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. 2021 Apr 29:11:680918.
doi: 10.3389/fonc.2021.680918. eCollection 2021.

Hippo Pathway Core Genes Based Prognostic Signature and Immune Infiltration Patterns in Lung Squamous Cell Carcinoma

Affiliations

Hippo Pathway Core Genes Based Prognostic Signature and Immune Infiltration Patterns in Lung Squamous Cell Carcinoma

Chang Gu et al. Front Oncol. .

Abstract

Background: We investigated the prognostic effects and their patterns of immune infiltration of hippo pathway core genes in lung squamous cell carcinoma, in order to find some clues for underlying mechanisms of LUSC tumorigenesis and help developing new therapeutic methods.

Methods: The mutational data, transcriptome data and corresponding clinical medical information of LUSC patients were extracted from The Cancer Genome Atlas (TCGA) database. Differential expression genes (DEGs) and Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were explored. Survival analysis for the hippo core genes and the prognostic model were performed. Immune infiltration was estimated by CIBERSORT algorithm and some immune checkpoints-related genes were further investigated.

Results: Overall, 551 LUSC samples were included in our study, consisting of 502 LUSC tumor samples and 49 adjacent normal samples, respectively. There were 1910 up-regulated DEGs and 2253 down-regulated DEGs were finally identified. The top five mutational hippo pathway core genes were LATS1 (4%), WWC1 (2%), TAOK1 (2%), TAOK3 (2%), and TAOK2 (2%), respectively. the mutation of LATS2 was highly associated with co-mutational NF2 (P <0.05) and TAOK1 (P <0.05). In survival analyses, we found only WWC1 (log-rank p = 0.046, HR = 1.32, 95% CI = 1-1.73) and LATS2 (log-rank p = 0.013, HR = 1.41, 95%CI = 1.08-1.86) had significant prognostic roles. After getting the three subgroups according to the subtyping results, we demonstrated that T cell gamma delta (p = 5.78e-6), B cell memory (p = 4.61e-4) and T cell CD4+ memory resting (p = 2.65e-5) had significant differences among the three groups. SIGLEC15 (P <0.01) and CD274 (P <0.05) also had statistical differences among the three subgroups.

Conclusions: Our study verified the prognostic roles of WWC1 and LATS2 in LUSC patients. Immune checkpoints-related genes SIGLEC15 and CD274 had statistical differences among the three subgroups, which may provide new perceptions on the molecular mechanisms in LUSC and maybe helpful for precisely selecting specific LUSC patients with potential immunotherapy benefits.

Keywords: CD274; LATS2; SIGLEC15; WWC1; lung squamous cell carcinoma; the Hippo pathway.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Differentially expressed genes (DEGs) in patients with lung squamous cell carcinoma (LUSC). (A) DEGs between tumor and normal tissues; (B) heatmap for DEGs in all the LUSC samples; (C) the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses for up- and down-regulated DEGs, respectively.
Figure 2
Figure 2
Mutation patterns of LUSC patients. (A) Oncoplot displays the mutational patterns of 18 hippo core genes in 97 LUSC patients; (B) the co-expression patterns of 18 hippo core genes in LUSC patients; (C) the enriched oncogenic pathways; (D) the hippo pathway in enriched oncogenic pathways.
Figure 3
Figure 3
Correlation analysis among 18 hippo core genes in LUSC patients.
Figure 4
Figure 4
Survival curves according to the expression of (A) WWC1; (B) LATS2.
Figure 5
Figure 5
The identification of prognostic factor for OS and the development of nomogram. (A) Univariate Cox analysis; (B) multivariate Cox analysis; (C) nomogram for OS in LUSC patients; (D) the calibration curves for each year.
Figure 6
Figure 6
Identification of consensus clusters according to the expression similarity of hippo core genes. (A) Cumulative distribution function (CDF)(k = 2–6); (B) Relative change in area under CDF curve (k = 2–6); (C) the matrix of consensus clustering (k = 3); (D) heatmap of m6A-related gene expression in different subgroups, red represents high expression while blue represents low expression.
Figure 7
Figure 7
Immune infiltration estimated by CIBERSORT algorithm. (A) Immune cell score heat map; (B) the proportions of 22 types of immune cells were shown for each LUSC patient by a histogram. *p < 0.05, **p < 0.01, ***p < 0.001.
Figure 8
Figure 8
The expression distributions of 8 immune checkpoints-related genes in LUSC subgroups. *p < 0.05, **p < 0.01.

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