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Abstract Background Colorectal cancer (CRC) is a frequent cancer worldwide with varied survival outcomes. Objective We aimed to develop a nomogram model to predict the overall survival (OS) of CRC patients after surgery. Design This is a retrospective study. Setting This study was conducted from 2015 to 2016 in a single tertiary center for CRC. Patients CRC patients who underwent surgery between 2015 and 2016 were enrolled and randomly assigned into the training (n = 480) and validation (n = 206) groups. The risk score of each subject was calculated based on the nomogram. All participants were categorized into two subgroups according to the median value of the score. Main outcome measures The clinical characteristics of all patients were collected, significant prognostic variables were determined by univariate analysis. Least absolute shrinkage and selection operator (LASSO) regression was applied for variable selection. The tuning parameter (λ) for LASSO regression was determined by cross-validation. Independent prognostic variables determined by multivariable analysis were used to establish the nomogram. The predictive capacity of the model was assessed by risk group stratification. Results Infiltration depth, macroscopic classification, BRAF, carbohydrate antigen 19 − 9 (CA-199) levels, N stage, M stage, TNM stage, carcinoembryonic antigen levels, number of positive lymph nodes, vascular tumor thrombus, and lymph node metastasis were independent prognostic factors. The nomogram established based on these factors exhibited good discriminatory capacity. The concordance indices for the training and validation groups were 0.796 and 0.786, respectively. The calibration curve suggested favorable agreement between predictions and observations. Moreover, the OS of different risk subgroups was significantly different. Limitations The limitations of this work included small sample size and single-center design. Also, some prognostic factors could not be included due to the retrospective design. Conclusions A prognostic nomogram for predicting the OS of CRC patients after surgery was developed, which might be helpful for evaluating the prognosis of CRC patients.

Background: Skin cutaneous melanoma (SKCM) poses a significant public health challenge due to its aggressive nature and limited treatment options. To address this, the study introduces the Tumor Mutational Burden-Derived Immune lncRNA Prognostic Index (TILPI) as a potential prognostic tool for SKCM. Methods: TILPI was developed using a combination of gene set variation analysis, differential expression analysis, and COX regression analysis. Additionally, functional experiments were conducted to validate the findings, focusing on the effects of STARD4-AS1 knockdown on SKCM tumor cell behavior. These experiments encompassed assessments of tumor cell proliferation, gene and protein expression, migration, invasion, and in vivo tumor growth. Results: The results demonstrated that knockdown of STARD4-AS1 led to a significant reduction in tumor cell proliferation and impaired migration and invasion abilities. Moreover, it resulted in the downregulation of ADCY4, PRKACA, and SOX10 gene expression, as well as decreased protein expression of ADCY4, PRKACA, and SOX10. In vivo experiments further confirmed the efficacy of STARD4-AS1 knockdown in reducing tumor growth. Conclusions: This study elucidates the mechanistic role of STARD4-AS1 and its downstream targets in SKCM progression, highlighting the importance of the ADCY4/PRKACA/SOX10 pathway. The integration of computational analysis with experimental validation enhances the understanding of TILPI and its clinical implications. Overall, the findings underscore the potential of novel computational frameworks like TILPI in predicting and managing SKCM, particularly through targeting the ADCY4/PRKACA/SOX10 pathway. [ABSTRACT FROM AUTHOR]

Background: Despite effective strategies, resistance in EGFR mutated lung cancer remains a challenge. Metabolic reprogramming is one of the main mechanisms of tumor drug resistance. A class of drugs known as "statins" inhibit lipid cholesterol metabolism and are widely used in patients with cardiovascular diseases. Previous studies have also documented its ability to improve the therapeutic impact in lung cancer patients who receive EGFR-TKI therapy. Therefore, the effect of statins on targeted drug resistance to lung cancer remains to be investigated. Methods: Prolonged exposure to gefitinib resulted in the emergence of a resistant lung cancer cell line (PC9GR) from the parental sensitive cell line (PC9), which exhibited a traditional EGFR mutation. The CCK-8 assay was employed to assess the impact of various concentrations of pitavastatin on cellular proliferation. RNA sequencing was conducted to detect differentially expressed genes and their correlated pathways. For the detection of protein expression, Western blot was performed. The antitumor activity of pitavastatin was evaluated in vivo via a xenograft mouse model. Results: PC9 gefitinib resistant strains were induced by low-dose maintenance. Cell culture and animal-related studies validated that the application of pitavastatin inhibited the proliferation of lung cancer cells, promoted cell apoptosis, and restrained the acquired resistance to EGFR-TKIs. KEGG pathway analysis showed that the hippo/YAP signaling pathway was activated in PC9GR cells relative to PC9 cells, and the YAP expression was inhibited by pitavastatin administration. With YAP RNA interference, pAKT, pBAD and BCL-2 expression was decreased, while BAX expression as increased. Accordingly, YAP down-regulated significantly increased apoptosis and decreased the survival rate of gefitinib-resistant lung cancer cells. After pAKT was increased by SC79, apoptosis of YAP down-regulated cells induced by gefitinib was decreased, and the cell survival rate was increased. Mechanistically, these effects of pitavastatin are associated with the YAP pathway, thereby inhibiting the downstream AKT/BAD-BCL-2 signaling pathway. Conclusion: Our study provides a molecular basis for the clinical application of the lipid-lowering drug pitavastatin enhances the susceptibility of lung cancer to EGFR-TKI drugs and alleviates drug resistance. [ABSTRACT FROM AUTHOR]