Machine learning identifies ferroptosis-related genes as potential diagnostic biomarkers for osteoarthritis.

Background: Osteoarthritis (OA) is one of the most common forms of degenerative arthritis and a major cause of pain and disability. Ferroptosis, a novel mode of cell death, has been verified to participate in the development of OA, but its mechanism is still unclear. This paper analyzed the ferroptosis-related genes (FRGs) in OA and explored their potential clinical value. Methods: We downloaded data through the GEO database and screened for DEGs. Subsequently, FRGs were obtained using two machine learning methods, LASSO regression and SVM-RFE. The accuracy of the FRGs as disease diagnosis was identified using ROC curves and externally validated. The CIBERSORT analyzed the immune microenvironment rug regulatory network constructed through the DGIdb. The competitive endogenous RNA (ceRNA) visualization network was constructed to search for possible therapeutic targets. The expression levels of FRGs were verified by qRT-PCR and immunohistochemistry. Results: In this study, we found 4 FRGs. The ROC curve showed that the combined 4 FRGs had the highest diagnostic value. Functional enrichment analysis showed that the 4 FRGs in OA could influence the development of OA through biological oxidative stress, immune response, and other processes. qRT-PCR and immunohistochemistry verified the expression of these key genes, further confirming our findings. Monocytes and macrophages are heavily infiltrated in OA tissues, and the persistent state of immune activation may promote the progression of OA. ETHINYL ESTRADIOL was a possible targeted therapeutic agent for OA. Meanwhile, ceRNA network analysis identified some lncRNAs that could regulate the FRGs. Conclusion: We identify 4 FRGs (AQP8, BRD7, IFNA4, and ARHGEF26-AS1) closely associated with bio-oxidative stress and immune response, which may become early diagnostic and therapeutic targets for OA.

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.

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.

Identification and validation of a hypoxia-related prognostic signature in clear cell renal cell carcinoma patients.

ABSTRACT: Increasing evidence has shown that hypoxia is closely related to the development, progression, and prognosis of clear cell renal cell carcinoma (ccRCC). Nevertheless, reliable prognostic signatures based on hypoxia have not been well-established. This study aimed to establish a hypoxia-related prognostic signature and construct an optimized nomogram for patients with ccRCC.We accessed hallmark gene sets of hypoxia, including 200 genes, and an original RNA seq dataset of ccRCC cases with integrated clinical information obtained by mining the Cancer Genome Atlas database and the International Cancer Genome Consortium (ICGC) database. Univariate Cox regression analysis and multivariate Cox proportional hazards regression were performed to identify prognostic hub genes and further established prognostic model as well as visualized the nomogram. External validation of the optimized nomogram was performed in independent cohorts from the ICGC database.ANKZF1, ETS1, PLAUR, SERPINE1, FBP1, and PFKP were selected as prognostic hypoxia-related hub genes, and the prognostic model effectively distinguishes high-risk and low-risk patients with ccRCC. The results of receiver operating characteristic curve, risk plots, survival analysis, and independent analysis suggested that RiskScore was a useful tool and independent predictive factor. A novel prognosis nomogram optimized via RiskScore showed its promising performance in both the Cancer Genome Atlas-ccRCC cohort and an ICGC-ccRCC cohort.Our study reveals that the differential expressions of hypoxia-related genes are associated with the overall survival of patients with ccRCC. The prognostic model we established showed a good predictive and discerning ability in ccRCC patients. The novel nomogram optimized via RiskScore exhibited a promising predictive ability. It may be able to serve as a visualized tool for guiding clinical decisions and selecting effective individualized treatments.

MiRNA-based model for predicting the TMB level in colon adenocarcinoma based on a LASSO logistic regression method.

ABSTRACT: Some patients with advanced colon adenocarcinoma (COAD) are not sensitive to radiotherapy and chemotherapy, and as such, immunotherapy has become the most popular option for these patients. However, different patients respond differently to immunotherapy. Tumor mutational burden (TMB) has been used as a predictor of the response of advanced COAD patients to immunotherapy. A high TMB typically indicates that the patient's immune system will respond well to immunotherapy. In addition, while microRNAs (miRNA) have been shown to play an important role in treatment responses associated with the immune system, the relationship between miRNA expression levels and TMB has not been clarified in COAD.We downloaded miRNA data and mutational files of COAD from the Cancer Genome Atlas database. Differentially expressed miRNAs were screened in the training group, and miRNAs used to construct the model were further identified using the LASSO logistic regression method. After building the miRNA-based model, we explored the correlation between the model and TMB. The model was verified by a receiver operating characteristic curve, and the correlation between it and 3 widely used immune checkpoints (programmed death receptor-1, programmed death-ligand 1, and cytotoxic T-lymphocyte associated protein-4) was explored. Functional enrichment analysis of the selected miRNAs was performed, and these respective miRNA target genes were predicted using online tools.Our results showed that a total of 32 differentially expressed miRNAs were used in the construction of the model. The accuracies of the models of the 2 datasets (training and test sets) were 0.987 and 0.934, respectively. Correlation analysis showed that the correlation of the model with programmed death-ligand 1 and cytotoxic T-lymphocyte associated protein-4, as well as TMB, was high, but there was no correlation with programmed death receptor-1. The results of functional enrichment analysis indicated that these 32 miRNAs were involved in many immune-related biological processes and tumor-related pathways.Therefore, this study demonstrated that differentially expressed miRNAs can be used to predict the TMB level, which can help identify advanced COAD patients who will respond well to immunotherapy. The miRNA-based model may be used as a tool to predict the TMB level in patients with advanced COAD.

Activation of autophagy contributes to the protective effects of lycopene against oxidative stress-induced apoptosis in rat chondrocytes.

Oxidative stress is commonly associated with osteoarthritis (OA). Lycopene (LYC), a natural carotenoid compound, is an effective antioxidant with potential cartilage-protecting actions. However, how it affects hydrogen peroxide (H2 O2 )-induced damage to the cartilage is unclear. In this study, an in vitro oxidative stress model was developed via treating primary chondrocytes with H2 O2 . Western blot, immunohistochemistry, and quantitative RT-PCR (qRT-PCR) were used to assess the levels of related factors. Reactive oxygen species (ROS) and apoptosis levels were analyzed by the use of appropriate probes and flow cytometry. The expression and activity of stress-specific enzymes (malondialdehyde, superoxide dismutase, and catalase) were also assessed. The role of autophagy was explored by using the inhibitor, 3-methyladenine (3-MA), as well as monodansylcadaverine staining, western blotting, and red fluorescent protein-green fluorescent protein-light chain 3 lentivirus infection. The result showed LYC exerted significant chondrocyte-protective effects, including reduced inflammation and chondrocyte degradation, increased chondrocyte proliferation, apoptosis inhibition, and reduced ROS production. LYC could effectively induce autophagy in the H2 O2 treatment group, and this effect could be attenuated by 3-MA. In terms of mechanism, LYC played a role in inhibiting MAPK and PI3K/Akt/NF-κB axis, which down-regulates levels of mTOR and had a potential therapeutic significance for cartilage degeneration.

PLCG2 as a potential indicator of tumor microenvironment remodeling in soft tissue sarcoma.

ABSTRACT: The tumor microenvironment (TME) plays an important role in the occurrence and development of soft tissue sarcoma (STS). A number of studies have shown that to inhibit tumor growth, the TME can be remodeled into an environment unsuitable for tumor proliferation. However, a lack of understanding exists regarding the dynamic regulation of TME.In this study, we used CIBERSORT and ESTIMATE calculation methods from the Cancer Genome Atlas (TCGA) database to calculate the proportion of tumor infiltrating immune cells (TICs) and the number of immune and stromal components in 263 STS samples. Differential expression genes (DEGs) shared by Immune Score and Stromal Score were obtained via difference analysis. Univariate Cox regression analysis and construction of protein-protein interaction (PPI) networks were applied to the DEGs.Through intersection analysis of univariate COX and PPI, PLCG2 was determined as the indicator. Further analysis showed that PLCG2 expression was positively correlated with the survival of STS patients. Gene set enrichment analysis (GSEA) showed that genes in the highly expressed PLCG2 group were enriched in immune-related activities. In the low-expression PLCG2 group, genes were enriched in the E2F, G2M, and MYC pathways. Difference analysis and correlation analysis showed that CD8+ T cells, gamma delta T cells, monocytes, and M1 macrophages were positively correlated with PLCG2 expression, indicating that PLCG2 may represent the immune status of TME.Therefore, the level of PLCG2 may aid in determining the prognosis of STS patients, especially the status of TME. These data provide additional insights into the remodeling of TME.

Identification and prognostic value of a glycolysis-related gene signature in patients with bladder cancer.

ABSTRACT: Bladder cancer (BC) is one of the most common malignancies worldwide. Several biomarkers related to the prognosis of patients with BC have previously been identified. However, these prognostic models use only one gene and are thus not reliable or accurate enough. The purpose of our study was to develop an innovative gene signature that has greater prognostic value in BC. So, in this study, we performed mRNA expression profiling of glycolysis-related genes in BC (n = 407) cohorts by mining data from The Cancer Genome Atlas (TCGA) database. The glycolysis-related gene sets were confirmed using the Gene Set Enrichment Analysis (GSEA). Using Cox regression analysis, a risk score staging model was built based on the genes that were determined to be significantly associated with BC outcome. Eventually, the system of risk score was structured to predict a patient's survival, and we identified four genes (CHPF, AK3, GALK1, and NUP188) that were associated with the outcomes of BC patients. According to the above-mentioned gene signature, patients were divided into two risk subgroups. The analysis showed that our constructed risk model was independent of clinical features and that the risk score was a highly powerful tool for predicting the overall survival (OS) of BC patients. Taking together, we identified a gene signature associated with glycolysis that could effectively predict the prognosis of BC patients. Our findings offer a new perspective for the clinical research and treatment of BC.

Integrated analysis identifies oxidative stress genes associated with progression and prognosis in gastric cancer.

Oxidative stress (OS) reactions are reported to be associated with oncogenesis and tumor progression. However, little is known about the potential diagnostic value of OS in gastric cancer (GC). This study identified hub OS genes associated with the prognosis and progression of GC and illustrated the underlying mechanisms. The transcriptome data and corresponding GC clinical information were collected from The Cancer Genome Atlas (TCGA) database. Aberrantly expressed OS genes between tumors and adjacent normal tissues were screened, and 11 prognosis-associated genes were identified with a series of bioinformatic analyses and used to construct a prognostic model. These genes were validated in the Gene Expression Omnibus (GEO) database. Furthermore, weighted gene co-expression network analysis (WGCNA) was subsequently conducted to identify the most significant hub genes for the prediction of GC progression. Analysis revealed that a good prognostic model was constructed with a better diagnostic accuracy than other clinicopathological characteristics in both TCGA and GEO cohorts. The model was also significantly associated with the overall survival of patients with GC. Meanwhile, a nomogram based on the risk score was established, which displayed a favorable discriminating ability for GC. In the WGCNA analysis, 13 progression-associated hub OS genes were identified that were also significantly associated with the progression of GC. Furthermore, functional and gene ontology (GO) analyses were performed to reveal potential pathways enriched with these genes. These results provide novel insights into the potential applications of OS-associated genes in patients with GC.