exhibited the most immuno-escaped phenotype among all NSCLC subtypes. In addition, FOLR1 was identified as a biomarker for LASC discrimination using the WGCNA analysis, and its diagnostic value was validated by the in-house cohort. Moreover, FOLR1 was related to immuno-escaped tumors in LUSC but not in LUAD. Overall, we proposed a novel typing strategy in NSCLC and identified FOLR1 as a biomarker for LASC discrimination.

Keywords: Biomarker; Immuno-escaped; NSCLC; Subtype.

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Conflict of interest statement

There are no competing interests.

Figures

Fig. 1
Fig. 1
Identification of LASC as a novel subtype in LUSC. A Unsupervised clustering of LUAD, LUSC, and LASC samples. B, C Expression levels of KRT7, KRT18, NAPSA, KRT5, TP63, and DSG3 in LUAD (n = 512), LUSC (n = 430), and LASC (n = 66) samples. Significance was calculated with One-way ANOVA with Tukey’s multiple comparisons test. ns no statistical difference, **P < 0.01, ***P < 0.001. D Mutant profiles of EGFR, KEAP1, KRAS, STK11, TP53, CDKN2A, PIK3CA, ROS1, and NF1 in LUAD, LUSC, and LASC samples. E Prognostic analysis of patients in LUAD, LUSC, and LASC subtypes. Significance was calculated with log-rank test
Fig. 2
Fig. 2
FOLR1 is a biomarker for LASC discrimination and correlated immune feature in LUSC. A Levels of the score of genes in the blue calculated by the ssGSEA method in LUAD (n = 512), LUSC (n = 430), and LASC (n = 66) subtypes. Significance was calculated with One-way ANOVA with Tukey’s multiple comparisons test. B Diagnostic value of the score of genes in the blue for the discrimination LASC in LUSC. C Diagnostic value of the single gene in the blue for the discrimination LASC in LUSC. D, E Representative images revealing FOLR1 expression in LUAD (n = 30) and LUSC (n = 90) subtypes and semi-quantitative analysis. Significance was calculated with Student’s t-test. F Prognostic value of FOLR1 expression in LUSC. Fifty-three patients with low FOLR1 expression, and 47 patients with high FOLR1 expression. Significance was calculated with log-rank test. G Representative images revealing low and high FOLR1 and PD-L1 expression in LUSC. H Correlation between FOLR1 and PD-L1 expression in LUSC. Significance was calculated with Pearson test. I Representative images revealing low and high FOLR1 and PD-L1 expression in LUAD. J Correlation between FOLR1 and PD-L1 expression in LUAD. Significance was calculated with Pearson test
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