Expression, Prognostic Value, and Functional Mechanism of Polarity-Related Genes in Hepatocellular Carcinoma

Zhu, Dan; Zhang, Yanhong; Ouyang, Xiaoxi; Meng, Xiaohong; Cao, Qingyi; Yu, Xiaopeng; Lu, Juan; Li, Lanjuan; Su, Kunkai

doi:10.3390/ijms232112784

Cited by 1 publication

(1 citation statement)

References 57 publications

(59 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the publicly available data used for the study did not contain any personally identifiable information, ethical approval or informed consent procedures were not required for the research. Polarity-related genes (PRGs) data were obtained from the Molecular Signatures Database (MSigDB, https:// www.gsea-msigdb.org/gsea/msigdb) and collected from literature 11 (Table S1). The analysis flowchart is shown in Figure 1.…”

Section: Data Downloadmentioning

confidence: 99%

Development of a prognostic model for lung adenocarcinoma polarity‐related genes and analysis of immune landscape

Xu,

Du,

Jing

et al. 2024

Biotech and App Biochem

View full text Add to dashboard Cite

Despite the progress made in the management of lung adenocarcinoma (LUAD), the overall prognosis for LUAD individuals remains suboptimal. While the role of cell polarity in tumor invasion and metastasis is well established, its prognostic significance in LUAD is still unknown. Differential analysis was performed on the Cancer Genome Atlas (TCGA)‐LUAD and normal lung tissue, and candidate genes were identified by intersecting differentially expressed genes with polarity‐related genes (PRGs). A prognostic model was constructed using univariate and multivariate Cox regression and LASSO regression. To enhance the robustness of the analysis, an independent prognostic analysis was conducted by incorporating relevant clinical information. The accuracy and sensitivity of the model were validated using survival analysis and ROC curves. Finally, immune landscape, immune therapy, tumor mutation burden, and drug sensitivity analysis were carried out on high‐ and low‐risk patients. Ten prognostic genes were screened to divide LUAD patients into different risk groups. Survival analysis, ROC curves, and univariate/multivariate Cox regression analyses collectively demonstrated the favorable predictive performance of the model, which could be an independent prognostic factor. The nomogram, in conjunction with the calibration curve, demonstrated the model's compelling predictive capacity in prognosticating the overall survival of LUAD individuals. Low‐risk LUAD patients exhibited heightened levels of immune cell infiltration, immune scores, and immune checkpoint expression compared to high‐risk individuals. So, they may have a greater likelihood of benefiting from immune therapy. The high‐risk group demonstrated a remarkably higher tumor mutation burden (TMB) in contrast with the low‐risk group. XAV‐939, Fulvestrant, and SR16157 may have potential value in the clinical use of LUAD. We revealed the potential linkage between PRGs and LUAD prognosis, and the application of these prognostic factors in risk stratification and prognosis prediction of LUAD patients may be of great significance.

show abstract

Section: Data Downloadmentioning

confidence: 99%