Correlations between the modification patterns mediated by pyroptosis-related genes, tumor microenvironment, and immunotherapy in soft tissue sarcoma.

Soft tissue sarcoma (STS) incidence, progression, and metastasis are tightly linked to the tumor microenvironment (TME). The modification patterns mediated by pyroptosis-related genes (PRGs) in STS are unknown regarding the immune cell infiltration landscape of TME, immunotherapy effect, and prognostic value. First, we downloaded STS samples from the Cancer Genome Atlas (TCGA) and gene-expression omnibus (GEO) databases. Based on 52 PRGs, 2 pyroptosis modification patterns were analyzed, and the associations of pyroptosis modification patterns with immune cell infiltration in the TME were elucidated systematically. To quantify PRG modification patterns in STS patients, we generated a pyroptosis scoring system using principal component analysis (PCA). We identified 2 distinct pyroptosis modification patterns in STS. Compared to PRG cluster A, the prognosis of cluster B was better. These 2 pyroptosis modification patterns corresponded to different characteristics of immune cell infiltration in the TME and biological behaviors. In the pyroptosis scoring system, a high pyroptosis score was connected to higher immune cell infiltration, stronger immune surveillance, immune-killing effects on tumor cells, and better clinical benefits. The results from 3 anti-PD1/PD-L1-treated immune cohorts demonstrated that higher pyroptosis scores are also closely connected to better immunotherapy results. We demonstrated that pyroptosis modification is essential to the STS microenvironment. Moreover, the pyroptosis score is a reliable and independent prognostic factor in STS patients, enabling a richer understanding of the STS microenvironment and the screening of immunotherapy candidates, predicting the immunotherapeutic effects for individual STS patients, and guiding the use of chemotherapy drugs.

Bidirectional causal relationship between psychiatric disorders and osteoarthritis: A univariate and multivariate Mendelian randomization study.

BACKGROUND: Observational studies have shown associations between psychiatric disorders and osteoarthritis (OA). However, the causal impact of different psychiatric disorder types on specific sites of osteoarthritis remains unclear. This study aimed to comprehensively understand the potential causal associations between psychiatric disorders and osteoarthritis using Mendelian randomization (MR) analysis. METHODS: We collected data from genome-wide association studies of knee osteoarthritis (KOA) (n = 403,124), hip osteoarthritis (HOA) (n = 393,873), osteoarthritis of the knee or hip (KHOA) (n = 417,596), as well as three psychiatric disorders: bipolar disorder (n = 41,917), major depressive disorder (n = 170,756), and schizophrenia (n = 76,755) among European populations. We applied bidirectional univariate and multivariate MR analyses, including inverse variance weighted, Mendelian randomization-Egger, weighted median, simple mode, and weighted mode. We considered p < .05 as a criterion for identifying potential evidence of association. Bonferroni correction was used for multiple tests. RESULTS: Our univariate MR analysis results demonstrated that bipolar disorder is a protective factor for KOA (OR = 0.90, 95% CI = 0.83 to 0.97, p = 0.0048) and may also be protective for KHOA (p = 0.02). Conversely, major depression has a positive causal effect on both KOA (OR = 1.27; 95% CI = 1.08 to 1.49; p = 0.0036) and KHOA (OR = 1.24; 95% CI = 1.12 to 1.37; p = 3.62×10-05 ). Furthermore, our analysis suggested that KHOA may be a risk factor for major depression (OR = 1.06; 95% CI = 1.00 to 1.12; p = 0.0469) in reverse MR. After adjusting smoking (OR = 1.46; 95% CI = 1.19 to 1.65; p = 0.0032) and body mass index (OR = 1.44; 95% CI = 1.09 to 1.81; p = 8.56×10-04 ), the casual association between major depression and KHOA remained. CONCLUSION: Our study indicates that major depression is a great risk factor for KHOA, increasing the likelihood of their occurrence. However, further in-depth studies will be required to validate these results and elucidate the underlying molecular mechanisms.

Construction and validation of a predictive nomogram for ferroptosis-related genes in osteosarcoma.

BACKGROUND: Ferroptosis is a new type of cellular regulation of necrosis that has attracted great attention in recent years, which is different from the traditional mode of autophagy, apoptosis, and necrosis. Studies suggest that ferroptosis is key to the occurrence and development of tumors. METHODS: Here, we investigated the prognostic significance of ferroptosis-related genes (FRGs) in osteosarcoma (OS) using RNA transcriptome data from 88 OS samples collected from the UCSC Xena platform. We defined the OS sample from the UCSC platform as the training cohort and the GEO dataset (GSE21257 and GSE16091) as the validation cohorts. We assessed 73 up-regulated and 63 down-regulated FRGs. We divided patients from the UCSC database into groups at high risk and low risk and built a prognostic risk model to assess prognosis using five FRGs: MT1G, G6PD, ARNTL, BNIP3, and SQLE. RESULTS: High-risk OS patients presented a lower survival rate. These results were confirmed in the validation groups. In the training group, the areas under the ROC curves (AUC) were as follows: 0.880 for 1 year, 0.833 for 3 years, and 0.818 for 5 years. In the GSE21257 validation cohort, the AUC were as follows: 0.770 for 1 year, 0.641 for 3 years, and 0.632 for 5 years survival, and in the GSE16091 were 0.729 for 1 year, 0.663 for 3 years, and 0.735 for 5 years survival. CONCLUSIONS: These findings suggest that FRGs are associated with the prognosis of osteosarcoma. Moreover, our prognostic risk model can predict overall survival in osteosarcoma. This provides new ideas for the clinical diagnosis and personalized treatment of osteosarcoma.

Autophagy-related long non-coding RNA prognostic model predicts prognosis and survival of melanoma patients.

BACKGROUND: Melanomas are malignant tumors that can occur in different body parts or tissues such as the skin, mucous membrane, uvea, and pia mater. Long non-coding RNAs (lncRNAs) are key factors in the occurrence and development of many malignant tumors, and are involved in the prognosis of some patients. AIM: To identify autophagy-related lncRNAs in melanoma that are crucial for the diagnosis, treatment, and prognosis of melanoma patients. METHODS: We retrieved transcriptome expression profiles and clinical information of 470 melanoma patients from The Cancer Genome Atlas (TCGA) database. Then, we identified autophagy-related genes in the Human Autophagy Database. Using R, coexpression analysis of lncRNAs and autophagy-related genes was conducted to obtain autophagy-related lncRNAs and their expression levels. We also performed univariate and multivariate Cox proportional risk analyses on the obtained datasets, to systematically evaluate the prognostic value of autophagy-related lncRNAs in melanoma. Fifteen autophagy-related lncRNAs were identified and an autophagy-related prognostic signature for melanoma was established. The Kaplan-Meier and univariate and multivariate Cox regression analyses were used to calculate risk scores. Based on the risk scores, melanoma patients were randomly divided into high- and low-risk groups. Receiver operating characteristic curve analysis, dependent on time, was performed to assess the accuracy of the prognostic model. At the same time, we also downloaded the melanoma data sets GSE65904, GSE19234, and GSE78220 from the GENE EXPRESSION OMNIBUS database for model verification. Finally, we performed Gene Set Enrichment Analysis functional annotation, which showed that the low and the high-risk groups had different enriched pathways. RESULTS: The co-expression network for autophagy-related genes was constructed using R, and 936 lncRNAs related to autophagy were identified. Then, 52 autophagy-related lncRNAs were significantly associated with TCGA melanoma patients' survival by univariate Cox proportional risk analysis (P < 0.01). Further, the 52 autophagy-related lncRNAs mentioned above were analyzed by multivariate Cox analysis with R. Fifteen lncRNAs were selected: LINC01943, AC090948.3, USP30-AS1, AC068282.1, AC004687.1, AL133371.2, AC242842.1, PCED1B-AS1, HLA-DQB1-AS1, AC011374.2, LINC00324, AC018553.1, LINC00520, DBH-AS1, and ITGB2-AS1. The P values in all survival analyses using these 15 lncRNAs were < 0.05. These lncRNAs were used to build a risk model based on the risk score. Negative correlations were observed between risk scores and overall survival rate in melanoma patients over time. Additionally, the melanoma risk curve and scatter plot analyses showed that the death number increased along with the increase in the risk score. Overall, we identified and established a new prognostic risk model for melanoma using 15 autophagy-related lncRNAs. The risk model constructed with these lncRNAs can help and guide melanoma patient prognosis predictions and individualized treatments in the future. CONCLUSION: Overall, the risk model developed based on the 15 autophagy-related lncRNAs can have important prognostic value and may provide autophagy-related clinical targets for melanoma treatment.

Correlative Study on the Relationship between the Expression of m6a-Related Genes and the Prognosis and Immunotherapy of Soft Tissue Sarcoma.

Background: Soft tissue sarcomas (STS) are rare malignancies arising from mesenchymal tissue and interlacing ectodermal nerve tissue. Immunotherapy plays an important role in the prognosis and survival of STS patients. However, there is insufficient evidence to confirm the prognostic value of m6A-related genes and to evaluate the efficacy of immunotherapy for STS. Methods: We analyzed 23 m6A regulators from STS samples using R software and defined the modification patterns for three STS m6A regulators. Then, we constructed the m6A scores and divided the samples into high and low subgroups. Finally, we used data from the GEO database to verify the results. Results: We found that the m6A clusters differed in the overall survival (OS), progression-free survival (PFS), and immune infiltration rate. Additionally, the m6A score was positively correlated with the contents of activated B cells, activated dendritic cells, CD56 bright natural killer cells, helper T cells, and regulatory T cells. The group with a higher m6A score also presented higher OS and PFS rates. Regarding immunotherapy, STS patients with a high m6A score presented better results. Consistently, we found similar results in another dataset with patients that received anti-PD-1/PD-L1 therapy. Conclusion: Our current results indicated that the m6A score can be used to assess the survival rate of STS patients and guide immunotherapy and predict its effects. The analysis of different m6A patterns of STS samples contributed to the understanding of the diversity and complexity of the tumor microenvironment (TME) and provided new ideas for the clinical development of personalized immunotherapy and prediction of the prognosis of STS patients.

Identification of the osteoarthritis signature gene PDK1 by machine learning and its regulatory mechanisms on chondrocyte autophagy and apoptosis.

Background: Osteoarthritis (OA) is a degenerative joint disease frequently diagnosed in the elderly and middle-aged population. However, its specific pathogenesis has not been clarified. This study aimed to identify biomarkers for OA diagnosis and elucidate their potential mechanisms for restoring OA-dysregulated autophagy and inhibiting chondrocyte apoptosis in vitro. Material and methods: Two publicly available transcriptomic mRNA OA-related datasets (GSE10575 and GSE51588) were explored for biomarker identification by least absolute shrinkage and selection operator (LASSO) regression, weighted gene co-expression network analysis (WGCNA), and support vector machine recursive feature elimination (SVM-RFE). We applied the GSE32317 and GSE55457 cohorts to validate the markers' efficacy for diagnosis. The connections of markers to chondrocyte autophagy and apoptosis in OA were also comprehensively explored in vitro using molecular biology approaches, including qRT-PCR and Western blot. Results: We identified 286 differentially expressed genes (DEGs). These DEGs were enriched in the ECM-receptor interaction and PI3K/AKT signaling pathway. After external cohort validation and protein-protein interaction (PPI) network construction, PDK1 was finally identified as a diagnostic marker for OA. The pharmacological properties of BX795-downregulated PDK1 expression inhibited LPS-induced chondrocyte inflammation and apoptosis and rescued OA-dysregulated autophagy. Additionally, the phosphorylation of the mediators associated with the MAPK and PI3K/AKT pathways was significantly downregulated, indicating the regulatory function of PDK1 in apoptosis and autophagy via MAPK and PI3K/AKT-associated signaling pathways in chondrocytes. A significantly positive association between the PDK1 expression and Neutrophils, Eosinophils, Plasma cells, and activated CD4 memory T cells, as well as an evident negative correlation between T cells follicular helper and CD4 naive T cells, were detected in the immune cell infiltration analysis. Conclusions: PDK1 can be used as a diagnostic marker for OA. Inhibition of its expression can rescue OA-dysregulated autophagy and inhibit apoptosis by reducing the phosphorylation of PI3K/AKT and MAPK signaling pathways.

Pyroptosis-related gene mediated modification patterns and immune cell infiltration landscapes in cutaneous melanoma to aid immunotherapy.

Tumor occurrence, infiltration, and metastasis are significantly affected by the tumor microenvironment (TME). Increasing evidence has elucidated TME's clinical significance in prognostic assessment and immunotherapy efficacy. Nonetheless, no studies have reported the potential pyroptosis-related genes (PRGs) function in TME immune cell infiltration. In this study, we systematically analyzed different PRG modification patterns in 685 cutaneous melanoma (CM) cases. We comprehensively explored the relationship between these PRG modification patterns and TME cell infiltration characteristics. Then, we used principal component analysis to construct a pyroptosis scoring system to quantify the PRG modification patterns in each CM patient. Three different PRG modification patterns were identified. Pyroptosis score was confirmed as an independent prognostic factor for CM patients. High pyroptosis score was characterized by high immunophenscore and more lymphocytes infiltration, such as T, B, and NK cells - indicating a strong ability to monitor and clear tumors, which may be responsible for the advantageous survival. Three independent cohorts that received immunotherapy confirmed the significant therapeutic efficacy and clinical benefit in high pyroptosis scores patients. This study revealed that the PRG modification patterns have a crucial effect on the CM complex and diverse microenvironment. Pyroptosis scores might serve as credible predictors of immunotherapy response and prognostic assessment. This provides a new direction for personalized immunotherapy strategies and appropriate immunotherapy candidates screening.

Characterization of m6A-Related Genes Landscape in Skin Cutaneous Melanoma to Aid Immunotherapy and Assess Prognosis.

BACKGROUND: Skin cutaneous melanoma (SKCM) is the most malignant tumor among skin cancers. Immunotherapy has shown a great role in the advantageous prognosis of SKCM. However, only a small percentage of people can benefit from immunotherapy. To date, there has been insufficient evidence to reveal the prognostic value of m6A in SKCM and its relationship with the infiltration of immune cells and the efficacy of immunotherapy. METHODS: Here, we synthetically analyzed 23 m6A regulators from SKCM samples collected from the TCGA and GEO databases. We defined three m6A modification patterns and constructed m6A scores using principal component analysis (PCA). RESULTS: We found significant differences in overall survival (OS) and immune infiltration between different m6A subclusters. Besides, m6A score was positively correlated with regulatory T-cell and helper T-cell content, which may account for the association of high m6A scores with superior prognosis. Multivariate Cox regression analysis revealed that the m6A score was an independent prognostic indicator. Moreover, patients with high m6A scores showed a better response to immunotherapy, and this result was further validated in two independent immunotherapy cohorts receiving anti-PD-1/PD-L1 therapy. CONCLUSION: The findings suggested the m6A score can screen suitable candidates for immunotherapy and can predict immunotherapy response. This analysis of different m6A patterns in a large sample of SKCM expanded our understanding of TME and provided new ideas for prognostic assessment and personalized immunotherapy strategies for SKCM patients.