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Background: Clear cell renal cell carcinoma (ccRCC) predominates among kidney cancer cases and is influenced by mutations in cancer driver genes (CDGs). However, significant obstacles persist in the early diagnosis and treatment of ccRCC. While various genetic models offer new hopes for improving ccRCC management, the relationship between CDG-related long non-coding RNAs (CDG-RlncRNAs) and ccRCC remains poorly understood. Therefore, this study aims to construct prognostic molecular features based on CDG-RlncRNAs to predict the prognosis of ccRCC patients, and aims to provide a new strategy to enhance clinical management of ccRCC patients. Methods: This study employed Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses to comprehensively investigate the association between lncRNAs and CDGs in ccRCC. Leveraging The Cancer Genome Atlas (TCGA) dataset, we identified 97 prognostically significant CDG-RlncRNAs and developed a robust prognostic model based on these CDG-RlncRNAs. The performance of the model was rigorously validated using the TCGA dataset for training and the International Cancer Genome Consortium (ICGC) dataset for validation. Functional enrichment analysis elucidated the biological relevance of CDG-RlncRNA features in the model, particularly in tumor immunity. Experimental validation further confirmed the functional role of representative CDG-RlncRNA SNHG3 in ccRCC progression. Results: Our analysis revealed that 97 CDG-RlncRNAs are significantly associated with ccRCC prognosis, enabling patient stratification into different risk groups. Development of a prognostic model incorporating key lncRNAs such as HOXA11-AS, AP002807.1, APCDD1L-DT, AC124067.2, and SNHG3 demonstrated robust predictive accuracy in both training and validation datasets. Importantly, risk stratification based on the model revealed distinct immune-related gene expression patterns. Notably, SNHG3 emerged as a key regulator of the ccRCC cell cycle, highlighting its potential as a therapeutic target. Conclusions: Our study established a concise CDG-RlncRNA signature and underscored the pivotal role of SNHG3 in ccRCC progression. It emphasizes the clinical relevance of CDG-RlncRNAs in prognostic prediction and targeted therapy, offering potential avenues for personalized intervention in ccRCC.

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Background: Paclitaxel resistance is the major clinical obstacle in the chemotherapy of prostate cancer (PCa), but the resistant mechanism is less investigated.Purpose: To establish two paclitaxel-resistant PCa cells, provide a comprehensive gene expression profile analysis of resistant cells and the potential target to reverse resistance.Methods: Two Paclitaxel-resistant PCa cells (PC3/PR, LNcap/PR) were established by gradually increasing drug concentration. MTT and transwell assays were performed to detect drug sensitivity, cell proliferation and migration abilities. RNA-Sequencing (RNA-seq) and bioinformatic analyses were performed to identify abnormally expressed genes (AEGs) in resistant cells, and annotate the biological functions of AEGs. The role of the candidate AEG, TLR-4, on the resistant phenotypes was further investigated.Results: The resistance index of resistant cells was 2-3, and they showed a slower proliferation and increased migration ability. 4741 AEGs were screened out (Log2fold change absolute: log2FC(abs) > 1) in the resistant cells, and they were enriched in 2'-5'-oligoadenylate synthetase activity and chemical carcinogenesis. A number of AEGs, CCND2, IGFBP3, FOS, SHH, ZEB2, and members of FGF, FGFR and WNT families were also identified to be involved in cancer- and resistant phenotype-related processes. Finally, TLR-4 was validated significantly increased in resistant cells, and knockdown of TLR-4 increased drug-sensitivity, inhibited the proliferation and migration abilities.Conclusions: The study provided a comprehensive gene expression profile of paclitaxel-resistant PCa cells, and TLR-4 could be a potential target to reverse paclitaxel resistance.

Asunto(s)

Antineoplásicos Fitogénicos , Resistencia a Antineoplásicos , Paclitaxel , Neoplasias de la Próstata , 2',5'-Oligoadenilato Sintetasa/metabolismo , Antineoplásicos Fitogénicos/uso terapéutico , Línea Celular Tumoral , Proliferación Celular , Resistencia a Antineoplásicos/genética , Regulación Neoplásica de la Expresión Génica , Humanos , Masculino , Paclitaxel/farmacología , Paclitaxel/uso terapéutico , Neoplasias de la Próstata/tratamiento farmacológico , Neoplasias de la Próstata/genética , Neoplasias de la Próstata/metabolismo , RNA-Seq , Receptor Toll-Like 4/genética , Transcriptoma

RESUMEN

Background: Prostate cancer (PCa) is the second most common malignant tumor in men worldwide. MiRNAs have been reported to play significant roles in prognosis prediction for patients with malignant tumors. Methods: The survival-related miRNAs (sDMIRs) were identified by Cox regression analysis. A risk score model (RSM) was established based on three sDMIRs. The expression levels of sDMIRs in cell lines and clinical samples were detected via quantitative polymerase chain reaction. The correlations between sDMIRs and clinicopathological characteristics of PCa patients were evaluated using the chi-square test and Fisher's exact probability method. Results: Four sDMIRs were remarkably related to the prognosis of PCa patients based on univariate Cox analysis, of which miR-10a-5p, miR-20a-5p, and miR-508-3p were used to establish the RSM. The OS in the low-risk group was better than that in the high-risk group. In the verification of various prostate cell lines and clinical samples from 162 PCa patients, the prominently higher expression of miR-10a-5p and miR-20a-5p and lower expression of miR-508-3p were detected in PCa cell lines and tumor tissues, especially the more advanced T-stage. Besides, the higher expression of miR-20a-5p and miR-10a-5p was significantly correlated to the higher level of PSA, Gleason score, more advanced T-stage, and distant metastasis status. Conclusion: We identify and validate the clinical significance of three sDMIRs and establish a verified RSM to evaluate the prognosis for PCa patients. The findings not only provide a reliable tool for clinical decision-makers to evaluate patients' prognosis but also offer a novel perspective into the field of biomarker identification.