Comprehensive profiling of immune-related genes in soft tissue sarcoma patients

Prognostic role of MUCIN family and its relationship with immune characteristics and tumor biology in diffuse-type gastric cancer

The main component of O-glycoproteins, mucin, is known to play important roles in physiological conditions and oncogenic processes, particularly correlated with poor prognosis in different carcinomas. Diffuse-type gastric cancer (DGC) has long been associated with genomic stability and unfavorable clinical outcomes. To investigate further, we obtained clinical information and the RNA-seq data of the TCGA-STAD cohort. Through the use of unsupervised clustering methods and GSEA, we identified two distinct clusters, characterized by higher and lower expression of MUC2 and MUC20, denoted as cluster 1 and cluster 2, respectively. Subsequently, employing CIBERSORT, it was determined that cluster 2 exhibited a higher tumor mutation burden (TMB) and a greater abundance of CD8+ T cells and activated CD4+ memory T cells, in addition to immune checkpoints (ICPs). On the other hand, cluster 1 showed a lower TIDE score estimation, indicating a higher probability of tumor immune escape. Furthermore, overexpression of MUC15 and MUC20 was confirmed through qPCR and Western blotting, and their specific roles in mediating the epithelial-mesenchymal transition (EMT) process of GC cells (SNU484 and Hs746t) were validated via CCK-8 assay and wound healing assay in vitro. These findings highlight the potential prognostic value of MUC20 and offer insights into the prospects of immunotherapy for DGC by targeting MUC20.

Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework

Lung adenocarcinoma (LUAD) is a malignant tumor with high lethality, and the aim of this study was to identify promising biomarkers for LUAD. Using the TCGA-LUAD dataset as a discovery cohort, a novel joint framework VAEjMLP based on variational autoencoder (VAE) and multilayer perceptron (MLP) was proposed. And the Shapley Additive Explanations (SHAP) method was introduced to evaluate the contribution of feature genes to the classification decision, which helped us to develop a biologically meaningful biomarker potential scoring algorithm. Nineteen potential biomarkers for LUAD were identified, which were involved in the regulation of immune and metabolic functions in LUAD. A prognostic risk model for LUAD was constructed by the biomarkers HLA-DRB1, SCGB1A1, and HLA-DRB5 screened by Cox regression analysis, dividing the patients into high-risk and low-risk groups. The prognostic risk model was validated with external datasets. The low-risk group was characterized by enrichment of immune pathways and higher immune infiltration compared to the high-risk group. While, the high-risk group was accompanied by an increase in metabolic pathway activity. There were significant differences between the high- and low-risk groups in metabolic reprogramming of aerobic glycolysis, amino acids, and lipids, as well as in angiogenic activity, epithelial-mesenchymal transition, tumorigenic cytokines, and inflammatory response. Furthermore, high-risk patients were more sensitive to Afatinib, Gefitinib, and Gemcitabine as predicted by the pRRophetic algorithm. This study provides prognostic signatures capable of revealing the immune and metabolic landscapes for LUAD, and may shed light on the identification of other cancer biomarkers.

Immune Cells in the Tumor Microenvironment of Soft Tissue Sarcomas

Soft tissue sarcomas (STSs) are a rare heterogeneous group of malignant neoplasms characterized by their aggressive course and poor response to treatment. This determines the relevance of research aimed at studying the pathogenesis of STSs. By now, it is known that STSs is characterized by complex relationships between the tumor cells and immune cells of the microenvironment. Dynamic interactions between tumor cells and components of the microenvironment enhance adaptation to changing environmental conditions, which provides the high aggressive potential of STSs and resistance to antitumor therapy. Today, active research is being conducted to find effective antitumor drugs and to evaluate the possibility of using therapy with immune cells of STS. The difficulty in assessing the efficacy of new antitumor options is primarily due to the high heterogeneity of this group of malignant neoplasms. Studying the role of immune cells in the microenvironment in the progression STSs and resistance to antitumor therapies will provide the discovery of new biomarkers of the disease and the prediction of response to immunotherapy. In addition, it will help to initially divide patients into subgroups of good and poor response to immunotherapy, thus avoiding wasting precious time in selecting the appropriate antitumor agent.

Leveraging a disulfidptosis‑related lncRNAs signature for predicting the prognosis and immunotherapy of glioma

BACKGROUND

Gliomas, a prevalent form of primary brain tumors, are linked with a high mortality rate and unfavorable prognoses. Disulfidptosis, an innovative form of programmed cell death, has received scant attention concerning disulfidptosis-related lncRNAs (DRLs). The objective of this investigation was to ascertain a prognostic signature utilizing DRLs to forecast the prognosis and treatment targets of glioma patients.

METHODS

RNA-seq data were procured from The Cancer Genome Atlas database. Disulfidptosis-related genes were compiled from prior research. An analysis of multivariate Cox regression and the least absolute selection operator was used to construct a risk model using six DRLs. The risk signature's performance was evaluated via Kaplan-Meier survival curves and receiver operating characteristic curves. Additionally, functional analysis was carried out using GO, KEGG, and single-sample GSEA to investigate the biological functions and immune infiltration. The research also evaluated tumor mutational burden, therapeutic drug sensitivity, and consensus cluster analysis. Reverse transcription quantitative PCR was conducted to validate the expression level of DRLs.

RESULTS

A prognostic signature comprising six DRLs was developed to predict the prognosis of glioma patients. High-risk patients had significantly shorter overall survival than low-risk patients. The robustness of the risk model was validated by receiver operating characteristic curves and subgroup survival analysis. Risk model was used independently as a prognostic indicator for the glioma patients. Notably, the low-risk patients displayed a substantial decrease in the immune checkpoints, the proportion of immune cells, ESTIMATE and immune score. IC50 values from the different risk groups allowed us to discern three drugs for the treatment of glioma patients. Lastly, the potential clinical significance of six DRLs was determined.

CONCLUSIONS

A novel six DRLs signature was developed to predict prognosis and may provide valuable insights for patients with glioma seeking novel immunotherapy and targeted therapy.

Identification of the potential regulatory interactions in rheumatoid arthritis through a comprehensive analysis of lncRNA-related ceRNA networks

OBJECTIVE

This study aimed at constructing a network of competing endogenous RNA (ceRNA) in the synovial tissues of rheumatoid arthritis (RA). It seeks to discern potential biomarkers and explore the long non-coding RNA (lncRNA)-microRNA (miRNA)-messenger RNA (mRNA) axes that are intricately linked to the pathophysiological mechanisms underpinning RA, and providing a scientific basis for the pathogenesis and treatment of RA.

METHODS

Microarray data pertaining to RA synovial tissue, GSE103578, GSE128813, and GSE83147, were acquired from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ). Conducted to discern both differentially expressed lncRNAs (DELncRNAs) and differentially expressed genes (DEGs). A ceRNA network was obtained through key lncRNAs, key miRNAs, and key genes. Further investigations involved co-expression analyses to uncover the lncRNA-miRNA-mRNA axes contributing to the pathogenesis of RA. To delineate the immune-relevant facets of this axis, we conducted an assessment of key genes, emphasizing those with the most substantial immunological correlations, employing the GeneCards database. Finally, gene set enrichment analysis (GSEA) was executed on the identified key lncRNAs to elucidate their functional implications in RA.

RESULTS

The 2 key lncRNAs, 7 key miRNAs and 6 key genes related to the pathogenesis of RA were obtained, as well as 2 key lncRNA-miRNA-mRNA axes (KRTAP5-AS1-hsa-miR-30b-5p-PNN, XIST-hsa-miR-511-3p/hsa-miR-1277-5p-F2RL1). GSEA of two key lncRNAs obtained biological processes and signaling pathways related to RA synovial lesions.

CONCLUSION

The findings of this investigation hold promise in furnishing a foundational framework and guiding future research endeavors aimed at comprehending the etiology and therapeutic interventions for RA.

Establishment of a N1-methyladenosine-related risk signature for breast carcinoma by bioinformatics analysis and experimental validation

OBJECTIVES

Breast carcinoma (BRCA) has resulted in a huge health burden globally. N1-methyladenosine (m¹A) RNA methylation has been proven to play key roles in tumorigenesis. Nevertheless, the function of m¹A RNA methylation-related genes in BRCA is indistinct.

METHODS

The RNA sequencing (RNA-seq), copy-number variation (CNV), single-nucleotide variant (SNV), and clinical data of BRCA were acquired via The Cancer Genome Atlas (TCGA) database. In addition, the GSE20685 dataset, the external validation set, was acquired from the Gene Expression Omnibus (GEO) database. 10 m¹A RNA methylation regulators were obtained from the previous literature, and further analyzed through differential expression analysis by rank-sum test, mutation by SNV data, and mutual correlation by Pearson Correlation Analysis. Furthermore, the differentially expressed m¹A-related genes were selected through overlapping m¹A-related module genes obtained by weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs) in BRCA and DEGs between high- and low- m¹A score subgroups. The m¹A-related model genes in the risk signature were derived by univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. In addition, a nomogram was built through univariate and multivariate Cox analyses. After that, the immune infiltration between the high- and low-risk groups was investigated through ESTIMATE and CIBERSORT. Finally, the expression trends of model genes in clinical BRCA samples were further confirmed by quantitative real-time PCR (RT‒qPCR).

RESULTS

Eighty-five differentially expressed m¹A-related genes were obtained. Among them, six genes were selected as prognostic biomarkers to build the risk model. The validation results of the risk model showed that its prediction was reliable. In addition, Cox independent prognosis analysis revealed that age, risk score, and stage were independent prognostic factors for BRCA. Moreover, 13 types of immune cells were different between the high- and low-risk groups and the immune checkpoint molecules TIGIT, IDO1, LAG3, ICOS, PDCD1LG2, PDCD1, CD27, and CD274 were significantly different between the two risk groups. Ultimately, RT-qPCR results confirmed that the model genes MEOX1, COL17A1, FREM1, TNN, and SLIT3 were significantly up-regulated in BRCA tissues versus normal tissues.

CONCLUSIONS

An m¹A RNA methylation regulator-related prognostic model was constructed, and a nomogram based on the prognostic model was constructed to provide a theoretical reference for individual counseling and clinical preventive intervention in BRCA.

The Landscape of Immunotherapy for Retroperitoneal Sarcoma

Significant multidisciplinary scientific effort has been undertaken to understand the heterogeneous family of neoplasms that comprise soft tissue sarcomas. Within this family of neoplasms, outcomes for retroperitoneal sarcomas (RPS) are currently limited given a lack of effective therapies. In this review, we focus on immunotherapy and its relationship with the common RPS histologic subtypes. Although initial outcomes for RPS patients with immune checkpoint inhibition alone have been somewhat disappointing, subsequent analyses on histologies, the tumor microenvironment, sarcoma immune class, tumor infiltrating lymphocytes and genetic analysis for tumor mutational burden have yielded insight into the interplay between sarcomas and immunotherapy. Such approaches have all provided critical insight into the environment and characterization of these tumors, with targets for potential immunotherapy in future clinical trials. With this insight, molecularly tailored combination treatments for improving response rates and oncologic outcomes for RPS are promising.

Immune Biomarkers in Blood from Sarcoma Patients: A Pilot Study

The main role of the host immune system is to identify and eliminate cancer cells, which is a complex process, but it is not a fail-safe mechanism. Many sarcoma patients succumb to this disease despite treatments rendered. The aim of this pilot study was to compare the levels of CD4+ T-cells, T-regulatory (Treg) cells, and cytokines such as tumor necrosis factor-alpha (TNF-α), interferon-gamma (IFN-γ), interleukin-17A (IL-17A), and transforming growth factor-beta-1 (TGF-β1) in peripheral blood leukocytes of sarcoma patients and healthy controls. For gene expression studies, total ribonucleic acid (RNA) was extracted from peripheral blood leukocytes and genes that were differentially regulated in peripheral blood leukocytes of sarcoma patients compared with healthy controls were determined using a commercial T-helper cell differentiation quantitative polymerase chain reaction (qPCR) array. Flow cytometer analysis was performed on blood samples from 26 sarcoma patients and 10 healthy controls to identify the levels of CD4+ T-cells and T-reg cells. The level of cytokines in plasma and culture supernatant were quantified using commercial enzyme-linked immunosorbent assay (ELISA) kits. A marked reduction in the percentage of CD4+ T-cells (p = 0.037) and levels of TNF-α (p = 0.004) and IFN-γ (0.010) was observed in sarcoma patients. Gene expression analysis showed five genes (homeobox A10 (HOXA10), GATA binding protein 3 (GATA3), prostaglandin D2 receptor 2 (PTGDR2), thymocyte selection associated high mobility group box (TOX), and C-C motif chemokine receptor 3 (CCR3)) were dysregulated (p < 0.05) in sarcoma patients. This study suggests that T-helper-1 immune responses are reduced in sarcoma patients.

Immunotherapy in Sarcoma: Where Do Things Stand?

Early experiences with modern immunotherapy have been disappointing in trials of unselected sarcoma subtypes. However, remarkable efficacy has been observed with immune checkpoint inhibitors (ICIs) in a subset of patients, with the most promising outcomes to date in alveolar soft part sarcoma, cutaneous angiosarcoma, undifferentiated pleomorphic sarcoma (UPS), and dedifferentiated liposarcoma (dLPS). Adoptive cellular therapies targeting cancer testis antigens have shown promising activity, but only synovial sarcoma (SS) and myxoid/round cell liposarcomas reliably express these targets. The majority of sarcomas are immunologically "cold" with sparse immune infiltration, which may explain the poor response to immunotherapy. Current immunotherapy trials for sarcomas explore combination therapies with checkpoint inhibitors to overcome immune evasion and novel targets in adoptive cellular therapies. The role of tertiary lymphoid structures, PD-L1 expression, tumor mutational burden, microsatellite instability, and tumor lymphocytes as biomarkers for response are areas of active investigation. In this review, we highlight prior and ongoing clinical efforts to improve outcomes with immunotherapy and discuss the current state of understanding for biomarkers to select patients most likely to benefit from this approach.

A Necroptosis-Related lncRNA Signature Predicts Prognosis and Indicates the Immune Microenvironment in Soft Tissue Sarcomas

Background: The necroptosis and long noncoding RNA (lncRNA) are critical in the occurrence and development of malignancy, while the association between the necroptosis-related lncRNAs (NRlncRNAs) and soft tissue sarcoma (STS) remains controversial. Therefore, the present study aims to construct a novel signature based on NRlncRNAs to predict the prognosis of STS patients and investigate its possible role.

Methods: The transcriptome data and clinical characteristics were extracted from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression database (GTEx). A novel NRlncRNA signature was established and verified by the COX regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Subsequently, the K-M survival analysis, ROC, univariate, multivariate Cox regression analysis, and nomogram were used to evaluate the predictive value of the signature. Also, a variety of bioinformatic analysis algorithms explored the differences between the potential mechanism, tumor immune status, and drug sensitivity in the two-risk group. Finally, the RT-qPCR was performed to evaluate the expression of signature NRlncRNAs.

Results: A novel signature consisting of seven NRlncRNAs was successfully established and verified with stable prediction performance and general applicability for STS. Next, the GSEA showed that the patients in the high-risk group were mainly enriched with tumor-related pathways, while the low-risk patients were significantly involved in immune-related pathways. In parallel, we found that the STS patients in the low-risk group had a better immune status than that in the high-risk group. Additionally, there were significant differences in the sensitivity to anti-tumor agents between the two groups. Finally, the RT-qPCR results indicated that these signature NRlncRNAs were abnormally expressed in STS.

Conclusion: To the best of our knowledge, it is the first study to construct an NRlncRNA signature for STS. More importantly, the novel signature displays stable value and translational potential for predicting prognosis, tumor immunogenicity, and therapeutic response in STS.

Immunologic constant of rejection signature is prognostic in soft-tissue sarcoma and refines the CINSARC signature

BACKGROUND

Soft-tissue sarcomas (STSs) are heterogeneous and aggressive tumors, with high metastatic risk. The immunologic constant of rejection (ICR) 20-gene signature is a signature of cytotoxic immune response. We hypothesized that ICR might improve the prognostic assessment of early-stage STS.

METHODS

We retrospectively applied ICR to 1455 non-metastatic STS and searched for correlations between ICR classes and clinicopathological and biological variables, including metastasis-free survival (MFS).

RESULTS

Thirty-four per cent of tumors were classified as ICR1, 27% ICR2, 24% ICR3, and 15% ICR4. These classes were associated with patients' age, pathological type, and tumor depth, and an enrichment from ICR1 to ICR4 of quantitative/qualitative scores of immune response. ICR1 class was associated with a 59% increased risk of metastatic relapse when compared with ICR2-4 class. In multivariate analysis, ICR classification remained associated with MFS, as well as pathological type and Complexity Index in Sarcomas (CINSARC) classification, suggesting independent prognostic value. A prognostic clinicogenomic model, including the three variables, was built in a learning set (n=339) and validated in an independent set (n=339), showing greater prognostic precision than each variable alone or in doublet. Finally, connectivity mapping analysis identified drug classes potentially able to reverse the expression profile of poor-prognosis tumors, such as chemotherapy and targeted therapies.

CONCLUSION

ICR signature is independently associated with postoperative MFS in early-stage STS, independently from other prognostic features, including CINSARC. We built a robust prognostic clinicogenomic model integrating ICR, CINSARC, and pathological type, and suggested differential vulnerability of each prognostic group to different systemic therapies.

Identification and Validation of a Novel Pyroptosis-Related Gene Signature for Prognosis Prediction in Soft Tissue Sarcoma

Soft tissue sarcoma (STS) represents an uncommon and heterogenous group of malignancies, and poses substantial therapeutic challenges. Pyroptosis has been demonstrated to be related with tumor progression and prognosis. Nevertheless, no studies exist that delineated the role of pyroptosis-related genes (PRGs) in STS. In the present study, we comprehensively and systematically analyzed the gene expression profiles of PRGs in STS. The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were utilized to identify differentially expressed PRGs. In total, 34 PRGs were aberrantly expressed between STS and normal tissues. Several PRGs were validated with RT-qPCR. Consensus clustering analysis based on PRGs was conducted to divide STS patients into two clusters, and significant survival difference was observed between two distinct clusters (p = 0.019). Differentially expressed genes (DEGs) were identified between pyroptosis-related clusters. Based on the least absolute shrinkage and selection operator (LASSO) COX regression analysis, the pyroptosis-related gene signature with five key DEGs was constructed. The high pyroptosis-related risk score group of TCGA cohort was characterized by poorer prognosis (p < 0.001), with immune infiltration and function significantly decreased. For external validation, STS patients from Gene Expression Omnibus (GEO) were grouped according to the same cut-off point. The survival difference between two risk groups of GEO cohort was also significant (p < 0.001). With the combination of clinical characteristics, pyroptosis-related risk score was identified to serve as an independent prognostic factor for STS patients. In conclusion, this study provided a comprehensive overview of PRGs in STS and the potential role in prognosis, which could be an important direction for future studies.

Improving Immunotherapy Efficacy in Soft-Tissue Sarcomas: A Biomarker Driven and Histotype Tailored Review

Anti-PD-(L)1 therapies yield a disappointing response rate of 15% across soft-tissue sarcomas, even if some subtypes benefit more than others. The proportions of TAMs and TILs in their tumor microenvironment are variable, and this heterogeneity correlates to histotype. Tumors with a richer CD8+ T cell, M1 macrophage, and CD20+ cells infiltrate have a better prognosis than those infiltrated by M0/M2 macrophages and a high immune checkpoint protein expression. PD-L1 and CD8+ infiltrate seem correlated to response to immune checkpoint inhibitors (ICI), but tertiary lymphoid structures have the best predictive value and have been validated prospectively. Trials for combination therapies are ongoing and focus on the association of ICI with chemotherapy, achieving encouraging results especially with pembrolizumab and doxorubicin at an early stage, or ICI with antiangiogenics. A synergy with oncolytic viruses is seen and intratumoral talimogene laherpavec yields an impressive 35% ORR when associated to pembrolizumab. Adoptive cellular therapies are also of great interest in tumors with a high expression of cancer-testis antigens (CTA), such as synovial sarcomas or myxoid round cell liposarcomas with an ORR ranging from 20 to 50%. It seems crucial to adapt the design of clinical trials to histology. Leiomyosarcomas are characterized by complex genomics but are poorly infiltrated by immune cells and do not benefit from ICI. They should be tested with PIK3CA/AKT inhibition, IDO blockade, or treatments aiming at increasing antigenicity (radiotherapy, PARP inhibitors). DDLPS are more infiltrated and have higher PD-L1 expression, but responses to ICI remain variable across clinical studies. Combinations with MDM2 antagonists or CDK4/6 inhibitors may improve responses for DDLPS. UPS harbor the highest copy number alterations (CNA) and mutation rates, with a rich immune infiltrate containing TLS. They have a promising 15-40% ORR to ICI. Trials for ICB should focus on immune-high UPS. Association of ICI with FGFR inhibitors warrants further exploration in the immune-low group of UPS. Finally translocation-related sarcomas are heterogeneous, and although synovial sarcomas a poorly infiltrated and have a poor response rate to ICI, ASPS largely benefit from ICB monotherapy or its association with antiangiogenics agents. Targeting specific neoantigens through vaccine or adoptive cellular therapies is probably the most promising approach in synovial sarcomas.

Construction of a Novel Signature and Prediction of the Immune Landscape in Soft Tissue Sarcomas Based on N6-Methylandenosine-Related LncRNAs

Background: N6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) are critically involved in cancer development. However, the roles and clinical significance of m6A-related lncRNAs in soft tissue sarcomas (STS) are inconclusive, thereby warranting further investigations.

Methods: Transcriptome profiling data were extracted from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx). Consensus clustering was employed to divide patients into clusters and Kaplan–Meier analysis was used to explore the prognostic differences between the subgroups. Gene set enrichment analysis (GSEA) was conducted to identify the biological processes and signaling pathways associated with m6A-Related lncRNAs. Finally, patients were randomly divided into training and validation cohorts, and least absolute shrinkage and selection operator (LASSO) Cox regression was conducted to establish the m6A-related lncRNA-based risk signature.

Results: A total of 259 STS patients from TCGA-SARC dataset were enrolled in our study. Thirteen m6A-Related lncRNAs were identified to be closely related to the prognosis of STS patients. Patients were divided into two clusters, and patients in cluster 2 had a better overall survival (OS) than those in cluster 1. Patients in different clusters also showed differences in immune scores, infiltrating immune cells, and immune checkpoint expression. Patients were further classified into high-risk and low-risk subgroups according to risk scores, and high-risk patients were found to have a worse prognosis. The receiver operating characteristic (ROC) curve indicated that the risk signature displayed excellent performance at predicting the prognosis of patients with STS. Further, the risk signature was remarkably connected with the immune microenvironment and chemosensitivity in STS.

Conclusion: Our study demonstrated that m6A-related lncRNAs were significantly associated with prognosis and tumor immune microenvironment and could function as independent prognosis-specific predictors in STS, thereby providing novel insights into the roles of m6A-related lncRNAs in STS.

Tumor and Peripheral Immune Status in Soft Tissue Sarcoma: Implications for Immunotherapy

Simple Summary

Soft Tissue Sarcomas are a rare and heterogeneous group of tumors, which have a characteristic complexity, leading to a difficult diagnosis and a lack of response to treatment. The aim of this review is to summarize the role of immune cells, soluble plasmatic factors, immune checkpoints; and the expression of immune-related genes predicting survival, response to therapy, and potential immunotherapeutic agents or targets in Soft Tissue Sarcomas.

Abstract

Soft Tissue Sarcomas (STS) are a heterogeneous and rare group of tumors. Immune cells, soluble factors, and immune checkpoints are key elements of the complex tumor microenvironment. Monitoring these elements could be used to predict the outcome of the disease, the response to therapy, and lead to the development of new immunotherapeutic approaches. Tumor-infiltrating B cells, Natural Killer (NK) cells, tumor-associated neutrophils (TANs), and dendritic cells (DCs) were associated with a better outcome. On the contrary, tumor-associated macrophages (TAMs) were correlated with a poor outcome. The evaluation of peripheral blood immunological status in STS could also be important and is still underexplored. The increased lymphocyte-to-monocyte ratio (LMR) and neutrophil-to-lymphocyte ratio (NLR), higher levels of monocytic myeloid-derived suppressor cells (M-MDSCs), and Tim-3 positive CD8 T cells appear to be negative prognostic markers. Meanwhile, NKG2D-positive CD8 T cells were correlated with a better outcome. Some soluble factors, such as cytokines, chemokines, growth factors, and immune checkpoints were associated with the prognosis. Similarly, the expression of immune-related genes in STS was also reviewed. Despite these efforts, only very little is known, and much research is still needed to clarify the role of the immune system in STS.

Molecular Determinants of Soft Tissue Sarcoma Immunity: Targets for Immune Intervention

Soft tissue sarcomas (STSs) are a family of rare malignant tumors encompassing more than 80 histologies. Current therapies for metastatic STS, a condition that affects roughly half of patients, have limited efficacy, making innovative therapeutic strategies urgently needed. From a molecular point of view, STSs can be classified as translocation-related and those with a heavily rearranged genotype. Although only the latter display an increased mutational burden, molecular profiles suggestive of an “immune hot” tumor microenvironment are observed across STS histologies, and response to immunotherapy has been reported in both translocation-related and genetic complex STSs. These data reinforce the notion that immunity in STSs is multifaceted and influenced by both genetic and epigenetic determinants. Cumulative evidence indicates that a fine characterization of STSs at different levels is required to identify biomarkers predictive of immunotherapy response and to discover targetable pathways to switch on the immune sensitivity of “immune cold” tumors. In this review, we will summarize recent findings on the interplay between genetic landscape, molecular profiling and immunity in STSs. Immunological and molecular features will be discussed for their prognostic value in selected STS histologies. Finally, the local and systemic immunomodulatory effects of the targeted drugs imatinib and sunitinib will be discussed.

Immune-related prognostic genes signatures in the tumor microenvironment of sarcoma

Sarcomas are a heterogeneous group of malignant mesenchymal neoplasms. This study aimed to investigate the immune-related prognostic gene signatures in the tumor microenvironment of sarcoma. The RNA-sequencing data and clinical phenotype data of 260 sarcoma samples and two normal samples were downloaded from The Cancer Genome Atla (TCGA) database. Tumor purity and immune cells infiltration were evaluated by Estimation of Stromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) deconvolution algorithm. Differentially expressed genes (DEGs) were screened in high vs. low immune score groups. Survival analysis was performed using Kaplan-Meier curve with log-rank test. Tumor infiltrating of immune cells was analyzed by Tumor Immune Estimation Resource (TIMER). High immune score and ESTIMATE score were associated with favorable prognosis. A total of 623 immune DEGs were screened. The majority of these genes (532 genes accounting for 85% of the DEGs) were up-regulated, and these genes were significantly enriched in various immune related biological processed and pathways, such as neutrophil activation, T cell activation, antigen processing and presentation. A total of 146 prognosis-related immune DEGs, and seven hub genes were identified, including B2M, HLA-DRB1, HLA-DRA, HLA-E, LCK, HLA-DPA1, and VAV1. Survival analysis showed that high expression of these genes was associated with a favorable prognosis. There were negative correlations between the expression of these hub genes and tumor purity, while positive correlations between expression of these hub genes and f infiltration levels of B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages and dendritic cells. These results help to stratify patients with different immune subtypes and help to design immunotherapy strategies for these patients in sarcoma.

Bioinformatics analysis reveals the competing endogenous RNA (ceRNA) coexpression network in the tumor microenvironment and prognostic biomarkers in soft tissue sarcomas

Soft tissue sarcomas (STSs) are rare, heterogeneous mesenchymal neoplasias. Understanding the tumor microenvironment (TME) and identifying potential biomarkers for prognosis associated with the TME of STS might provide effective clues for immune therapy. We evaluated the immune scores and stromal scores of STS patients by using the RNA sequencing dataset from The Cancer Genome Atlas (TCGA) database and the ESTIMATE algorithm. Then, the differentially expressed mRNAs (DEGs), miRNAs (DEMs) and lncRNAs (DELs) were identified after comparing the high- and low-score groups. Next, we established a competing endogenous RNA (ceRNA) network and explored the prognostic values of biomarkers involved in the network with the help of bioinformatics analysis. High immune score was significantly associated with favorable overall survival in STS patients. A total of 328 DEGs, 18 DEMs and 67 DELs commonly regulated in the immune and stromal score groups were obtained. A ceRNA network and protein-protein interaction (PPI) network identified some hub nodes with considerable importance in the network. Kaplan-Meier survival analysis demonstrated that nine mRNAs, two miRNAs and three lncRNAs were closely associated with overall survival of STS patients. Gene set enrichment analysis (GSEA) suggested that these three lncRNAs were mainly involved in immune response-associated pathways in STS patients. Finally, the expression levels of five mRNAs (APOL1, EFEMP1, LYZ, RARRES1 and TNFAIP2) were verified, which were consistent with the results of the TCGA cohort. The results of our study confirmed the prognostic value of immune scores for STS patients. We also identified several TME-related biomarkers that might contribute to prognostic prediction and immune therapy.