Cellular functions of heat shock protein 20 (HSPB6) in cancer: A review

Identifying prognostic hub genes and key pathways in pediatric adrenocortical tumors through RNA sequencing and Co-expression analysis

Pediatric adrenocortical tumors (ACTs), rare conditions with uncertain prognoses, have high incidence in southern and southeastern Brazil. Pediatric ACTs are highly heterogeneous, so establishing prognostic markers for these tumors is challenging. We have conducted transcriptomic analysis on 14 pediatric ACT samples and compared cases with favorable and unfavorable clinical outcomes to identify prognostically significant genes. This comparison showed 1257 differentially expressed genes in favorable and unfavorable cases. Among these genes, 15 out of 60 hub genes were significantly associated with five-year event-free survival (EFS), and 10 had significant diagnostic value for predicting ACT outcomes in an independent microarray dataset of pediatric adrenocortical carcinomas (GSE76019). Overexpression of N4BP2, HSPB6, JUN, APBB1IP, STK17B, CSNK1D, and KDM3A was associated with poorer EFS, whereas lower expression of ISCU, PTPR, PRKAB2, CD48, PRF1, ITGAL, KLK15, and HIST1H3J was associated with worse outcomes. Collectively, these findings underscore the prognostic significance of these hub genes and suggest that they play a potential role in pediatric ACT progression and are useful predictors of clinical outcomes.

Heat shock proteins (HSPs) in non-alcoholic fatty liver disease (NAFLD): from molecular mechanisms to therapeutic avenues

Non-alcoholic fatty liver disease (NAFLD), a spectrum of liver conditions characterized by fat accumulation without excessive alcohol consumption, represents a significant global health burden. The intricate molecular landscape underlying NAFLD pathogenesis involves lipid handling, inflammation, oxidative stress, and mitochondrial dysfunction, with endoplasmic reticulum (ER) stress emerging as a key contributor. ER stress triggers the unfolded protein response (UPR), impacting hepatic steatosis in NAFLD and contributing to inflammation, fibrosis, and progression to NASH and eventually hepatocellular carcinoma (HCC). Heat shock proteins (HSPs), including small HSPs such as HSP20 and HSP27, HSP60, HSP70, GRP78, and HSP90, are integral to cellular stress responses. They aid in protein folding, prevent aggregation, and facilitate degradation, thus mitigating cellular damage under stress conditions. In NAFLD, aberrant HSP expression and function contribute to disease pathogenesis. Understanding the specific roles of HSP subtypes in NAFLD offers insights into potential therapeutic interventions. This review discusses the involvement of HSPs in NAFLD pathophysiology and highlights their therapeutic potential. By elucidating the molecular mechanisms underlying HSP-mediated protection in NAFLD, this article aims to pave the way for the development of targeted therapies for this prevalent liver disorder.

Identification of 10 differentially expressed genes involved in the tumorigenesis of cervical cancer via next-generation sequencing

Background

The incidence and mortality of cervical cancer remain high in female malignant tumors worldwide. There is still a lack of diagnostic and prognostic markers for cervical carcinoma. This study aimed to screen differentially expressed genes (DEGs) between normal and cervical cancer tissues to identify candidate genes for further research.

Methods

Uterine cervical specimens were resected from our clinical patients after radical hysterectomy. Three patients' transcriptomic datasets were built by the next generation sequencing (NGS) results. DEGs were selected through the edgeR and DESeq2 packages in the R environment. Functional enrichment analysis, including GO/DisGeNET/KEGG/Reactome enrichment analysis, was performed. Normal and cervical cancer tissue data from the public databases TCGA and GTEx were collected to compare the expression levels of 10 selected DEGs in tumor and normal tissues. ROC curve and survival analysis were performed to compare the diagnostic and prognostic values of each gene. The expression levels of candidate genes were verified in 15 paired clinical specimens via quantitative real-time polymerase chain reaction.

Results

There were 875 up-regulated and 1,482 down-regulated genes in cervical cancer samples compared with the paired adjacent normal cervical tissues according to the NGS analysis. The top 10 DEGs included APOD, MASP1, ACKR1, C1QTNF7, SFRP4, HSPB6, GSTM5, IGFBP6, F10 and DCN. GO, DisGeNET and Reactome analyses revealed that the DEGs were related to extracellular matrix and angiogenesis which might influence tumorigenesis. KEGG enrichment showed that PI3K-Akt signaling pathway might be involved in cervical cancer tumorigenesis and progression. The expression levels of selected genes were decreased in tumors in both the public database and our experimental clinical specimens. All the candidate genes showed excellent diagnostic value, and the AUC values exceeded 0.90. Additionally, APOD, ACKR1 and SFRP4 expression levels could help predict the prognosis of patients with cervical cancer.

Conclusions

In this study, we selected the top 10 DEGs which were down-regulated in cervical cancer tissues. All of them had dramatically diagnostic value. APOD, ACKR1 and SFRP4 were associated with the survivals of cervical cancer. C1QTNF7, HSPB6, GSTM5, IGFBP6 and F10 were first reported to be candidate genes of cervical carcinoma.

Heat Shock Proteins, a Double-Edged Sword: Significance in Cancer Progression, Chemotherapy Resistance and Novel Therapeutic Perspectives

Heat shock proteins (Hsps) are involved in one of the adaptive mechanisms protecting cells against environmental and metabolic stress. Moreover, the large role of these proteins in the carcinogenesis process, as well as in chemoresistance, was noticed. This review aims to draw attention to the possibilities of using Hsps in developing new cancer therapy methods, as well as to indicate directions for future research on this topic. In order to discuss this matter, a thorough review of the latest scientific literature was carried out, taking into account the importance of selected proteins from the Hsp family, including Hsp27, Hsp40, Hsp60, Hsp70, Hsp90 and Hsp110. One of the more characteristic features of all Hsps is that they play a multifaceted role in cancer progression, which makes them an obvious target for modern anticancer therapy. Some researchers emphasize the importance of directly inhibiting the action of these proteins. In turn, others point to their possible use in the design of cancer vaccines, which would work by inducing an immune response in various types of cancer. Due to these possibilities, it is believed that the use of Hsps may contribute to the progress of oncoimmunology, and thus help in the development of modern anticancer therapies, which would be characterized by higher effectiveness and lower toxicity to the patients.

MSF-PFP: A Novel Multisource Feature Fusion Model for Protein Function Prediction

Protein function prediction is essential for disease treatment and drug development; yet, traditional biological experimental methods are less efficient in annotating protein function, and existing automated methods fail to fully leverage protein multisource data. Here, we present MSF-PFP, a computational framework that fuses multisource data features to predict protein function with high accuracy. Our framework designs specific models for feature extraction based on the characteristics of various data sources, including a global-local-individual strategy for local location features. MSF-PFP then integrates extracted features through a multisource feature fusion model, ultimately categorizing protein functions. Experimental results demonstrate that MSF-PFP outperforms eight state-of-the-art models, achieving FMax scores of 0.542, 0.675, and 0.624 for the biological process (BP), molecular function (MF), and cellular component (CC), respectively. The source code and data set for MSF-PFP are available at https://swanhub.co/TianGua/MSF-PFP, facilitating further exploration and validation of the proposed framework. This study highlights the potential of multisource data fusion in enhancing protein function prediction, contributing to improved disease therapy and medication discovery strategies.