A Novel Inflammation-Related Gene Signature for Overall Survival Prediction and Comprehensive Analysis in Pediatric Patients with Wilms Tumor

Lymph node metastasis related gene BICC1 promotes tumor progression by promoting EMT and immune infiltration in pancreatic cancer

BACKGROUND

Pancreatic cancer (PC) is one of the most aggressive abdominal malignancies with a poor prognosis and it is urgent to find effective biomarkers for prediction. Although BICC1 expression is related to the survival, no evidence for its role in PC development has been found.

METHODS

We used RNA-seq data to screen for molecular markers highly associated with lymph node metastasis. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) public databases were used to analyze the expression and prognosis of Differential Expressed Genes (DEGs) in PC. R studio was used for visualization and functional analysis.

RESULTS

BicC Family RNA Binding Protein 1 (BICC1) was a lymph node metastasis-related DEGs in PC patients. Our study found that BICC1 mRNA levels in the tumor tissue were significantly higher and associated with poorer prognosis. Enrichment analysis found that BICC1 was enriched primarily in the Epithelial Mesenchymal Transition (EMT) pathway. Using the ESTIMATE and CIBERSORT algorithms, we found that BICC1 was related to immune cell infiltration. As a regulator of multiple immune checkpoints, BICC1 was also involved in PC's immune response.

CONCLUSIONS

BICC1 has the potential to be a new marker in association with lymph node metastasis as well as immune infiltration of PC. In addition to being a prognostic indicator, it may also be a potential therapeutic target.

A novel molecular subtypes and risk model based on inflammatory response-related lncrnas for bladder cancer

Background

Inflammation and long noncoding RNAs (lncRNAs) are gradually becoming important in the development of bladder cancer (BC). Nevertheless, the potential of inflammatory response-related lncRNAs (IRRlncRNAs) as a prognostic signature remains unexplored in BC.

Methods

The Cancer Genome Atlas (TCGA) provided RNA expression profiles and clinical information of BC samples, and GSEA Molecular Signatures database provided 1171 inflammation-related genes. IRRlncRNAs were identified using Pearson correlation analysis. After that, consensus clustering was performed to form molecular subtypes. After performing least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses, a risk model constructed based on the prognostic IRRlncRNAs was validated in an independent cohort. Kaplan–Meier (KM) analysis, univariate and multivariate Cox regression, clinical stratification analysis, and time-dependent receiver operating characteristic (ROC) curves were utilized to assess clinical effectiveness and accuracy of the risk model. In clusters and risk model, functional enrichment was investigated using GSEA and GSVA, and immune cell infiltration analysis was demonstrated by ESTIMATE and CIBERSORT analysis.

Results

A total of 174 prognostic IRRlncRNAs were confirmed, and 406 samples were divided into 2 clusters, with cluster 2 having a significantly inferior prognosis. Moreover, cluster 2 exhibited a higher ESTIMATE score, immune infiltration, and PD-L1 expression, with close relationships with the inflammatory response. Further, 12 IRRlncRNAs were identified and applied to construct the risk model and divide BC samples into low-risk and high-risk groups successfully. KM, ROC, and clinical stratification analysis demonstrated that the risk model performed well in predicting prognosis. The risk score was identified as an independently significant indicator, enriched in immune, cell cycle, and apoptosis-related pathways, and correlated with 9 immune cells.

Conclusion

We developed an inflammatory response-related subtypes and steady prognostic risk model based on 12 IRRlncRNAs, which was valuable for individual prognostic prediction and stratification and outfitted new insight into inflammatory response in BC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-022-00245-w.