Gene expression profiles for an immunoscore model in bone and soft tissue sarcoma

Necroptosis-related lncRNA-based novel signature to predict the prognosis and immune landscape in soft tissue sarcomas

BACKGROUND

Necroptosis-related long noncoding RNAs (lncRNAs) play crucial roles in cancer initiation and progression. Nevertheless, the role and mechanism of necroptosis-related lncRNAs in soft tissue sarcomas (STS) is so far unknown and needs to be explored further.

METHODS

Clinical and genomic data were obtained from the UCSC Xena database. All STS patients' subclusters were performed by unsupervised consensus clustering method based on the prognosis-specific lncRNAs, and then assessed their survival advantage and immune infiltrates. In addition, we explored the pathways and biological processes in subclusters through gene set enrichment analysis. At last, we established the necroptosis-related lncRNA-based risk signature (NRLncSig) using the least absolute shrinkage and selection operator (LASSO) method, and explored the prediction performance and immune microenvironment of this signature in STS.

RESULTS

A total of 911 normal soft tissue samples and 259 STS patients were included in current study. 39 prognosis-specific necroptosis-related lncRNAs were selected. Cluster 2 had a worse survival than the cluster 1 and characterized by different immune landscape in STS. A worse outcome in the high-risk group was observed by survival analysis and indicated an immunosuppressive microenvironment. The ROC curve analyses illustrated that the NRLncSig performing competitively in prediction of prognosis for STS patients. In addition, the nomogram presents excellent performance in predicting prognosis, which may be more beneficial towards STS patients' treatment.

CONCLUSIONS

Our result indicated that the NRLncSig could be a good independent predictor of prognosis, and significantly connected with immune microenvironment, thereby providing new insights into the roles of necroptosis-related lncRNAs in STS.

Type 2 diabetes mellitus and the risk of male infertility: a Mendelian randomization study

Objective

To assess the causal effect of type 2 diabetes mellitus (T2DM) on male infertility (MI) and erectile dysfunction (ED) by Mendelian randomization (MR) analysis.

Methods

Data for T2DM, MI, and ED were obtained from genome-wide association studies (GWAS) involving 298, 957, 73, 479, and 223, 805 Europeans, respectively. We performed univariate MR analysis using MR Egger, Weighted median (WM) and Inverse variance weighted (IVW) methods to assess causal effects among the three. Through the Genotype Tissue Expression (GTEx) database, single-nucleotide polymorphisms (SNPs) that affect the expression levels of T2DM-related genes were located using expression quantitative trait loci (eQTL).

Results

MR analysis showed a significant causal relationship between T2DM and ED (WM, OR: 1.180, 95%CI: 1.010-1.378, P = 0.037; IVW, OR: 1.190, 95%CI: 1.084-1.300, P < 0.001). There is also a significant causal relationship between T2DM and MI (MR Egger, OR: 0.549, 95%CI: 0.317-0.952, P = 0.037; WM, OR: 0.593, 95%CI: 0.400, P = 0.010; IVW, OR: 0.767, 95%CI: 0.600-0.980, P = 0.034). ED may not cause MI (P > 0.05). We also found that rs6585827 corresponding to the PLEKHA1 gene associated with T2DM is an eQTL variant affecting the expression of this gene.

Conclusion

T2DM has a direct causal effect on ED and MI. The level of PLEKHA1 expression suppressed by rs6585827 is potentially associated with a lower risk of T2DM.

The Role of Macrophages in Sarcoma Tumor Microenvironment and Treatment

Sarcomas are a heterogeneous group of malignant mesenchymal tumors, including soft tissue and bone sarcomas. Macrophages in the tumor microenvironment, involved in immunosuppression and leading to tumor development, are called tumor-associated macrophages (TAMs). TAMs are very important in modulating the microenvironment of sarcomas by expressing specific markers and secreting factors that influence immune and tumor cells. They are involved in many signaling pathways, such as p-STAT3/p-Erk1/2, PI3K/Akt, JAK/MAPK, and JAK/STAT3. TAMs also significantly impact the clinical outcomes of patients suffering from sarcomas and are mainly related to poor overall survival rates among bone and soft tissue sarcomas, for example, chondrosarcoma, osteosarcoma, liposarcoma, synovial sarcoma, and undifferentiated pleomorphic sarcoma. This review summarizes the current knowledge on TAMs in sarcomas, focusing on specific markers on sarcoma cells, cell-cell interactions, and the possibly involved molecular pathways. Furthermore, we discuss the clinical significance of macrophages in sarcomas as a potential target for new therapies, presenting clinical relevance, possible new treatment options, and ongoing clinical trials using TAMs in sarcoma treatment.

A novel necroptosis-related lncRNAs signature effectively predicts the prognosis for osteosarcoma and is associated with immunity

Background: Necroptosis is closely related to tumorigenesis and development. Accumulating evidence has revealed that long non-coding RNAs (lncRNAs) are also central players in osteosarcoma (OS). However, the role of necroptosis-related lncRNAs in OS remains unclear. In the present study, we aim to craft a prognostic signature based on necroptosis-related lncRNAs to improve the OS prognosis prediction.

Methods: The signature based on necroptosis-related lncRNAs was discovered using univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis. The prognosis efficiency of the signature was then estimated by employing various bioinformatics methods. Subsequently, immunological analysis and Gene Set Enrichment Analysis (GSEA) were used to explore the association between necroptosis-related lncRNAs with clinical outcomes and immune status. More importantly, several necroptosis-related lncRNAs were validated with RT-qPCR.

Results: Consequently, a novel prognosis signature was successfully constructed based on eight necroptosis-related lncRNAs. Meanwhile, the novel necroptosis-related lncRNAs model could distribute OS patients into two risk groups with a stable and accurate predictive ability. Additionally, the GSEA and immune analysis revealed that the necroptosis-related lncRNAs signature affects the development and prognosis of OS by regulating the immune status. The necroptosis-related lncRNA signature was closely correlated with multiple anticancer agent susceptibility. Moreover, the RT-qPCR results indicated several necroptosis-related lncRNAs were significantly differently expressed in osteosarcoma and osteoblast cell lines.

Conclusion: In this summary, a novel prognostic signature integrating necroptosis-related lncRNAs was firstly constructed and could accurately predict the prognosis of OS. This study may increase the predicted value and guide the personalized chemotherapy treatment for OS.

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.

A novel immune-related gene signature predicting survival in sarcoma patients

Sarcomas are a heterogeneous group of rare mesenchymal tumors. The migration of immune cells into these tumors and the prognostic impact of tumor-specific factors determining their interaction with these tumors remain poorly understood. The current risk stratification system is insufficient to provide a precise survival prediction and treatment response. Thus, valid prognostic models are needed to guide treatment. This study analyzed the gene expression and outcome of 980 sarcoma patients from seven public datasets. The abundance of immune cells and the response to immunotherapy was calculated. Immune-related genes (IRGs) were screened through a weighted gene co-expression network analysis (WGCNA). A least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish a powerful IRG signature predicting prognosis. The identified IRG signature incorporated 14 genes and identified high-risk patients in sarcoma cohorts. The 14-IRG signature was identified as an independent risk factor for overall and disease-free survival. Moreover, the IRG signature acted as a potential indicator for immunotherapy. The nomogram based on the risk score was built to provide a more accurate survival prediction. The decision tree with IRG risk score discriminated risk subgroups powerfully. This proposed IRG signature is a robust biomarker to predict outcomes and treatment responses in sarcoma patients.

Five hypoxia and immunity related genes as potential biomarkers for the prognosis of osteosarcoma

Osteosarcoma accounts for a frequently occurring cancer of the primary skeletal system. In osteosarcoma cells, a hypoxic microenvironment is commonly observed that drives tumor growth, progression, and heterogeneity. Hypoxia and tumor-infiltrating immune cells might be closely related to the prognosis of osteosarcoma. In this study, we aimed to determine the biomarkers and therapeutic targets related to hypoxia and immunity through bioinformatics methods to improve the clinical prognosis of patients. We downloaded the gene expression data of osteosarcoma samples and normal samples in the UCSC Xena database and GTEx database, respectively, and downloaded the validation dataset (GSE21257) in the GEO database. Subsequently, we performed GO enrichment analysis and KEGG pathway enrichment analysis on the data of the extracted osteosarcoma hypoxia-related genes. Through univariate COX regression analysis, lasso regression analysis, multivariate COX regression analysis, etc., we established a predictive model for the prognosis of osteosarcoma. Five genes, including ST3GAL4, TRIM8, STC2, TRPS1, and FAM207A, were found by screening. In particular, we analyzed the immune cell composition of each gene based on the five genes through the CIBERSORT algorithm and verified each gene at the cell and tissue level. Our findings are valuable for the clinical diagnosis and treatment of this disease.

Identification and Validation of Pyroptosis-Related lncRNA Signature and Its Correlation with Immune Landscape in Soft Tissue Sarcomas

Background

Pyroptosis is critically associated with cancer initiation and progression, which can be modulated by diverse long noncoding RNAs (lncRNAs). However, the roles of pyroptosis-related lncRNAs in soft tissue sarcomas (STS) are still largely unknown.

Methods

Our study included a total of 259 STS patients extracted from The Cancer Genome Atlas Sarcoma (TCGA-SARC) dataset. Gene expression data fragments per kilobase of transcript per million mapped reads (FPKM) values were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) for the investigation of the expression pattern of pyroptosis-related lncRNAs. Unsupervised clustering based on pyroptosis-related lncRNAs was performed, and the associations of pyroptosis-related lncRNAs with clinical outcomes and immune microenvironment were investigated. Two risk signatures for overall survival (OS) and disease-free survival (DFS) were constructed and validated in independent cohorts.

Results

A total of 166 pyroptosis-related lncRNAs were identified in STS. Patients were clustered into two subgroups by unsupervised clustering, and cluster 2 had better prognoses, higher immune scores, higher abundance of immune cells, and higher expression of some immune checkpoints. OS- and DFS-risk signatures based on 10 and 13 pyroptosis-related lncRNAs, respectively, with favorable discrimination were constructed and validated. High-risk patients had favorable prognoses, and receiver operating characteristic (ROC) curves showed that both risk signatures could function as excellent predictors for prognoses of STS patients. Besides, the OS-risk signature could also excellently predict the immune landscape of STS.

Conclusion

In conclusion, our study revealed the clinical significance and critical roles of pyroptosis-related lncRNAs in STS, and constructed novel risk signatures based on pyroptosis-related lncRNAs that could effectively predict clinical outcomes and immune microenvironment in STS.