Three-gene prognostic biomarkers for seminoma identified by weighted gene co-expression network analysis

POLR3G promotes EMT via PI3K/AKT signaling pathway in bladder cancer

RNA Polymerase III Subunit G (POLR3G) promotes tumorigenesis, metastasis, cancer stemness, and chemoresistance of breast cancer and lung cancer; however, its biological function in bladder cancer (BLCA) remains unclear. Through bioinformatic analyses, we found that POLR3G expression was significantly elevated in BLCA tumor tissues and was associated with decreased survival. Multivariate Cox analysis indicated that POLR3G could serve as an independent prognostic risk factor. Our functional investigations revealed that POLR3G deficiency resulted in reduced migration and invasion of BLCA cells both in vitro and in vivo. Additionally, the expressions of EMT-related mesenchymal markers were also downregulated in POLR3G knockdown cells. Mechanistically, we showed that POLR3G could activate the PI3K/AKT signaling pathway. Inhibition of this pathway with LY294002 reduced the enhanced migration and invasion of BLCA cells induced by POLR3G overexpression, whereas the activation of this pathway using 740Y-P restored the abilities that were inhibited by POLR3G knockdown. Taken together, our findings suggested that POLR3G is a prognostic predictor for BLCA and promotes EMT of BLCA through activation of the PI3K/AKT signaling pathway.

Machine learning-based identification of tumor-infiltrating immune cell-associated model with appealing implications in improving prognosis and immunotherapy response in bladder cancer patients

Background

Immune cells are crucial components of the tumor microenvironment (TME) and regulate cancer cell development. Nevertheless, the clinical implications of immune cell infiltration-related mRNAs for bladder cancer (BCa) are still unclear.

Methods

A 10-fold cross-validation framework with 101 combinations of 10 machine-learning algorithms was employed to develop a consensus immune cell infiltration-related signature (IRS). The predictive performance of IRS in terms of prognosis and immunotherapy was comprehensively evaluated.

Results

The IRS demonstrated high accuracy and stable performance in prognosis prediction across multiple datasets including TCGA-BLCA, eight independent GEO datasets, our in-house cohort (PUMCH_Uro), and thirteen immune checkpoint inhibitors (ICIs) cohorts. Additionally, IRS was superior to traditional clinicopathological features (e.g., stage and grade) and 94 published signatures. Furthermore, IRS was an independent risk factor for overall survival in TCGA-BLCA and several GEO datasets, and for recurrence-free survival in PUMCH_Uro. In the PUMCH_Uro cohort, patients in the high-IRS group were characterized by upregulated CD8A and PD-L1 and TME of inflamed and immunosuppressive phenotypes. As predicted, these patients should benefit from ICI therapy and chemotherapy. Furthermore, in the ICI cohorts, the high-IRS group was related to a favorable prognosis and responders have dramatically higher IRS compared to non-responders.

Conclusions

Generally, these indicators suggested the promising application of IRS in urological practices for the early identification of high-risk patients and potential candidates for ICI application to prolong the survival of individual BCa patients.

The role of SPI1-TYROBP-FCER1G network in oncogenesis and prognosis of osteosarcoma, and its association with immune infiltration

Osteosarcoma is an aggressive malignant bone sarcoma worldwide. A causal gene network with specific functions underlying both the development and progression of OS was still unclear. Here we firstly identified the differentially expressed genes (DEGs) between control and OS samples, and then defined the hub genes and top clusters in the protein–protein interaction (PPI) network of these DEGs. By focusing on the hub gene TYROBP in the top 1 cluster, a conserved TYROBP co-expression network was identified. Then the effect of the network on OS overall survival was analyzed. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and Gene Set Enrichment Analysis (GSEA) were used to explore the functions of the network. XCell platform and ssGSEA algorithm were conducted to estimate the status of immune infiltration. ChEA3 platform, GSEA enrichment analysis, and Drug Pair Seeker (DPS) were used to predict the key transcription factor and its upstream signal. We identified the downregulated SPI1-TYROBP-FCER1G network in OS, which were significantly enriched in immune-related functions. We also defined a two-gene signature (SPI1/FCER1G) that can predict poorer OS overall survival and the attenuated immune infiltration when downregulated. The SPI1-TYROBP-FCER1G network were potentially initiated by transcription factor SPI1 and would lead to the upregulated CD86, MHC-II, CCL4/CXCL10/CX3CL1 and hence increased immune infiltrations. With this study, we could better explore the mechanism of OS oncogenesis and metastasis for developing new therapies.