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. 2024 Sep 20:17:6583-6602.
doi: 10.2147/JIR.S475088. eCollection 2024.

Involvement of ICAM5 in Carcinostasis Effects on LUAD Based on the ROS1-Related Prognostic Model

Baoliang Liu #  1 , Haotian Zheng #  1 , Guoyuan Ma  1   2 , Hongchang Shen  3 , Zhaofei Pang  3 , Gemu Huang  4 , Qingtao Song  4 , Guanghui Wang  1   2 , Jiajun Du  1   2
Affiliations

Involvement of ICAM5 in Carcinostasis Effects on LUAD Based on the ROS1-Related Prognostic Model

Baoliang Liu et al. J Inflamm Res. .

Abstract

Background: Lung cancer is the most common type of cancer in the world. In lung adenocarcinoma (LUAD), studies on receptor tyrosine kinase ROS proto-oncogene 1 (ROS1) have mainly focused on the oncogenic effects of its fusion mutations, whereas ROS1 has been reported to be aberrantly expressed in a variety of cancers and can extensively regulate the growth, survival, and proliferation of tumor cells through multiple signaling pathways. The comprehensive analysis of ROS1 expression has not been fully investigated regarding its predictive value for LUAD patients.

Methods: Gene expression profiles collected from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were used to build and validate prognostic risk models. The association of ROS1 with overall survival and the immune landscape was obtained from the Tumor Immune Estimation Resource (TIMER) database. The following analyses were performed using the R package to determine the model's validity: pathway dysregulation analysis, gene set enrichment analysis, Gene Oncology analysis, immune invasion analysis, chemotherapy, radiotherapy, and immunotherapy sensitivity analysis. Finally, we conducted a pan-cancer analysis and performed in vitro experiments to explore the regulatory role of intercellular adhesion molecule 5 (ICAM5) in the progression of LUAD.

Results: We constructed a 17-gene model that categorized patients into two risk groups. The model had predictive accuracy for tumor prognosis and was specific for patients with high ROS1 expression. Comprehensive analysis showed that patients in the high-risk group were characterized by marked dysregulation of multiple pathways (eg, unfolded protein response), immune suppression of the tumor microenvironment, and poor benefit from immunotherapy and radiotherapy compared with patients in the low-risk group. PLX4720 may be a suitable treatment for the high-risk patient population. The ICAM5 gene has been demonstrated to inhibit the proliferation, cell cycle, invasion, and migration of LUAD cells.

Conclusion: We constructed a 17-gene prognostic risk model and found differences in immune-related cells, biological processes, and prognosis among patients in different risk groups based on the correlation between ROS1 and immunity. Personalized therapy may play an essential role in treatment. We further investigated the role of ICAM5 in inhibiting the malignant bioactivity of LUAD cells.

Keywords: ICAM5; LUAD; ROS1; immune; prognostic model.

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Conflict of interest statement

Gemu Huang and Qingtao Song are affiliated with Amoy Diagnostics Co., LTD. The authors declare that they have no other competing interests in this work.

Figures

Figure 1
Figure 1
The overall study design and workflow.
Figure 2
Figure 2
From the TIMER database, (A) differential expression of ROS1 gene, (B) relationship between ROS1 expression in different tumors and patient prognosis (blue indicates decreased risk and red indicates increased risk). In the TCGA cohort, (C) correlation between ROS1 expression and immune score, (D) ssGSEA analysis result, and (E) ESTIMATE analysis results of H and L groups. *0.01<p<0.05; **0.001<p<0.01; ***0.0001<p<0.001; ****p<0.0001; ns, not significant.
Figure 3
Figure 3
(A) Differentially expressed mRNA between the H and L groups. In the H group: (B) The risk score distribution, OS status, and heat map of the 17 gene-based prognostic risk model. (C) KM survival curves grouped by the median risk score. (D) The ROC curve analysis of the prognostic risk model for predicting OS. (E) ROC curves of the risk score and clinical-pathological factors with OS. (F) Uni-Cox and multi-Cox regression analyses of clinical characteristics and risk score with OS. (G) Nomogram of patients with LUAD.
Figure 4
Figure 4
(A) An overview of the association between risk score and PDS (the correlation coefficient and significance are determined using Spearman correlation.) and the distribution of clinical phenotypes in different risk groups. (B and C) Go enrichment analysis showed the BP (biological processes) and CC (cellular components) of upregulated genes and downregulated genes between the high-risk score group and the low-risk score group. (D) Differences in pathway activities scored by GSEA between high- and low-risk patients. *0.01<p<0.05; ***0.0001<p<0.001; ****p<0.0001.
Figure 5
Figure 5
(A) Boxplots of ssGSEA. (B) Boxplots of MCP-counter. (C) HLA-related gene expression level in high- and low-risk score group patients. (D) Boxplots of immune checkpoints and cytokines or their ligands. (E) Spearman correlation analysis between risk score and CD8+ T cells; risk score and CXCR6 expression; and CXCR6 and CD8+ T cells. *0.01<p<0.05; **0.001<p<0.01; ***0.0001<p<0.001; ****p<0.0001; ns, not significant.
Figure 6
Figure 6
Boxplot of ssGSEA scores for (A) injury repair responses and (B) radiation responses between the high- and low-risk score groups. (C) A box plot of susceptibility scores for ten chemotherapy drugs in two groups. (D) Boxplot of TIDE score between high- and low-risk score groups. *0.01<p<0.05; ****p<0.0001.
Figure 7
Figure 7
(A) KM survival curves in the L group in the TCGA cohort and (C) in the L group in the GEO cohort. (B) The ROC curve analysis of the prognostic risk model for predicting OS in the L group in the TCGA cohort. (D) Uni-Cox, and (E) multi-Cox regression analyses of clinical characteristics and risk score with OS in the L group in the TCGA cohort. (F) ROC curves of the risk score and clinical-pathological factors with OS.
Figure 8
Figure 8
(A) Expression of ICAM5 in normal and tumor tissues of pan-cancer cohorts. (B) IHC staining images of ICAM5 in normal lung tissue and LUAD. (C) Differences in HLA genes expression in LUAD cohort. (D) Differences in immunostimulatory factors expression in LUAD cohort. (E) Differences in the results of the ESTIMATE algorithm in pan-cancer cohort. (F) Forest plot demonstrating the effect of ICAM5 expression in LUAD on OS, DSS, PFI and DFI. (G) KM survival analysis of OS in LUAD. (H, I) Enrichment plots from GSEA in the high-ICAM5 expression group and low-ICAM5 expression group. *0.01<p<0.05; **0.001<p<0.01; ***0.0001<p<0.001; ****p<0.0001; ns, not significant.
Figure 9
Figure 9
(A, B) SRB and (C-F) EDU assay detected the proliferative capacity of A549 and PC9 LUAD cells. *0.01<p<0.05; **0.001<p<0.01; ***p<0.001.
Figure 10
Figure 10
Migration and invasion of cells were detected employing the (A-D) Transwell assay and (E, F) wound healing assay in A549 and PC9 LUAD cells. *0.01<p<0.05; **0.001<p<0.01; ***p<0.001.
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Shandong Provincial Natural Science Foundation [Grant number ZR2021MH192].

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