HSPB8 is a Potential Prognostic Biomarker that Correlates With Immune Cell Infiltration in Bladder Cancer

Using clusterProfiler to characterize multiomics data

With the advent of multiomics, software capable of multidimensional enrichment analysis has become increasingly crucial for uncovering gene set variations in biological processes and disease pathways. This is essential for elucidating disease mechanisms and identifying potential therapeutic targets. clusterProfiler stands out for its comprehensive utilization of databases and advanced visualization features. Importantly, clusterProfiler supports various biological knowledge, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, through performing over-representation and gene set enrichment analyses. A key feature is that clusterProfiler allows users to choose from various graphical outputs to visualize results, enhancing interpretability. This protocol describes innovative ways in which clusterProfiler has been used for integrating metabolomics and metagenomics analyses, identifying and characterizing transcription factors under stress conditions, and annotating cells in single-cell studies. In all cases, the computational steps can be completed within ~2 min. clusterProfiler is released through the Bioconductor project and can be accessed via https://bioconductor.org/packages/clusterProfiler/ .

HSPB8 Facilitates the Oncogenesis and Advancement of Bladder Cancer via Activation of HSP27

Bladder cancer (BCa) stands as a significant malignancy within the genitourinary system. Notably, heat shock proteins (HSPs) exhibit elevated expression in cells subjected to environmental stresses and have been linked to the progression of many human malignancies. Among these, the functional implications and specific mechanism of HSPB8 in BCa have yet to be fully explored. In this study, we measured HSPB8 expression in both BCa tissues and various cell lines, further delving into its influence on cellular behaviors. Our observations pinpoint an upregulation of HSPB8 in BCa, a trend strongly associated with more advanced clinical manifestations. Suppressing HSPB8 exhibited marked reductions in cell proliferation and migration capabilities, while simultaneously amplifying apoptosis and inducing cell cycle arrest. Reinforcing these findings, our in vivo analyses using mouse models showed similar trends. Notably, upon HSPB8 knockdown, levels of specific proteins including eNOS (S1177), Hsp27 (S78/S82), PRAS40(T246), RSK1/2(S221/S227), and STAT3 (S727) decreased, with Hsp27 (S78/S82) and PRAS40(T246) experiencing the most profound drops. Furthermore, the application of an HSP27 inhibitor effectively reversed the phenotypes caused by increased HSPB8 expression. Collectively, our results suggest that elevated HSPB8 expression could act as a potential prognostic marker for BCa, and targeting HSPB8 might open new therapeutic avenues for treating this malignancy.

Identification and validation of telomerase related lncRNAs signature to predict prognosis and tumor immunotherapy response in bladder cancer

Telomerase allows eukaryotic cells to proliferate indefinitely, an important characteristic of tumor cells. Telomerase-related long no coding RNAs (TERLs) are involved in prognosis and drug sensitivity prediction; however, their association with bladder cancer (BLCA) is still unreported. The objective of this research is to determine a predictive prognostic TERL signature for OS and to provide an efficient treatment option for BLCA. The RNA sequence, clinical information, and mutational data of BLCA patients were acquired from The Cancer Genome Atlas (TCGA) database. With the help of the data from least absolute shrinkage and selection operator (LASSO) regression and Cox regression, a prognostic signature was established including 14 TERLs, which could divide BLCA patients into low-risk (L-R) and high-risk (H-R) cohorts. The time-dependent receiver operating characteristic (ROC) curve demonstrated the greater predictive power of the model. By combing the TERLs-based signature and clinical risk factors (age, sex, grade, and stage), a prognostic nomogram was constructed to forecast the survival rates of patients with BLCA at 1-, 3-, and 5-years, which was well matched by calibration plots C-index and Decision curve analysis (DCA). Furthermore, the L-R cohort showed higher tumor mutation burden (TMB) and lower tumor immune dysfunction and exclusion (TIDE) than the H-R cohort, as well as substantial variability in immune cell infiltration and immune function between the two cohorts was elucidated. As for external validation, LINC01711 and RAP2C-AS1 were identified as poor prognostic factors by survival analysis from the Kaplan-Meier Plotter database, which were validated in BLCA cell lines (EJ, 253J, T24, and 5637) and SV-HUC-1 cells as the control group using qRT-PCR. In addition, interference with the expression of RAP2C-AS1 suppresses the proliferation and migration of BLCA cells, and RAP2C-AS1 could affect the expression of CD274 and CTLA4, which could serve as prognostic markers and characterize the tumor microenvironment in BLCA. Overall, the model based on the 14-TERLs signature can efficiently predict the prognosis and drug treatment response in individuals with bladder cancer.

Identification of a six-gene signature to predict survival and immunotherapy effectiveness of gastric cancer

Background

Gastric cancer (GC) ranks as the fifth most prevalent malignancy and the second leading cause of oncologic mortality globally. Despite staging guidelines and standard treatment protocols, significant heterogeneity exists in patient survival and response to therapy for GC. Thus, an increasing number of research have examined prognostic models recently for screening high-risk GC patients.

Methods

We studied DEGs between GC tissues and adjacent non-tumor tissues in GEO and TCGA datasets. Then the candidate DEGs were further screened in TCGA cohort through univariate Cox regression analyses. Following this, LASSO regression was utilized to generate prognostic model of DEGs. We used the ROC curve, Kaplan-Meier curve, and risk score plot to evaluate the signature's performance and prognostic power. ESTIMATE, xCell, and TIDE algorithm were used to explore the relationship between the risk score and immune landscape relationship. As a final step, nomogram was developed in this study, utilizing both clinical characteristics and a prognostic model.

Results

There were 3211 DEGs in TCGA, 2371 DEGs in GSE54129, 627 DEGs in GSE66229, and 329 DEGs in GSE64951 selected as candidate genes and intersected with to obtain DEGs. In total, the 208 DEGs were further screened in TCGA cohort through univariate Cox regression analyses. Following this, LASSO regression was utilized to generate prognostic model of 6 DEGs. External validation showed favorable predictive efficacy. We studied interaction between risk models, immunoscores, and immune cell infiltrate based on six-gene signature. The high-risk group exhibited significantly elevated ESTIMATE score, immunescore, and stromal score relative to low-risk group. The proportions of CD4⁺ memory T cells, CD8⁺ naive T cells, common lymphoid progenitor, plasmacytoid dentritic cell, gamma delta T cell, and B cell plasma were significantly enriched in low-risk group. According to TIDE, the TIDE scores, exclusion scores and dysfunction scores for low-risk group were lower than those for high-risk group. As a final step, nomogram was developed in this study, utilizing both clinical characteristics and a prognostic model.

Conclusion

In conclusion, we discovered a 6 gene signature to forecast GC patients' OS. This risk signature proves to be a valuable clinical predictive tool for guiding clinical practice.