Establishing a metastasis-related diagnosis and prognosis model for lung adenocarcinoma through CRISPR library and TCGA database

DEP domain‑containing 1 is a prognostic biomarker associated with the cell cycle in gliomas

Glioma is the most common primary tumor in the intracranial region, accounting for more than one-half of all central nervous system tumors. Abnormal expression of key cancer genes often promotes the occurrence and development of tumors. DEP domain containing 1 (DEPDC1) is a gene that encodes a protein containing a DEP domain, which serves an important role in numerous biological processes. In the present study, the relationship between the expression level of DEPDC1 and the clinical features of glioma was explored in datasets from the China Glioma Genome Atlas Project. Kaplan-Meier survival analysis was performed to evaluate the value of DEPDC1 expression in the prognosis of patients with glioma. T-test and univariate Cox analysis were used to identify differential genes, and Gene Ontology and gene set enrichment analysis (GSEA) were used to explore the function and related mechanisms of DEPDC1 in glioma. Univariate cox and multivariate cox analyses were used to screen variables, and a nomogram model was used to construct a prediction model. In glioma U87 and LN229 cell lines, the expression of DEPDC1 was decreased using shRNA to assess the effects of DEPDC1 on the proliferation, migration and invasion of glioma cells. The findings revealed that there was a positive association between the expression level of DEPDC1 and the poor clinical features of glioma, and patients with high expression of DEPDC1 had a significantly shorter overall survival time. GSEA demonstrated that the differential genes in the DEPDC1 high expression group were mainly enriched in 'cell cycle' and 'mitotic cell cycle'. Cell experiments showed that silencing DEPDC1 in U87 and LN229 cells significantly attenuated cell proliferation, migration and invasion. To conclude, the present study demonstrates that DEPDC1 is an independent prognostic indicator for patients with glioma and is associated with a poor prognosis. The expression of DEPDC1 is closely associated with the cell cycle of glioma and provides individualized treatment options for tumors.

A Novel KIF4A-related Model for Predicting Immunotherapy Response and Prognosis in Kidney Renal Clear Cell Carcinoma

BACKGROUND

The efficacy of chemotherapy in treating Kidney Renal Clear Cell Carcinoma (KIRC) is limited, whereas immunotherapy has shown some promising clinical outcomes. In this context, KIF4A is considered a potential therapeutic target for various cancers. Therefore, identifying the mechanism of KIF4A that can predict the prognosis and immunotherapy response of KIRC would be of significant importance.

METHODS

Based on the TCGA Pan-Cancer dataset, the prognostic significance of the KIF4A expression across 33 cancer types was analyzed by univariate Cox algorithm. Furthermore, overlapping differentially expressed genes (DEGs1) between the KIF4A high- and lowexpression groups and DEGs2 between the KIRC and normal groups were also analyzed. Machine learning and Cox regression algorithms were performed to obtain biomarkers and construct a prognostic model. Finally, the role of KIF4A in KIRC was analyzed using quantitative real-time PCR, transwell assay, and EdU experiment.

RESULTS

Our analysis revealed that KIF4A was significant for the prognosis of 13 cancer types. The highest correlation with KIF4A was found for KICH among the tumour mutation burden (TMB) indicators. Subsequently, a prognostic model developed with UBE2C, OTX1, PPP2R2C, and RFLNA was obtained and verified with the Renal Cell Cancer-EU/FR dataset. There was a positive correlation between risk score and immunotherapy. Furthermore, the experiment results indicated that KIF4A expression was considerably increased in the KIRC group. Besides, the proliferation, migration, and invasion abilities of KIRC tumor cells were significantly weakened after KIF4A was knocked out.

CONCLUSION

We identified four KIF4A-related biomarkers that hold potential for prognostic assessment in KIRC. Specifically, early implementation of immunotherapy targeting these biomarkers may yield improved outcomes for patients with KIRC.

Prognostic Factors and Construction of Nomogram Prediction Model of Lung Cancer Patients Using Clinical and Blood Laboratory Parameters

Objective

This work aimed to explore the prognostic risk factors of lung cancer (LC) patients and establish a line chart prediction model.

Methods

A total of 322 LC patients were taken as the study subjects. They were randomly divided into a training set (n = 202) and a validation set (n = 120). Basic information and laboratory indicators were collected, and the progression-free survival (PFS) and overall survival (OS) were followed up. Single-factor and cyclooxygenase (COX) multivariate analyses were performed on the training set to construct a Nomogram prediction model, which was validated with 120 patients in the validation set, and Harrell's consistency was analyzed.

Results

Single-factor analysis revealed significant differences in PFS (P<0.05) between genders, body mass index (BMI), carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), squamous cell carcinoma antigen (SCCA), treatment methods, treatment response evaluation, smoking status, presence of pericardial effusion, and programmed death ligand 1 (PD-L1) at 0 and 1-50%. Significant differences in OS (P<0.05) were observed for age, tumor location, treatment methods, White blood cells (WBC), uric acid (UA), CA125, pro-gastrin-releasing peptide (ProGRP), SCCA, cytokeratin fragment 21 (CYFRA21), and smoking status. COX analysis identified male gender, progressive disease (PD) as treatment response, and SCCA > 1.6 as risk factors for LC PFS. The consistency indices of the line chart models for predicting PFS and OS were 0.782 and 0.772, respectively.

Conclusion

Male gender, treatment response of PD, and SCCA > 1.6 are independent risk factors affecting the survival of LC patients. The PFS line chart model demonstrates good concordance.