Chromatin Separation Regulators Predict the Prognosis and Immune Microenvironment Estimation in Lung Adenocarcinoma

Abstract

Background: Abnormal chromosome segregation is identified to be a common hallmark of cancer. However, the specific predictive value of it in lung adenocarcinoma (LUAD) is unclear. Method: The RNA sequencing and the clinical data of LUAD were acquired from The Cancer Genome Atlas (TACG) database, and the prognosis-related genes were identified. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were carried out for functional enrichment analysis of the prognosis genes. The independent prognosis signature was determined to construct the nomogram Cox model. Unsupervised clustering analysis was performed to identify the distinguishing clusters in LUAD-samples based on the expression of chromosome segregation regulators (CSRs). The differentially expressed genes (DEGs) and the enriched biological processes and pathways between different clusters were identified. The immune environment estimation, including immune cell infiltration, HLA family genes, immune checkpoint genes, and tumor immune dysfunction and exclusion (TIDE), was assessed between the clusters. The potential small-molecular chemotherapeutics for the individual treatments were predicted via the connectivity map (CMap) database. Results: A total of 2,416 genes were determined as the prognosis-related genes in LUAD. Chromosome segregation is found to be the main bioprocess enriched by the prognostic genes. A total of 48 CSRs were found to be differentially expressed in LUAD samples and were correlated with the poor outcome in LUAD. Nine CSRs were identified as the independent prognostic signatures to construct the nomogram Cox model. The LUAD-samples were divided into two distinct clusters according to the expression of the 48 CSRs. Cell cycle and chromosome segregation regulated genes were enriched in cluster 1, while metabolism regulated genes were enriched in cluster 2. Patients in cluster 2 had a higher score of immune, stroma, and HLA family components, while those in cluster 1 had higher scores of TIDES and immune checkpoint genes. According to the hub genes highly expressed in cluster 1, 74 small-molecular chemotherapeutics were predicted to be effective for the patients at high risk. Conclusion: Our results indicate that the CSRs were correlated with the poor prognosis and the possible immunotherapy resistance in LUAD.

Keywords: bioinformatics; chromosome segregation regulators; immune environment; lung adenocarcinoma; prognostic signature.

FIGURE 1

Chromatin Separation is the main bioprocess enriched by the prognostic genes in LUAD. (A) GO enrichment of the prognostic genes in LUAD. (B) KEGG enrichment of the prognostic genes in LUAD. (C) PPI network of the 128 CSRs. (D) Venn drawing showing the co genes between the 128 CSRs and LUAD-related DEGs. The LUAD-related DEGs were genes differentially expressed in LUAD-samples compared to the normal samples. Fold change>2, P. adjust<0.05.

FIGURE 2

Construction of the cox regression model by the 48 CSRs. (A) LASSO coefficient spectrum of prognostic gene screening. (B) Stepwise Cox proportional risk regression model to screen the prognostic genes. (C) The risk score distribution and survival status of patients in the training cohort. (D) The heatmap of prognostic gene distribution in the training cohort. (E) The overall survival of high- and low-risk groups. (F) ROC analyses of the cox model for 1-, 3-, and 5-years.

FIGURE 3

Construction of nomogram model by the 9-gene prognostic signature. (A) The nomogram to predict the prognosis of LUAD-patients. (B) The calibration analysis of the nomogram predicted a probability of 1-, 3-, and 5-years survival. (C) ROC analyses of the nomogram model for 1-, 3-, and 5-years. (D) DCA result of the prognostic model. (E) Risk groups in relation to a clinical index.

FIGURE 4

Validation of the CSRs-related prognostic signature with GEO datasets. (A–E) The overall survival of the high- and the low-risk group in the five validation sets. (F–J) ROC analyses of the cox model for 1-, 3-, and 5-years in the five validation sets.

FIGURE 5

Unsupervised consensus clustering of LUAD-samples based on the 48 CSRs. (A) Consistent cumulative distribution shows the cumulative distribution function with different values of k, which is used to judge the optimal value of k. (B) Delta area map. (C) The consistency matrix of all data sets with k = 2. (D) PCA showing the LUAD-samples were divided into two distinct clusters. (E) The OS of LUAD-patients in the cluster 1 and 2. (F) The difference in TMB between the two clusters. The significance was calculated by Wilcoxon rank-sum test. ^*** p < 0.001. (G) Heatmap showing the expression pattern of the 48 CSRs in the two clusters. (H) The 48 CSRs were all highly expressed in cluster 1. The significance was calculated by Wilcoxon rank-sum test. ^*** p < 0.001.

FIGURE 6

DEGs and the functional analysis between the two CSRs-related clusters. (A) Volcano Plot showing the DEGs in cluster 1 vs. cluster 2. (B) Heatmap showing the DEGs in cluster 1 vs. cluster 2. (C) KEGG enrichment of up-regulated genes in cluster 1. D GO enrichment of up-regulated genes in cluster 1. (E) KEGG enrichment of down-regulated genes in cluster 1. (F) GO enrichment of down-regulated genes in cluster 1.

FIGURE 7

Identification of hub-genes between the distinct clusters. (A) PPI network of the DEGs between the two clusters. The red represents the up-regulated genes, and the blue represents the down-regulated genes. (B) The network of the top 50 hub genes. The color depth represents the rank of the genes. (C) GO enrichment of the hub genes, which was performed by the Metascape database (https://metascape.org/gp/index).

FIGURE 8

CSRs are associated with immunization checkpoint block in LUAD. (A) The risk score in the two clusters. (B) The TIDE score in the two clusters. (C) The immune checkpoint genes in the two clusters. (D) The HLA family genes in the two clusters. The statistical significance was calculated via Wilcoxon rank-sum test, ^*** p < 0.001, ^** p < 0.01, ^* p < 0.05.

FIGURE 9

CSRs are associated with immune characteristics of LUAD. (A–D) Comparison of estimate score (A), immune score (B), stromal score (C), and tumor purity (D) in the two clusters. (E) Heatmap showing the 22 types of immune cells infiltration in the two clusters by CIBERSORT algorithm. (F) Heatmap showing the 27 types of immune cells infiltration in the two clusters by xCell algorithm. The red label represents the cells that were highly infiltrated in cluster 2, the blue label represents the cells that were highly infiltrated in cluster 1. The statistical significance was calculated via Wilcoxon rank-sum test, ^*** p < 0.001, ^** p < 0.01, ^* p < 0.05. (G) The difference in infiltrated abundance of immune cells between the two risk groups. The infiltrated abundance of immune cells was calculated by CIBERSORT algorithm. The statistical significance was calculated via Wilcoxon rank-sum test, ^*** p < 0.001, ^** p < 0.01, ^* p < 0.05.

FIGURE 10

The small-molecular chemotherapeutics forecast for patients with high-risk based on the hub genes. The blue label represents the most outstanding small-molecular perturbagens.