Comprehensive analysis of prognostic and immunological role of basement membrane-related genes in soft tissue sarcoma

Abstract

Background: Soft tissue sarcoma (STS) represents highly multifarious malignant tumors that often occur in adolescents and have a poor prognosis. The basement membrane, as an ancient cellular matrix, was recently proven to play a vital role in developing abundant tumors. The relationship between basement membrane-related genes and STS remains unknown.

Methods: Consensus clustering was employed to identify subgroups related to differentially expressed basement membrane-related genes. Cox and least absolute shrinkage and selection operator regression analyses were utilized to construct this novel signature. Then, we established a nomogram and calibration curve, including the risk score and available clinical characteristics. Finally, we carried out functional enrichment analysis and immune microenvironment analysis to investigate enriched pathways and the tumor immune microenvironment related to the novel signature.

Results: A prognostic predictive signature consisting of eight basement membrane-related genes was established. Kaplan-Meier survival curves demonstrated that the patients in the high-risk group had a poor prognosis. Independent analysis illustrated that this risk model could be an independent prognostic predictor. We validated the accuracy of our signature in the validation data set. In addition, gene set enrichment analysis and immune microenvironment analysis showed that patients with low-risk scores were enriched in some pathways associated with immunity. Finally, in vitro experiments showed significantly differential expression levels of these signature genes in STS cells and PSAT1 could promote the malignant behavior of STS.

Conclusions: The novel signature is a promising prognostic predictor for STS. The present study may improve the prognosis and enhance individualized treatment for STS in the future.

Keywords: basement membrane; prognosis; signature; soft tissue sarcoma; tumor microenvironment.

Figure 1

Genetic and transcriptional alterations of BMRGs in STS. (A) Expression distributions of BMRGs between normal and STS tissues. (B) Volcano map of the expression of genes. (C) Locations of CNV alterations in BMRGs on 23 chromosomes. (D) Frequencies of CNV gain, loss, and non‐CNV among BMRGs. BMRGs, basement membrane‐related genes; CNV, copy number variant; STS, soft tissue sarcoma; TCGA, The Cancer Genome Atlas.

Figure 2

BMRG subtypes and clinicopathological and biological characteristics of two distinct subtypes of samples divided by consistent clustering. (A) Interactions among BMRGs in STS. The line connecting the BMRGs represents their interaction, with the line thickness indicating the strength of the association between BMRGs. Green and pink represent negative and pink positive correlations, respectively. (B) Consensus matrix heatmap defining two clusters (k = 2) and their correlation area. (C) PCA analysis showing a remarkable difference in transcriptomes between the two subtypes. (D) Kaplan–Meier curves for OS of two distinct molecular subtypes (log‐rank tests, p = .153). (E) Differences in clinicopathologic characteristics and expression levels of BMRGs between the two distinct subtypes. BMRGs, basement membrane‐related genes; OS, overall survival; PCA, principal components analysis; STS, soft tissue sarcoma.

Figure 3

Correlations of tumor immune cell microenvironments and two STS subtypes. (A) GSVA of biological pathways between two distinct subtypes, in which red and blue represent activated and inhibited pathways, respectively. (B) The abundance of 22 infiltrating immune cell types in the two STS subtypes. GSVA, gene set variation analysis; STS, soft tissue sarcoma.

Figure 4

Identification of gene subtypes based on DEGs. (A) GO enrichment analysis of differentially expressed basement membrane cluster‐related genes. (B) KEGG enrichment analysis of differentially expressed basement membrane cluster‐related genes. (C). Kaplan–Meier curves for OS of the two gene subtypes (log‐rank tests, p < .05). (D) Relationships between clinicopathologic characteristics and the two gene subtypes. (E) Differences in the expression of BMRGs among the two gene subtypes. BMRGs, basement membrane‐related genes; DEG, differentially expressed gene; GO, gene ontology; KEGG, Kyoto Encyclopedia and Genomes; OS, overall survival.

Figure 5

Construction of the risk score in the training set. (A) Alluvial diagram of subtype distributions in groups with different risk scores and survival outcomes. (B) Differences in risk score between gene subtypes. (C) Differences in risk score between the two subtypes. (D) Ranked dot and scatter plots showing the risk score distribution and patient survival status. (E) Kaplan–Meier analysis of the OS between the two groups. (F) ROC curves to predict the sensitivity and specificity of 1‐, 3‐, and 5‐survival according to the risk score. (G) Expression level of 8 signature‐related genes. OS, overall survival; ROC, receiver operating characteristic.

Figure 6

Evaluation of the TME and checkpoints between the two groups. (A) Correlations between risk score and immune cell types. (B) Correlations between risk score and immune, stromal, and ESTIMATE scores. (C) Correlations between the abundance of immune cells and eight genes in the proposed model. (D) Expression of immune checkpoints in the high and low‐risk groups. (E) The violin plot showed the different proportions of tumor‐infiltrating cells between the high‐risk and low‐risk groups. TME, tumor microenvironment.

Figure 7

Comprehensive analysis of the risk score in STS. (A) Relationships between risk score and CSC index. (B) TMB in different risk score groups. (C) Spearman correlation analysis of the risk score and TMB. (D and E) The waterfall plot of somatic mutation features established with high and low risk scores. (F–K) Relationships between risk score and chemotherapeutic sensitivity. CSC, cancer stem cell; STS, soft tissue sarcoma; TMB, tumor mutation burden.

Figure 8

Construction and validation of a nomogram. (A) Nomogram for predicting the 1‐, 3‐, and 5‐ year OS of STS patients in the training set. (B) ROC curves for the 1‐, 3‐, and 5‐year predicted survival nomogram. (C) Calibration curve for the 1‐, 3‐, and 5‐year predicted survival nomogram. OS, overall survival; ROC, receiver operating characteristic; STS, soft tissue sarcoma.

Figure 9

Evaluation of the expression of these eight signature BMRGs in STS cell lines. (A) PSAT1. (B) GREM2. (C) KCND3. (D) HMGA1. (E) SOX11. (F) C1S. (G) ZNF385A. (H) PRF1. *p < .05, **p < .01, ***p < .001, ****p < .0001. BMRG, basement membrane‐related genes; STS, soft tissue sarcoma.

Figure 10

In vitro validation. (A) PCR validation of PSAT1. (B) CCK‐8 assay. (C) Colony formation assay in SYO‐1. (D) Colony formation assay in SW982. (E) Wound healing assay in SW982. (F) Wound healing assay in SYO‐1. CCK‐8, cell count kit‐8.