Background
Soft tissue sarcomas (STSs) are a rare group of heterogeneous malignant tumors originating from mesenchymal tissue and comprise more than 50 histological subtypes [
1,
2]. Although STSs only account for 1% of all malignancies, they account for approximately 10% of malignancies in children and adolescents [
3,
4]. According to previous investigations, the total STS incidence is 2.49 – 5.87 per 100,000 person-years, and the 5-year survival rate after diagnosis is 55.5% –56.5% [
5‐
7]. However, for advanced STS patients, the 5-year survival rate dramatically decreases to 27.2% [
5]. In addition, 40% – 50% of STS patients develop distant metastases [
8], which makes it difficult to select the most appropriate treatment, such as surgery, chemotherapy, or radiotherapy. Therefore, it is crucial to find accurate biomarkers for assessing risk in STS patients.
In recent years, several prognostic signatures based on lncRNA, miRNA, and plasmacytoma variant translocation 1 have been established for STS [
9‐
11]. Nevertheless, these markers have been unable to be translated into clinical practice due to their poor prognostic ability and lack of validation. The role of immune-related features in malignancies has been a recent area of active research. Elements of the immune system have proven to be strong factors for tumorigenesis and tumor progression [
12]. More importantly, previous studies have indicated that immune-related genes (IRGs) can serve as effective prognostic biomarkers of many tumors, such as lung cancer [
13,
14], ovarian cancer [
15], hepatocellular carcinoma [
16], head and neck squamous cell carcinoma [
17], papillary thyroid cancer [
18], bladder urothelial carcinoma [
19], and renal cancer [
20]. However, the prognostic significance of IRGs in STS remains unclear.
Here, we performed a systematic analysis of IRGs in STS and determined STS-related IRGs. The potential function and underlying regulatory mechanisms of these IRGs were also investigated. Furthermore, the integration of clinicopathological data and RNA-sequencing data provides novel insights into the prognostic value of IRGs. Finally, we discerned distinct clusters of STSs based on IRGs and investigated the association between IRG-based clusters and immune checkpoints, the tumor microenvironment (TME), and immune cells. The present study reveals a complex immune landscape consisting of both a continuous spectrum and discrete clusters across STS patients.
Discussion
STSs are a rare group of highly heterogeneous cancers with a high rate of metastasis of up to 40–50% [
8]. More than 50 different histological types with distinct clinical outcomes and biological behaviors complicate the prognostic prediction for STS patients [
1,
2]. Hence, the present challenge was to identify precise biomarkers for prognosis assessment and targeted therapy in STS patients. In this investigation, we revealed insights into the role of IRGs in STS patients. A total of 364 DEIRGs were identified as candidate prognostic biomarkers, and functional annotations identified the potential mechanisms of these DEIRGs. Additionally, DEIRG-based clusters identified by unsupervised consensus analysis revealed that DEIRGs presented discernable patterns in STS and had significant associations with immune features. Importantly, we established an OS-prognostic signature based on 11 key DEIRGs and a PFS-prognostic signature based on nine DEIRGs. Both of these DEIRG-based signatures were successfully validated in an independent validation set. The robustness of these two models was supported by the significant associations found between the risk score, levels of immune cell infiltration, and the expression levels of immune checkpoints. In addition, two comprehensive nomograms incorporating the DEIRG-based prognostic model and clinical parameters were constructed to improve the clinical application. By entering the score of each parameter, these nomograms may enable clinicians to estimate the OS and PFS for each STS patient. Finally, 14 OS-related DETFs and 14 PFS-related DETFs were selected, and two TF-IRG regulatory networks were generated to illustrate the relationship between prognostic TFs and IRGs.
Remarkable advances in our understanding of the tumor microenvironment and the immune system have resulted in significant breakthroughs in cancer immunotherapy [
30,
31]. Novel immune infiltrate-based classification of sarcoma identified by integrating immune cell populations and tumor cell characteristics has shown promising prognostic ability [
32]. IRG expression is connected to the immune infiltration level, key gene mutations, and chemosensitivity [
33‐
35]. IRGs have been identified as effective prognostic biomarkers in ovarian cancer [
36], non-squamous non-small cell lung cancer [
37], and renal papillary cell carcinoma [
38]. To the best of our knowledge, this is the first study to combine the entire set of IRGs with STS data from the perspective of OS and PFS. Our findings may greatly improve the precise classification and individual treatments of STS patients.
We first selected 364 DEIRGs and 83 DETFs from 259 STS patients and 911 normal tissue samples. Enrichment analysis revealed that the DEIRGs were primarily involved in leukocyte migration, the immune response-regulating cell surface receptor signaling pathway, and the immune response-activating cell surface receptor signaling pathway, the dysregulation of which are key factors in tumor initiation and development [
39,
40]. Cell surface receptors have long been considered to be significant at all stages of tumorigenesis, and the combined participation of integrins and MMPs is required for the invasion of tumor cells into surrounding tissues and metastasis [
39,
41]. The KEGG pathway analysis provided additional evidence that the associations between DEIRGs may have clinical application potential in cancer. It has been estimated that Epstein-Barr virus infection is associated with approximately 200,000 malignancies each year [
42], and EBS appears to dysregulate the expression of TCL1-family genes, leading to several typical lymphocyte cancers [
43]. Th17 cells are a subset of CD4 + T cells, and high levels of tumor-infiltrating Th17 cells are correlated with lymph node metastases and have a negative impact on the postoperative survival of cancer patients [
44].
To be the best of our knowledge, this is the first study to perform a systematic analysis of IRG-based clustering of STS. Four clusters were identified in our research. The TME score, immune checkpoints, and immune cells were confirmed to be unevenly distributed among the four clusters. As the worst-prognosis cluster, C3 had the lowest immune score, lowest stromal score, and several immune checkpoints. Although immunotherapy has been widely studied in lung cancer [
45], gastrointestinal cancer [
46,
47], melanoma [
48,
49], and renal cancer [
50], its application in STS has received little research attention. This might be attributed to the unclear role of immune checkpoints in STS patients. Although our research indicated that the cluster with the lowest PD-L1 expression level showed a worse prognosis, and the same conclusion was observed at the protein level [
51], some studies have come to the opposite conclusion [
52,
53]. For CTLA4, LAG3, and BTLA, some level of correlation between the expression level and cluster was also observed. For precision medicine, a further study based on a larger cohort with better controls should be performed to clearly elucidate the role of immune checkpoints in STS patients.
In this study, the prognostic value of DEIRGs was also investigated. An OS-prognostic signature based on 11 DEIRGs and a PFS-prognostic signature based on nine DEIRGs were constructed and then successfully validated in an independent set. The differences in OS and PFS status between patients with low and high scores were notable in the two sets. The time-dependent AUCs indicated that the models performed well in predicting the prognosis of STS patients. Among the DEIRGs included in the two prognostic models, ADM and SECTM1 were found to be associated with both OS and PFS, which may have great clinical significance. ADM, a vasodilating peptide known as a regulator in the pathophysiology of cardiovascular disease, was recently found to have the ability to promote the growth of subcutaneously transplanted sarcoma 180 tumor cells, and ADM inhibitors were shown to be useful for the management of sarcoma growth [
54]. In addition, as a receptor regulatory protein of AMD, the overexpression of RMAMP2 suppressed the adhesion of sarcoma cells to endothelial cells and metastasis via vascular integrity [
55]. Interestingly, the MYH11-ADM regulatory network has an important role in STS patients. Both proteins were significantly associated with OS and PFS in STS patients, and there is likely an important connection between them. Although details of this relationship within the tumor are unclear, our study lays the foundation for future research directions. Importantly, although both OS and PFS were studied, PFS is not a good surrogate for OS for patients who receive immunotherapy [
56]. Given the rapid development of therapeutics in oncologic research, the assessment of both outcomes (OS and PFS) is essential for clinical and policy decision-making.
Regarding the other overlapping prognostic DEIRG, SECTM1 is often referred to as a ligand of CD7 and has rarely been studied. Recently, Wang et al. [
57] showed that SECTM1 produced by tumor cells could bind to CD7 and significantly promote monocyte migration by activating the PI3K pathway, which plays essential roles in tumor progression. Although an association between SECTM1 and STS has not yet been identified, our study provides insight into the tumor-associated immune mechanisms of STS, and the overexpression of SECTM1 may be important in STS development. Several genes were included in the OS-prognostic signature and the PFS-prognostic signature. All of them were confirmed to be relevant to the pathogenesis and prognosis of sarcoma. For example, Chung et al. [
58] developed a polyclonal antibody against ILK and found that ILK expression was observed in Ewing’s sarcoma (ES, 100%), which indicated that ILK may be a specific and sensitive immunohistochemical marker for ES. In addition, Et-1 was shown to increase the expression of VEGF and angiogenesis via ILK, resulting in the migration and tube formation of chondrosarcoma cells [
59]. Moreover, the expression of RAF1, a part of the MAPK/ERK pathway, is related to cell proliferation in osteosarcoma [
60]. Hicks et al. [
61] revealed a novel MTAP-RAF1 fusion in a 51-year-old sarcoma patient. Metalloproteases-9 (MMP9) is secreted by metastatic cells and was shown to be highly associated with ES invasion and metastasis [
62], and the expression and distribution of MMP9 are related to the occurrence of metastasis and clinical outcomes in STS patients [
63]. Hence, the regulation of these IRGs may represent a significant breakthrough in tumor immunotherapy, as the immune system plays a crucial role in the occurrence and progression of cancer [
12]. The potential mechanisms by which these genes are involved in sarcoma require further clarification through experimental research.
We also constructed two comprehensive nomograms with satisfactory AUCs (OS: 0.832–0.926, PFS: 0.776–0.874) based on independent variables to assess the deterioration and survival of patients. To date, numerous studies have developed signatures based on sequencing data to stratify sarcoma patients, including CINSARC [
64], alternative splicing events [
65], relapse-related genes [
66], and lncRNA [
67]. However, none of them has been applied in clinical practice. Moreover, the use of specific biomarkers with a limited sample size to generate a risk score, which easily leads to overfitting, has no link to clinical reality. However, our prognostic models combining DEGs (which have a significant association with OS and PFS) and IRGs (the expression of which is strongly connected with immune infiltration and tumor progression) is essentially more generic than normal signatures. Therefore, our two nomograms based on the DEIRG-based prognostic signatures and clinicopathologic data can improve the assessment of risk in STS patients.
Some limitations of this study should be noted. First, the training and validation sets came from a retrospective study, which has an inherent bias, and some valuable variables were unavailable. Second, although the signatures were validated by an independent validation set, the prognostic ability in other ethnic groups remains unclear. Finally, the present study is a bioinformatic analysis, and the potential functional mechanisms of IRGs were not studied. Hence, further cell and animal studies should be performed to clearly elucidate the role of IRGs in STS.
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