Ion channels in sarcoma: pathophysiology and treatment options

Integrative profiling analysis reveals prognostic significance, molecular characteristics, and tumor immunity of angiogenesis-related genes in soft tissue sarcoma

Background

Soft tissue sarcoma (STS) is a class of malignant tumors originating from mesenchymal stroma with a poor prognosis. Accumulating evidence has proved that angiogenesis is an essential hallmark of tumors. Nevertheless, there is a paucity of comprehensive research exploring the association of angiogenesis-related genes (ARGs) with STS.

Methods

The ARGs were extracted from previous literature, and the differentially expressed ARGs were screened for subsequent analysis. Next, the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were conducted to establish the angiogenesis-related signature (ARSig). The predictive performance of the novel ARSig was confirmed using internal and external validation, subgroup survival, and independent analysis. Additionally, the association of the ARSig with the tumor immune microenvironment, tumor mutational burden (TMB), and therapeutic response in STS were further investigated. Notably, we finally conducted in vitro experiments to verify the findings from the bioinformatics analysis.

Results

A novel ARSig is successfully constructed and validated. The STS with a lower ARSig risk score in the training cohort has an improved prognosis. Also, consistent results were observed in the internal and external cohorts. The receiver operating characteristic (ROC) curve, subgroup survival, and independent analysis further indicate that the novel ARSig is a promising independent prognostic predictor for STS. Furthermore, it is proved that the novel ARSig is relevant to the immune landscape, TMB, immunotherapy, and chemotherapy sensitivity in STS. Encouragingly, we also validate that the signature ARGs are significantly dysregulated in STS, and ARDB2 and SRPK1 are closely connected with the malignant progress of STS cells.

Conclusion

In sum, we construct a novel ARSig for STS, which could act as a promising prognostic factor for STS and give a strategy for future clinical decisions, immune landscape, and personalized treatment of STS.

The development of a novel signature based on the m6A RNA methylation regulator-related ceRNA network to predict prognosis and therapy response in sarcomas

Background: N⁶ methyladenosine (m⁶A)-related noncoding RNAs (including lncRNAs and miRNAs) are closely related to the development of cancer. However, the gene signature and prognostic value of m⁶A regulators and m⁶A-associated RNAs in regulating sarcoma (SARC) development and progression remain largely unexplored. Therefore, further research is required.

Methods: We obtained expression data for RNA sequencing (RNA-seq) and miRNAs of SARC from The Cancer Genome Atlas (TCGA) datasets. Correlation analysis and two target gene prediction databases (miRTarBase and LncBase v.2) were used to deduce m⁶A-related miRNAs and lncRNAs, and Cytoscape software was used to construct ceRNA-regulating networks. Based on univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses, an m⁶A-associated RNA risk signature (m6Ascore) model was established. Prognostic differences between subgroups were explored using Kaplan–Meier (KM) analysis. Risk score-related biological phenotypes were analyzed in terms of functional enrichment, tumor immune signature, and tumor mutation signature. Finally, potential immunotherapy features and drug sensitivity predictions for this model were also discussed.

Results: A total of 16 miRNAs, 104 lncRNAs, and 11 mRNAs were incorporated into the ceRNA network. The risk score was obtained based on RP11-283I3.6, hsa-miR-455-3p, and CBLL1. Patients were divided into two risk groups using the risk score, with patients in the low-risk group having longer overall survival (OS) than those in the high-risk group. The receiver operating characteristic (ROC) curves indicated that risk characteristic performed well in predicting the prognosis of patients with SARC. In addition, lower m6Ascore was also positively correlated with the abundance of immune cells such as monocytes and mast cells activated, and several immune checkpoint genes were highly expressed in the low-m6Ascore group. According to our analysis, lower m6Ascore may lead to better immunotherapy response and OS outcomes. The risk signature was significantly associated with the chemosensitivity of SARC. Finally, a nomogram was constructed to predict the OS in patients with SARC. The concordance index (C-index) for the nomogram was 0.744 (95% CI: 0.707–0.784). The decision curve analysis (DCA), calibration plot, and ROC curve all showed that this nomogram had good predictive performance.

Conclusion: This m6Ascore risk model based on m⁶A RNA methylation regulator-related RNAs may be promising for clinical prediction of prognosis and might contain potential biomarkers for treatment response prediction for SARC patients.

Open channel block of Kv1.5 channels by HMQ1611

Kv1.5 channels conduct the ultra-rapid delayed rectifier potassium current (I_Kur). Pharmacological blockade of human Kv1.5 (hKv1.5) has been regarded as an effective treatment of re-entrant based atrial fibrillation, because Kv1.5 is highly expressed in human cardiac atria but scarcely in ventricles. The Kv1.5 blockade is also expected to be used in cancer therapeutics since Kv1.5 is overexpressed in some types of human tumors. Here, we investigated the blockade of hKv1.5 channels by HMQ1611, a symmetrical biphenyl derivative. hKv1.5 channels were heterologously expressed in Chinese hamster ovary cells. The effects of HMQ1611 on wild-type and 13 hKv1.5 mutant channels were examined using the whole-cell patch-clamp method, and molecular docking simulation was conducted to predict the docking position of HMQ1611 within Kv1.5 channels. We showed that HMQ1611 reversibly inhibited the hKv1.5 current in a concentration-dependent manner (IC₅₀ = 2.07 μM). HMQ1611 blockade of hKv1.5 current developed with time during depolarizing voltage-clamp steps, and this blockade was also voltage-dependent with a steep increase over the voltage range for channel openings. HMQ1611 inhibition was significantly reduced in the T479A, T480A, V505A, I508A, L510A, V512A, and V516A hKv1.5 mutant channels. Molecular docking analysis predicted that V505, V512, and T480 were involved in the blocking action of HMQ1611 on hKv1.5 channels. These results suggest that HMQ1611 inhibits hKv1.5 currents as an open channel blocker. Amino acid residues located at the base of the selectivity filter (T479 and T480) and in the S6 segment (V505, I508, L510, V512, and V516) of hKv1.5 appear to constitute potential binding sites for HMQ1611.

BCL7B is a potential novel diagnosis and prognosis biomarker for sarcomas using bioinformatics analysis

BCL7B plays a potential role in the progression of various cancers, while its role in sarcomas is unknown. We aimed to evaluate BCL7B's diagnostic and prognostic value, and potential BCL7B-related mechanisms in sarcomas based on The Cancer Genome Atlas (TCGA) database. We collected patients with sarcoma from TCGA. Wilcoxon rank sum test was used to compare the expression of BCL7B in sarcoma samples with different clinical-pathologic features. Univariate Cox regression analysis and multivariate Cox regression analysis were used to evaluate prognosis factors for sarcoma. Gene set enrichment analysis (GSEA) was conducted to elucidate the significant functions and pathways associated with BCL7B. BCL7B was a potential biomarker for distinguishing normal and tumor tissues with the analysis of ROC curve (AUC = 0.588). Low BCL7B expression was significantly correlated with tumor multifocal (OR = 0.39 for yes vs no), larger residual tumor (OR = 0.40 for R1,R2 vs RO), male gender (OR = 0.48 for male vs female) and White race (OR = 0.29 for White vs Asian, Black or African American). High BCL7B expression was correlated with leiomyosarcoma histological type (OR = 6.08 for leiomyosarcoma vs dedifferentiated liposarcoma, pleomorphic sarcoma). Univariate and multivariate Cox regression analysis showed that low BCL7B expression was independently associated with poor overall survival (P = .008). GSEA showed that GPCR (G protein-coupled receptors) ligand binding, secreted factors, class A1 rhodopsin-like receptors, extracellular matrix organization, core matrisome, Fc epsilon receptor I mediated NF-κB activation, and WNT signaling pathway were differentially enriched in BCL7B low expression phenotype (|NES| > 1, adjusted P value <.05, and FDR value <0.25). BCL7B may play an important role in sarcoma progression and may be a potential biomarker for prognosis and diagnosis in sarcomas.

Identification of Iron Metabolism-Related Gene Signatures for Predicting the Prognosis of Patients With Sarcomas

Iron is one of the essential trace elements in the human body. An increasing amount of evidence indicates that the imbalance of iron metabolism is related to the occurrence and development of cancer. Here, we obtained the gene expression and clinical data of sarcoma patients from TCGA and the GEO database. The prognostic value of iron metabolism-related genes (IMRGs) in patients with sarcoma and the relationship between these genes and the immune microenvironment were studied by comprehensive bioinformatics analyses. Two signatures based on IMRGs were generated for the overall survival (OS) and disease-free survival (DFS) of sarcoma patients. At 3, 5, and 7 years, the areas under the curve (AUCs) of the OS signature were 0.708, 0.713, and 0.688, respectively. The AUCs of the DFS signature at 3, 5, and 7 years were 0.717, 0.689, and 0.702, respectively. Kaplan–Meier survival analysis indicated that the prognosis of high-risk patients was worse than that of low-risk patients. In addition, immunological analysis showed that there were different patterns of immune cell infiltration among patients in different clusters. Finally, we constructed two nomograms that can be used to predict the OS and DFS of sarcoma patients. The C-index was 0.766 (95% CI: 0.697–0.835) and 0.763 (95% CI: 0.706–0.820) for the OS and DFS nomograms, respectively. Both the ROC curves and the calibration plots showed that the two nomograms have good predictive performance. In summary, we constructed two IMRG-based prognostic models that can effectively predict the OS and DFS of sarcoma patients.

TRPA1 Expression in Synovial Sarcoma May Support Neural Origin

Synovial sarcoma (SS) is a malignant mesenchymal soft tissue neoplasm. Despite its name, the cells of origin are not synovial cells, but rather neural, myogenic, or multipotent mesenchymal stem cells have been proposed as possible cells originators. Unlike other sarcomas, an unusual presentation of long-term pain at the tumor site has been documented, but the exact mechanisms have not been fully clarified yet. The transient receptor potential ankyrin 1 (TRPA1) is a nonselective cation channel mainly expressed in primary sensory neurons, where it functions as a pain sensor. TRPA1 have also been described in multiple non-excitable cells, including those derived from neural crest stem cells such as glial cells and, in particular, Schwann cell oligodendrocytes and astrocytes. We evaluated TRPA1 expression in SS. We selected a cohort of 41 SSs, and by immunohistochemistry, we studied TRPA1 expression. TRPA1 was found in 92.6% of cases. Triple TRPA1/pS100/SOX10 and TRPA1/SLUG/SNAIL staining strongly supports a neural origin of SS. TRPA1 positivity was also observed in a subset of cases negative with pS100, SOX10 and/or SLUG/SNAIL, and these divergent phenotypes may reflect a process of tumor plasticity and dedifferentiation of neural-derived SSs. Given the functional diversity of TRPA1 and its expression in neuronal and non-neuronal multipotent neural crest stem cells, it remains to be determined whether TRPA1 expression in SSs neoplastic cells plays a role in the molecular mechanism associated with premonitory pain symptoms and tumor progression.