A risk model constructed using 14 N6-methyladenosine-related lncRNAs as a new prognostic marker that correlates with the immunomodulatory effect and drug sensitivity in colorectal cancer.

Background: Colorectal cancer (CRC) remains the most common gastrointestinal malignancy. Despite multimodal therapy, its mortality is high due to recurrence and metastasis. This study developed and verified a risk model consisting of 14 N6-methyladenosine (m6A) long noncoding RNAs (lncRNAs) to assess the prognosis of patients with CRC and investigated its relevance to immune regulation and drug sensitivity. Methods: The gene expression profiles and clinical data of 446 patients with CRC were retrieved from The Cancer Genome Atlas (TCGA). 14 lncRNAs were screened using the Gene Co-expression Network (corFilter =0.5, P<0.001), and univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct the optimal risk model. The predictive performance and clinical applicability of the model were next verified. In addition, we performed Gene Ontology (GO) enrichment analysis to identify potential biological functions and detected the difference in tumor mutational burden (TMB), immune function, and sensitivity to immunotherapy and other drugs between the high- and low-risk groups to evaluate the application of the constructed risk model in depth. Results: The model was found to be an appropriate marker for predicting the prognosis of patients with CRC, independent of other clinical features, and demonstrated good precision and broad clinical applicability. It correlated with pathways in the development of cancer and immune-related functions, and patients in the high-risk group had higher tumor immune dysfunction and escape (TIDE) scores. Furthermore, we found significant differences in the overall survival (OS) between patients in the high- and low-tumor mutation burden (TMB) groups, which may work in conjunction with the constructed model to better predict patients' prognosis. Finally, we identified 12 drugs, including A-443654 and sorafenib, with lower half maximal inhibitory concentration (IC50) values in the high-risk group. Conversely, 21 drugs, including gemcitabine and rapamycin, had lower IC50 values in the low-risk group. Conclusions: We constructed a risk model based on 14 m6A-related lncRNAs that could predict the prognosis of patients with CRC and provided additional therapeutic ideas for their treatment. These findings may additionally serve as a foundation for further studies on regulating CRC via m6A-related lncRNAs.

Identification and validation of novel prognostic biomarkers and therapeutic targets for non-small cell lung cancer.

Background: Despite the significant survival benefits of anti-PD-1/PD-L1 immunotherapy, non-small cell lung cancer (NSCLC) remains one of the most common tumors and major causes of cancer-related deaths worldwide. Thus, there is an urgent need to identify new therapeutic targets for this refractory disease. Methods: In this study, microarray datasets GSE27262, GSE75037, GSE102287, and GSE21933 were integrated by Venn diagram. We performed functional clustering and pathway enrichment analyses using R. Through the STRING database and Cytoscape, we conducted protein-protein interaction (PPI) network analysis and identified the key genes, which were verified by the GEPIA2 and UALCAN portal. Validation of actin-binding protein anillin (ANLN) was performed by quantitative real-time polymerase chain reaction and Western blotting. Additionally, Kaplan-Meier methods were used to compute the survival analyses. Results: In total, 126 differentially expressed genes were identified, which were enriched in mitotic nuclear division, mitotic cell cycle G2/M transition, vasculogenesis, spindle, and peroxisome proliferator-activated receptor signaling pathway. 12 central node genes were identified in the PPI network complex. The survival analysis revealed that high transcriptional levels were associated with inferior survival in NSCLC patients. The clinical implication of ANLN was further explored; its protein expression showed a gradually increasing trend from grade I to III. Conclusion: These Key genes may be involved in the carcinogenesis and progression of NSCLC, which may serve as useful targets for NSCLC diagnosis and treatment.

The Ki-67 Proliferation Index-Related Nomogram to Predict the Response of First-Line Tyrosine Kinase Inhibitors or Chemotherapy in Non-small Cell Lung Cancer Patients With Epidermal Growth Factor Receptor-Mutant Status.

Background: The correlation between Ki-67 and epidermal growth factor receptor (EGFR)- or Kristen rat sarcoma viral oncogene homolog (KRAS)-mutant status in advanced or postoperative-recurrent non-small cell lung cancer (NSCLC) has fewer studies reported, and the prognostic role of Ki-67 with first-line EGFR-tyrosine kinase inhibitors (TKIs) or chemotherapy remains controversial. Methods: A total of 295 patients were tested for EGFR-mutant status in advanced or postoperative-recurrent NSCLC and received first-line EGFR-TKIs or chemotherapy for treatment. Ki-67 expression was retrospectively analyzed by immunohistochemistry. The Kaplan-Meier method was used to calculate survival rates. The multivariate Cox proportional hazards model was used to generate a nomogram. The established nomogram was validated using the calibration plots. Results: The expression levels of Ki-67 were divided into low (<60%, n = 186) and high (≥60%, n = 109) groups, based on the receiver operating characteristic curve. The expression levels of Ki-67 were found to be higher in patients with KRAS mutations when compared to KRAS wildtype, and EGFR wildtype was higher than EGFR mutations. The median overall survival (OS) of the low Ki-67 expression group was significantly longer than that of the high Ki-67 group, no matter in all NSCLC, EGFR mutations, EGFR wildtype, KRAS-mutant status, EGFR-TKIs, or chemotherapy of patients (P < 0.05). Subgroup analysis showed that the KRAS wildtype or EGFR mutations combine with low Ki-67 expression group had the longest median OS than KRAS mutations or EGFR wildtype combine with Ki-67 high expression group (P < 0.05). In the training cohort, the multivariate Cox analysis identified age, serum lactate dehydrogenase (LDH), serum Cyfra211, EGFR mutations, and Ki-67 as independent prognostic factors, and a nomogram was developed based on these covariates. The calibration curve for predicting the 12-, 24-, and 30-month OS showed an optimal agreement between the predicted and actual observed outcomes. Conclusions: The Ki-67 expression-based nomogram can well predict the efficacy of first-line therapy in NSCLC patients with EGFR- or KRAS-mutant status, high expression levels of Ki-67 correlated with a poor prognosis.