Search

Background: Cuproptosis is a novel form of programmed cell death which induced by copper. The special histology markers for diagnosing seminoma have not gained development in recent years. Results: A total of 165 normal testes and 78 seminoma samples were included in this study. After WGCNA analyses, 185 genes were intersected in seminoma and cluster characteristics module gene sets. The hub-genes of the 4 machine learning methods (FAM149B1, ARL6IP4, and RNASEH2B) displayed the highest area under curve (AUC=1). The expression of the 3 hub-genes were all down-regulated in seminoma. The nomogram based on the 3 hub-genes displayed satisfactory performance on diagnosing seminoma, according to calibration curve and decision curve analysis. Additionally, the results of the ROC analyses, using staining intensity, indicated that the combination of the 3 hub-genes showed the highest specificity and sensitivity (AUC=0.964, 95%Cl=0.895-1.000) for diagnosing seminoma. Conclusion: This is the first study using interactive and comprehensive bioinformatic methods to investigate the special markers for diagnosing seminoma from the perspective of Cuproptosis. The 3 hub-genes (FAM149B1, ARL6IP4, and RNASEH2B) were all down-regulated in seminoma. The three-gene model for the cytological diagnosis of indeterminate seminoma is both feasible and promising. Implementation of this as an adjunct to testis cytology may significantly impact the clinical management of patients with suspicious or indeterminate testicular lesions.

Rationale. Recent research has suggested that melatonin-related regulators play crucial roles in solid cancers and may be new targets for cancers. However, the characteristics and roles of melatonin modulators in clear cell renal cell carcinoma (ccRCC) remain largely unknown. Methods. Adopting multiomics data from TCGA and other public datasets, we analysed the expression, mutation, and prognostic evaluation in multiple cancers. ccRCC patients were categorized into two subgroups by an unsupervised cluster algorithm: melatonin-pattern cancer subtype 1 (MPCS1) and subtype 2 (MPCS2). We then explored the immune microenvironment, immune therapy response, and tumor metabolic pathways between the two subtypes. The clinical characteristics, genomic mutation landscape, and molecular inhibitor response were further investigated. Finally, a melatonin regulator-related prognostic model was constructed to predict patient prognosis in ccRCC. Results. We found that melatonin regulators were dysregulated depending on distinct cancer types, which were associated with genomic variation. The two subtypes indicated different clinical characteristics and biological processes in ccRCC. MPCS2, an aggressive subtype, led an advanced clinical stage and poorer survival of ccRCC patients. The activated oncogenic signaling pathway and metabolic signatures were responsible for cancer progression in the MPCS2 subtype. The MPCS2 subgroup suggested a higher tumor mutational burden and immune dysfunction state, resulting in a lower response to immunotherapy. The copy number variations of MPCS2 were significantly more frequent than those of MPCS1. In addition, the two subgroups exhibited distinct drug responsiveness, with MPCS2 being less responsive to multiple drugs. Finally, we established a subtype biomarker-based prognostic risk model that exhibited satisfactory performance in ccRCC patients. Conclusion. Melatonin regulator-related features could remodel functional pathways and the tumor immune microenvironment through genomic mutations and pathway regulation. Melatonin regulator-associated molecular subtypes enhance the understanding of the molecular characteristics of renal cancer and can guide clinical treatment. Activating the melatonergic system axis may improve the effect of immunotherapy for ccRCC.

BACKGROUND: Studies over the past decade have shown that long non-coding RNAs (lncRNAs) play an essential role in the tumorigenesis and progression of kidney renal clear cell carcinoma (KIRC). Meanwhile, autophagy has been demonstrated to regulate KIRC pathogenesis and targeting therapy resistance. However, the prognostic value of autophagy-related lncRNAs in KIRC patients has not been reported before. METHODS: In this study, we obtained transcriptome data of 611 KIRC cases from the TCGA database and 258 autophagy-related mRNAs from the HADb database to identify autophagy-related lncRNAs by co-expression network. A prognostic model was then established basing on these autophagy-related lncRNAs, dividing patients into high-risk and low-risk groups. Survival analysis, clinical variables dependent receiver operating characteristic (ROC) analyses, univariate/multivariate Cox analyses, and clinical correlation analysis were performed based on risk signature with R language. Gene set enrichment analysis (GSEA) was then performed to investigate the potential mechanism of the risk signature promoting KIRC progression with GSEA software. CIBERSORT algorithm was performed to assess the impact of these lncRNAs on the infiltration of immune cells. RESULTS: A total of 17 lncRNAs were screened out and all these lncRNAs were found significantly related to KIRC patients’ overall survival in subsequent survival analyses. Besides, the overall survival time in the high-risk group was much poorer than in the low-risk group. The ROC analysis revealed that the prognostic value of risk signature was better than age, gender, grade, and N stage. Univariate/multivariate analyses suggested that the risk signature was an independent predictive factor for KIRC patients. Immune and autophagy related pathways were dramatically enriched in high-risk and low-risk groups, respectively, and lncRNAs related immune cells were identified by CIBERSORT. CONCLUSIONS: In summary, our identified 17 autophagy-related lncRNAs had prognostic value for KIRC patients which may function in immunomodulation.

Background Cuproptosis is a new form of programmed cell death which induced by copper. The signature of cuproptosis related lncRNAs and mRNAs (CRLM) has not been reported before in clear cell renal cell carcinoma. Methods The RNA-seq, clinical data, and single nucleotide variants (SNV) data of clear cell renal cell carcinoma (ccRCC) were obtained from the Cancer Genome Atlas (TCGA). Cuproptosis related long non-coding RNAs (lncRNAs) were identified via Pearson’s test. Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were used to further screen prognosis related lncRNAs and mRNAs for signature construction. Cases were then randomly divided into training and testing set groups at ratio 1:1 to validate the cuproptosis signature. Kaplan–Meier survival analysis, receiver operating characteristic (ROC) curve analysis, nomogram, principal components analysis (PCA), gene set enrichment analyses (GSEA), mutation landscape, and therapies response were subsequently conducted with R or perl algorithms. A Japanese ccRCC cohort (n = 98) was used to validate this signature. Results A total of 245 lncRNAs were identified as cuproptosis related. Three of them (AL731577.2, LINC00460, AL133215.2) and cuproptosis related mRNA DBT were finally incorporated into the signature construction. High-risk group patients suffered from lower survival rate. The area under curve (AUC) of the novel signature for 1-, 3-, 5-year survival rates was 0.731, 0.718, 0.745, respectively. The concordance index (C-index) was 0.692. The Japanese cohort showed a good consistence with the results derived from the TCGA dataset. Tumor mutation burden (TMB) was also found to be a risking factor for ccRCC patients. Immune related Gene ontology terms were significantly enriched in high-risk patients. Besides, low-risk patients may be more sensitive to immune check inhibitors (ICIs), Sunitinib, and Pazopanib. High-risk patients may be more sensitive to Sorafenib. Conclusion The cuproptosis related signature is a promising and potential prognostic tool in predicting the survival of patients with ccRCC. It could contribute to precise and individualized ccRCC treatment.

BackgroundUbiquitylation modification is one of the multiple post-transcriptional process to regulate cellular physiology, including cell signaling, cycle regulation, DNA repair and transcriptional regulation. Members of TRIM family proteins could be defined as E3 ubiquitin ligases as they contain a RING-finger domain, and alterations of TRIM proteins are involved into a broad range of diverse disorders including cancer. TRIM37 is a novel discovered E3 ubiquitin ligase and acts as a oncoprotein in multiple human neoplasms, however its biological role in RCC still remains elusive.MethodsRCC microarray chips and public datasets were screened to identify novel TRIMs member as TRIM37, which was dysregulated in RCC. Gain or loss of functional cancer cell models were constructed, and in vitro and in vivo assays were performed to elucidate its tumorigenic phenotypes. Interactive network analyses were utilized to define intrinsic mechanism.ResultsWe identified TRIM37 was upregulated in RCC tumors, and its aberrant function predicted aggressive neoplastic phenotypes, poorer survival endings. TRIM37 promoted RCC cells EMT and malignant progression via TGF-β1 signaling activation, as a consequence of directly mediated by ubiquitinating-H2A modifications.ConclusionsOur findings identified a previously unappreciated role of TRIM37 in RCC progression and prognostic prediction. Importantly, we declared a novel ubiquitination-dependent link between TRIM ubiquitin ligases and TGF-β1 signaling in regulating cancerous malignancies.

Background: Studies over the past decade have shown that long non-coding RNAs (lncRNAs) play an essential role in the tumorigenesis and progression of kidney renal clear cell carcinoma (KIRC). Meanwhile, autophagy has been demonstrated to regulate KIRC pathogenesis and targeting therapy resistance. However, the prognostic value of autophagy-related lncRNAs in KIRC patients has not been reported before.Methods: In this study, we obtained transcriptome data of 611 KIRC cases from the TCGA database and 258 autophagy-related mRNAs from the HADb database to identify autophagy-related lncRNAs by co-expression network. A prognostic model was then established basing on these autophagy-related lncRNAs, dividing patients into high-risk and low-risk groups. Survival analysis, clinical variables dependent receiver operating characteristic (ROC) analyses, univariate/multivariate Cox analyses, and clinical correlation analysis were performed based on risk signature with R language. Gene set enrichment analysis (GSEA) was then performed to investigate the potential mechanism of the risk signature promoting KIRC progression with GSEA software. Results: A total of 17 lncRNAs were screened out and all these lncRNAs were found significantly related to KIRC patients' overall survival in subsequent survival analyses. Besides, the overall survival time in the high-risk group was much poorer than in the low-risk group. The ROC analysis revealed that the prognostic value of risk signature was better than age, gender, grade, and N stage. Univariate/multivariate analyses suggested that the risk signature was an independent predictive factor for KIRC patients. Immune-related pathways were dramatically enriched in high-risk patients according to GSEA. Conclusions: In summary, our identified 17 autophagy-related lncRNAs had prognostic value for KIRC patients which may function in Immunomodulation.