IMUP and GPRC5A: two newly identified risk score indicators in pancreatic ductal adenocarcinoma

Integrated analysis of scRNA-seq and bulk RNA-seq reveals that GPRC5A is an important prognostic gene in pancreatic cancer and is associated with B-cell Infiltration in pancreatic cancer

Introduction

Pancreatic cancer (PC) is a malignancy with poor prognosis. This investigation aimed to determine the relevant genes that affect the prognosis of PC and investigate their relationship with immune infiltration.

Methods

: First, we acquired PC single-cell chip data from the GEO database to scrutinize dissimilarities in immune cell infiltration and differential genes between cancerous and adjacent tissues. Subsequently, we combined clinical data from TCGA to identify genes relevant to PC prognosis. Employing Cox and Lasso regression analyses, we constructed a multifactorial Cox prognostic model, which we subsequently confirmed. The prognostic gene expression in PC was authenticated using RT-PCR. Moreover, we employed the TIMER online database to examine the relationship between the expression of prognostic genes and T and B cell infiltration. Additionally, the expression of GPRC5A and its correlation with B cells infiltration and patient prognosis were ascertained in tissue chips using multiple immune fluorescence staining.

Results

The single-cell analysis unveiled dissimilarities in B-cell infiltration between cancerous and neighboring tissues. We developed a prognostic model utilizing three genes, indicating that patients with high-risk scores experienced a more unfavorable prognosis. Immune infiltration analysis revealed a significant correlation among YWHAZ, GPRC5A, and B cell immune infiltration. In tissue samples, GPRC5A exhibited substantial overexpression and a robust association with an adverse prognosis, demonstrating a positive correlation with B cell infiltration.

Conclusion

GPRC5A is an independent risk factor in PC and correlated with B cell immune infiltration in PC. These outcomes indicated that GPRC5A is a viable target for treating PC.

Immune regulation and prognosis indicating ability of a newly constructed multi-genes containing signature in clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most common renal malignancy, although newly developing targeted therapy and immunotherapy have been showing promising effects in clinical treatment, the effective biomarkers for immune response prediction are still lacking. The study is to construct a gene signature according to ccRCC immune cells infiltration landscape, thus aiding clinical prediction of patients response to immunotherapy.

METHODS

Firstly, ccRCC transcriptome expression profiles from Gene Expression Omnibus (GEO) database as well as immune related genes information from IMMPORT database were combine applied to identify the differently expressed meanwhile immune related candidate genes in ccRCC comparing to normal control samples. Then, based on protein-protein interaction network (PPI) and following module analysis of the candidate genes, a hub gene cluster was further identified for survival analysis. Further, LASSO analysis was applied to construct a signature which was in succession assessed with Kaplan-Meier survival, Cox regression and ROC curve analysis. Moreover, ccRCC patients were divided as high and low-risk groups based on the gene signature followed by the difference estimation of immune treatment response and exploration of related immune cells infiltration by TIDE and Cibersort analysis respectively among the two groups of patients.

RESULTS

Based on GEO and IMMPORT databases, a total of 269 differently expressed meanwhile immune related genes in ccRCC were identified, further PPI network and module analysis of the 269 genes highlighted a 46 genes cluster. Next step, Kaplan-Meier and Cox regression analysis of the 46 genes identified 4 genes that were supported to be independent prognosis indicators, and a gene signature was constructed based on the 4 genes. Furthermore, after assessing its prognosis indicating ability by both Kaplan-Meier and Cox regression analysis, immune relation of the signature was evaluated including its association with environment immune score, Immune checkpoint inhibitors expression as well as immune cells infiltration. Together, immune predicting ability of the signature was preliminary explored.

CONCLUSIONS

Based on ccRCC genes expression profiles and multiple bioinformatic analysis, a 4 genes containing signature was constructed and the immune regulation of the signature was preliminary explored. Although more detailed experiments and clinical trials are needed before potential clinical use of the signature, the results shall provide meaningful insight into further ccRCC immune researches.

The use of RNA-based treatments in the field of cancer immunotherapy

Over the past several decades, mRNA vaccines have evolved from a theoretical concept to a clinical reality. These vaccines offer several advantages over traditional vaccine techniques, including their high potency, rapid development, low-cost manufacturing, and safe administration. However, until recently, concerns over the instability and inefficient distribution of mRNA in vivo have limited their utility. Fortunately, recent technological advancements have mostly resolved these concerns, resulting in the development of numerous mRNA vaccination platforms for infectious diseases and various types of cancer. These platforms have shown promising outcomes in both animal models and humans. This study highlights the potential of mRNA vaccines as a promising alternative approach to conventional vaccine techniques and cancer treatment. This review article aims to provide a thorough and detailed examination of mRNA vaccines, including their mechanisms of action and potential applications in cancer immunotherapy. Additionally, the article will analyze the current state of mRNA vaccine technology and highlight future directions for the development and implementation of this promising vaccine platform as a mainstream therapeutic option. The review will also discuss potential challenges and limitations of mRNA vaccines, such as their stability and in vivo distribution, and suggest ways to overcome these issues. By providing a comprehensive overview and critical analysis of mRNA vaccines, this review aims to contribute to the advancement of this innovative approach to cancer treatment.

Osteosarcoma transcriptome data exploration reveals STC2 as a novel risk indicator in disease progression

BACKGROUND

Osteosarcoma has been the most common primary bone malignant tumor in children and adolescents. Despite the considerable improvement in the understanding of genetic events attributing to the rapid development of molecular pathology, the current information is still lacking, partly due to the comprehensive and highly heterogeneous nature of osteosarcoma. The study is to identify more potential responsible genes during the development of osteosarcoma, thus identifying promising gene indicators and aiding more precise interpretation of the disease.

METHODS

Firstly, from GEO database, osteosarcoma transcriptome microarrays were used to screen the differential expression genes (DEGS) in cancer comparing to normal bone samples, followed by GO/KEGG interpretation, risk score assessment and survival analysis of the genes, for the purpose of selecting a credible key gene. Further, the basic physicochemical properties, predicted cellular location, gene expression in human cancers, the association with clinical pathological features and potential signaling pathways involved in the key gene's regulation on osteosarcoma development were in succession explored.

RESULTS

Based on the selected GEO osteosarcoma expression profiles, we identified the differential expression genes in osteosarcoma versus normal bone samples, and the genes were classified into four groups based on the difference level, further genes interpretation indicated that the high differently level (> 8 fold) genes were mainly located extracellular and related to matrix structural constituent regulation. Meanwhile, module function analysis of the 67 high differential level (> 8 fold) DEGS revealed a 22-gene containing extracellular matrix regulation associated hub gene cluster. Further survival analysis of the 22 genes revealed that STC2 was an independent prognosis indicator in osteosarcoma. Moreover, after validating the differential expression of STC2 in cancer vs. normal tissues using local hospital osteosarcoma samples by IHC and qRT-PCR experiment, the gene's physicochemical property revealed STC2 as a cellular stable and hydrophilic protein, and the gene's association with osteosarcoma clinical pathological parameters, expression in pan-cancers and the probable biological functions and signaling pathways it involved were explored.

CONCLUSION

Using multiple bioinformatic analysis and local hospital samples validation, we revealed the gain of expression of STC2 in osteosarcoma, which associated statistical significantly with patients survival, and the gene's clinical features and potential biological functions were also explored. Although the results shall provide inspiring insights into further understanding of the disease, further experiments and detailed rigorous clinical trials are needed to reveal its potential drug-target role in clinical medical use.