Development and validation of a six-RNA binding proteins prognostic signature and candidate drugs for prostate cancer

The RNA binding proteins LARP4A and LARP4B promote sarcoma and carcinoma growth and metastasis

RNA-binding proteins (RBPs) are emerging as important regulators of cancer pathogenesis. We reveal that the RBPs LARP4A and LARP4B are differentially overexpressed in osteosarcoma and osteosarcoma lung metastases, as well as in prostate cancer. Depletion of LARP4A and LARP4B reduced tumor growth and metastatic spread in xenografts, as well as inhibiting cell proliferation, motility, and migration. Transcriptomic profiling and high-content multiparametric analyses unveiled a central role for LARP4B, but not LARP4A, in regulating cell cycle progression in osteosarcoma and prostate cancer cells, potentially through modulating key cell cycle proteins such as Cyclins B1 and E2, Aurora B, and E2F1. This first systematic comparison between LARP4A and LARP4B assigns new pro-tumorigenic functions to LARP4A and LARP4B in bone and prostate cancer, highlighting their similarities while also indicating distinct functional differences. Uncovering clear biological roles for these paralogous proteins provides new avenues for identifying tissue-specific targets and potential druggable intervention.

Identifying the tumor immune microenvironment-associated prognostic genes for prostate cancer

PURPOSE

This study aimed to explore novel tumor immune microenvironment (TIME)-associated biomarkers in prostate adenocarcinoma (PRAD).

METHODS

PRAD RNA-sequencing data were obtained from UCSC Xena database as the training dataset. The ESTIMATE package was used to evaluate stromal, immune, and tumor purity scores. Differentially expressed genes (DEGs) related to TIME were screened using the immune and stromal scores. Gene functions were analyzed using DAVID. The LASSO method was performed to screen prognostic TIME-related genes. Kaplan-Meier curves were used to evaluate the prognosis of samples. The correlation between the screened genes and immune cell infiltration was explored using Tumor IMmune Estimation Resource. The GSE70768 dataset from the Gene Expression Omnibus was used to validate the expression of the screened genes.

RESULTS

The ESTIMATE results revealed that high immune, stromal, and ESTIMATE scores and low tumor purity had better prognoses. Function analysis indicated that DEGs are involved in the cytokine-cytokine receptor interaction signaling pathway. In TIME-related DEGs, METTL7B, HOXB8, and TREM1 were closely related to the prognosis. Samples with low expression levels of METTL7B, HOXB8, and TREM1 had better survival times. Similarly, both the validation dataset and qRT-PCR suggested that METTL7B, HOXB8, and TREM1 were significantly decreased. The three genes showed a positive correlation with immune infiltration.

CONCLUSIONS

This study identified three TIME-related genes, namely, METTL7B, HOXB8, and TREM1, which correlated with the prognosis of patients with PRAD. Targeting the TIME-related genes might have important clinical implications when making decisions for immunotherapy in PRAD.

Systematic review and integrated analysis of prognostic gene signatures for prostate cancer patients

Prostate cancer (PC) is one of the most common cancers in men and becoming the second leading cause of cancer fatalities. At present, the lack of effective strategies for prognosis of PC patients is still a problem to be solved. Therefore, it is significant to identify potential gene signatures for PC patients' prognosis. Here, we summarized 71 different prognostic gene signatures for PC and concluded 3 strategies for signature construction after extensive investigation. In addition, 14 genes frequently appeared in 71 different gene signatures, which enriched in mitotic and cell cycle. This review provides extensive understanding and integrated analysis of current prognostic signatures of PC, which may help researchers to construct gene signatures of PC and guide future clinical treatment.

High SNRPA1 expression leads to poor prognosis in patients with lung adenocarcinoma

OBJECTIVE

SNRPA1, a subunit of spliceosome complex, has been implicated in diverse cancers, while its biological effect in LUAD remains elusive. Therefore, we sought to decipher the relationship between SNRPA1 expression and the prognosis of patients with LUAD and reveal the underlying molecular mechanism.

MATERIALS AND METHODS

Based on the clinical data from TCGA databases, the multivariate Cox model was constructed to screen the prognostic value of SNRPA1. qRT-PCR and immunohistochemical staining were used to examine SNRPA1 mRNA and protein expression in LUAD. The effect of SNRPA1 on LUAD cell proliferation, migration, and epithelial mesenchymal transformation were examined using colony formation assays, wound healing, and western blot assays, respectively. Finally, the influence of SNRPA1 on LUAD immune microenvironment were validated from the Tumor Immune Estimation Resource database.

RESULTS

SNRPA1 was significantly upregulated in both LUAD tissues and cell lines, and highly expressed SNRPA1 contributed to poor prognosis of LUAD patients. In vitro, SNRPA1 knockdown inhibited the proliferation and migration, as well as delayed the EMT differentiation of LUAD cells. Lastly, SNRPA1 was found to be positively associated with immune infiltration and some immune-check-point markers.

CONCLUSIONS

Our findings indicate that SNRPA1 may be a new biomarker for prognostic prediction and a potential therapeutic target in the treatment of LUAD.

Exploration of prognostic genes and risk signature in breast cancer patients based on RNA binding proteins associated with ferroptosis

Background: Breast cancer (BRCA) is a life-threatening malignancy in women with an unsatisfactory prognosis. The purpose of this study was to explore the prognostic biomarkers and a risk signature based on ferroptosis-related RNA-binding proteins (FR-RBPs). Methods: FR-RBPs were identified using Spearman correlation analysis. Differentially expressed genes (DEGs) were identified by the "limma" R package. The univariate Cox and multivariate Cox analyses were executed to determine the prognostic genes. The risk signature was constructed and verified with the training set, testing set, and validation set. Mutation analysis, immune checkpoint expression analysis in high- and low-risk groups, and correlation between risk signature and chemotherapeutic agents were conducted using the "maftools" package, "ggplot2" package, and the CellMiner database respectively. The Human Protein Atlas (HPA) database was employed to confirm protein expression trends of prognostic genes in BRCA and normal tissues. The expression of prognostic genes in cell lines was verified by Real-time quantitative polymerase chain reaction (RT-qPCR). Kaplan-meier (KM) plotter database analysis was applied to predict the correlation between the expression levels of signature genes and survival statuses. Results: Five prognostic genes (GSPT2, RNASE1, TIPARP, TSEN54, and SAMD4A) to construct an FR-RBPs-related risk signature were identified and the risk signature was validated by the International Cancer Genome Consortium (ICGC) cohort. Univariate and multivariate Cox regression analysis demonstrated the risk score was a robust independent prognostic factor in overall survival prediction. The Tumor Mutational Burden (TMB) analysis implied that the high- and low-risk groups responded differently to immunotherapy. Drug sensitivity analysis suggested that the risk signature may serve as a chemosensitivity predictor. The results of GSEA suggested that five prognostic genes might be related to DNA replication and the immune-related pathways. RT-qPCR results demonstrated that the expression trends of prognostic genes in cell lines were consistent with the results from public databases. KM plotter database analysis suggested that high expression levels of GSPT2, RNASE1, and SAMD4A contributed to poor prognoses. Conclusion: In conclusion, this study identified the FR-RBPs-related prognostic genes and developed an FR-RBPs-related risk signature for the prognosis of BRCA, which will be of great significance in developing new therapeutic targets and prognostic molecular biomarkers for BRCA.

Gene expression analysis reveals a 5-gene signature for progression-free survival in prostate cancer

Prostate cancer (PCa) is the second most common male cancer worldwide, but effective biomarkers for the presence or progression risk of disease are currently elusive. In a series of nine matched histologically confirmed PCa and benign samples, we carried out an integrated transcriptome-wide gene expression analysis, including differential gene expression analysis and weighted gene co-expression network analysis (WGCNA), which identified a set of potential gene markers highly associated with tumour status (malignant vs. benign). We then used these genes to establish a minimal progression-free survival (PFS)-associated gene signature (GS) (PCBP1, PABPN1, PTPRF, DANCR, and MYC) using least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox regression analyses from The Cancer Genome Atlas prostate adenocarcinoma (TCGA-PRAD) dataset. Our signature was able to predict PFS over 1, 3, and 5 years in TCGA-PRAD dataset, with area under the curve (AUC) of 0.64–0.78, and our signature remained as a prognostic factor independent of age, Gleason score, and pathological T and N stages. A nomogram combining the signature and Gleason score demonstrated improved predictive capability for PFS (AUC: 0.71–0.85) and was superior to the Cambridge Prognostic Group (CPG) model alone and some conventionally used clinicopathological factors in predicting PFS. In conclusion, we have identified and validated a novel five-gene signature and established a nomogram that effectively predicted PFS in patients with PCa. Findings may improve current prognosis tools for PFS and contribute to clinical decision-making in PCa treatment.

Characterization of a Pyroptosis-Related Signature for Prognosis Prediction and Immune Microenvironment Infiltration in Prostate Cancer

This study was aimed at constructing a pyroptosis-related signature for prostate cancer (PCa) and elucidating the prognosis and immune landscape and the sensitivity of immune checkpoint blockade (ICB) therapy in signature-define subgroups of PCa. We identified 22 differentially expressed pyroptosis-related genes in PCa from The Cancer Genome Atlas (TCGA) database. The pyroptosis-related genes could divide PCa patients into two clusters with differences in survival. Seven genes were determined to construct a signature that was confirmed by qRT-PCR to be closely associated with the biological characteristics of malignant PCa. The signature could effectively and independently predict the biochemical recurrence (BCR) of PCa, which was validated in the GSE116918 and GSE21034. We found that patients in the high-risk group were more prone to BCR and closely associated with high-grade and advanced-stage disease progression. Outperforming clinical characteristics and nine published articles, our signature demonstrated excellent predictive performance. The patients in the low-risk group were strongly related to the high infiltration of various immune cells including CD8+ T cells and plasma B cells. Furthermore, the high-risk group with higher TMB levels and expression of immune checkpoints was more likely to benefit from immune checkpoint therapy such as PD-1 and CTLA-4 inhibitors. The sensitivity to chemotherapy, endocrine, and targeted therapy showed significant differences in the two risk groups. Our signature was a novel therapeutic strategy to distinguish the prognosis and guide treatment strategies.

Sarcopenia and a 5-mRNA risk module as a combined factor to predict prognosis for patients with stomach adenocarcinoma

BACKGROUND

Sarcopenia is an important factor affecting the prognostic outcomes in adult cancer patients. Gastric cancer is considered an age-related disease and is one of the leading causes of global cancer mortality. We aimed to establish an effective age-related model at a molecular level to predict the prognosis of patients with gastric cancer.

METHODS

TCGA STAD (stomach adenocarcinoma) and NCBI GEO database were utilized in this study to explore the expression, clinical relevance and prognostic value of age-related mRNAs in stomach adenocarcinoma through an integrated bioinformatics analysis. WGCNA co-expression network, Univariate Cox regression analysis, LASSO regression and Multivariate Cox regression analysis were implemented to construct an age-related prognostic signature.

RESULTS

As a result, sarcopenia is not only an unfavorable factor for OS (overall survival) in patients with tumor of gastric (HR: 1.707, 95%CI: 1.437-2.026), but also increases the risk of postoperative complications in patients with gastric cancer (OR: 2.904, 95%CI: 2.150-3.922). A panel of 5 mRNAs (DCBLD1, DLC1, IGFBP1, RNASE1 and SPC24) were identified to dichotomize patients with significantly different OS and independently predicted the OS in TCGA STAD (HR = 3.044, 95%CI = 2.078-4.460, P < 0.001).

CONCLUSION

The study provided novel insights to understand STAD at a molecular level and indicated that the 5 mRNAs might act as independent promising prognosis biomarkers for STAD. Sarcopenia and the 5-mRNA risk module as a combined factor to predict prognosis may play an important role in clinical diagnosis.

The Cardiac Glycoside Deslanoside Exerts Anticancer Activity in Prostate Cancer Cells by Modulating Multiple Signaling Pathways

Simple Summary

Prostate cancer is a leading cause of cancer-related deaths among men, and novel therapies for advanced PCa are urgently needed. Cardiac glycosides are a group of attractive candidates for anticancer repurposing, but deslanoside has not been tested for a potential anticancer effect so far. This study aims to test the anticancer effect of deslanoside in PCa and investigate the underlying mechanisms. Deslanoside effectively inhibited colony formation and tumor growth in multiple prostate cancer cell lines. Such an inhibitory effect involved both the cell cycle arrest at G2/M and the induction of apoptosis. Deslanoside altered the expression of many genes, which belonged to various cancer-associated cellular processes and signaling pathways. Altered expression levels for 15 deslanoside-modulated genes correlate with recurrence-free survival or overall survival in PCa patients, some of which have not been implicated in cancer before. Therefore, deslanoside exerts anticancer activity in PCa cells by modulating gene expression.

Abstract

Prostate cancer (PCa) is a leading cause of cancer-related deaths among men worldwide, and novel therapies for advanced PCa are urgently needed. Cardiac glycosides represent an attractive group of candidates for anticancer repurposing, but the cardiac glycoside deslanoside has not been tested for potential anticancer activity so far. We found that deslanoside effectively inhibited colony formation in vitro and tumor growth in nude mice of PCa cell lines 22Rv1, PC-3, and DU 145. Such an anticancer activity was mediated by both the cell cycle arrest at G2/M and the induction of apoptosis, as demonstrated by different functional assays and the expression status of regulatory proteins of cell cycle and apoptosis in cultured cells. Moreover, deslanoside suppressed the invasion and migration of PCa cell lines. Genome-wide expression profiling and bioinformatic analyses revealed that 130 genes were either upregulated or downregulated by deslanoside in both 22Rv1 and PC-3 cell lines. These genes enriched multiple cellular processes, such as response to steroid hormones, regulation of lipid metabolism, epithelial cell proliferation and its regulation, and negative regulation of cell migration. They also enriched multiple signaling pathways, such as necroptosis, MAPK, NOD-like receptor, and focal adhesion. Survival analyses of the 130 genes in the TCGA PCa database revealed that 10 of the deslanoside-downregulated genes (ITG2B, CNIH2, FBF1, PABPC1L, MMP11, DUSP9, TMEM121, SOX18, CMPK2, and MAMDC4) inversely correlated, while one deslanoside-upregulated gene (RASD1) positively correlated, with disease-free survival in PCa patients. In addition, one deslanoside-downregulated gene (ENG) inversely correlated, while three upregulated genes (JUN, MXD1, and AQP3) positively correlated with overall survival in PCa patients. Some of the 15 genes have not been implicated in cancer before. These findings provide another candidate for repurposing cardiac glycosides for anticancer drugs. They also suggest that a diverse range of molecular events underlie deslanoside’s anticancer activity in PCa cells.

RNA-binding proteins modulate drug sensitivity of cancer cells

As our understanding of the complex network of regulatory pathways for gene expression continues to grow, avenues of investigation for how these new findings can be utilised in therapeutics are emerging. The recent growth of interest in the RNA binding protein (RBP) interactome has revealed it to be rich in targets linked to, and causative of diseases. While this is, in and of itself, very interesting, evidence is also beginning to arise for how the RBP interactome can act to modulate the response of diseases to existing therapeutic treatments, especially in cancers. Here we highlight this topic, providing examples of work that exemplifies such modulation of chemotherapeutic sensitivity.

Proteins from the DNA Damage Response: Regulation, Dysfunction, and Anticancer Strategies

Cells respond to genotoxic stress through a series of complex protein pathways called DNA damage response (DDR). These monitoring mechanisms ensure the maintenance and the transfer of a correct genome to daughter cells through a selection of DNA repair, cell cycle regulation, and programmed cell death processes. Canonical or non-canonical DDRs are highly organized and controlled to play crucial roles in genome stability and diversity. When altered or mutated, the proteins in these complex networks lead to many diseases that share common features, and to tumor formation. In recent years, technological advances have made it possible to benefit from the principles and mechanisms of DDR to target and eliminate cancer cells. These new types of treatments are adapted to the different types of tumor sensitivity and could benefit from a combination of therapies to ensure maximal efficiency.

Identification of a three-gene-based prognostic model in multiple myeloma using bioinformatics analysis

Background

Multiple myeloma (MM), the second most hematological malignancy, has high incidence and remains incurable till now. The pathogenesis of MM is poorly understood. This study aimed to identify novel prognostic model for MM on gene expression profiles.

Methods

Gene expression datas of MM (GSE6477, GSE136337) were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in GSE6477 between case samples and normal control samples were screened by the limma package. Meanwhile, enrichment analysis was conducted, and a protein-protein interaction (PPI) network of these DEGs was established by STRING and cytoscape software. Co-expression modules of genes were built by Weighted Correlation Network Analysis (WGCNA). Key genes were identified both from hub genes and the DEGs. Univariate and multivariate Cox congression were performed to screen independent prognostic genes to construct a predictive model. The predictive power of the model was evaluated by Kaplan–Meier curve and time-dependent receiver operating characteristic (ROC) curves. Finally, univariate and multivariate Cox regression analyse were used to investigate whether the prognostic model could be independent of other clinical parameters.

Results

GSE6477, including 101 case and 15 normal control, were screened as the datasets. A total of 178 DEGs were identified, including 59 up-regulated and 119 down-regulated genes. In WGCNA analysis, module black and module purple were the most relevant modules with cancer traits, and 92 hub genes in these two modules were selected for further analysis. Next, 47 genes were chosen both from the DEGs and hub genes as key genes. Three genes (LYVE1, RNASE1, and RNASE2) were finally screened by univariate and multivariate Cox regression analyses and used to construct a risk model. In addition, the three-gene prognostic model revealed independent and accurate prognostic capacity in relation to other clinical parameters for MM patients.

Conclusion

In summary, we identified and constructed a three-gene-based prognostic model that could be used to predict overall survival of MM patients.

Interaction Between LncRNA and UPF1 in Tumors

Long non-coding RNAs (LncRNAs) can bind to other proteins or RNAs to regulate gene expression, and its role in tumors has been extensively studied. A common RNA binding protein, UPF1, is also a key factor in a variety of RNA decay pathways. RNA decay pathways serve to control levels of particular RNA molecules. The expression of UPF1 is often dysregulated in tumors, an observation which suggests that UPF1 contributes to development of a variety of tumors. Herein, we review evidence from studies of fourteen lncRNAs interact with UPF1. The interaction between lncRNA and UPFI provide fundamental basis for cell transformation and tumorigenic growth.