Identification of the key genes and pathways in prostate cancer

RPS21 Enhances hepatocellular carcinoma development through GPX4 stabilization

The study highlights that RPS21, a gene encoding a component of the 40S ribosomal subunit, plays an oncogenic role in hepatocellular carcinoma (HCC) and may influence tumor aggressiveness by affecting antioxidant capacity. RPS21 was found to be upregulated in HCC through RNA-sequencing of clinical samples and analysis of the TCGA database. Kaplan-Meier survival analyses linked higher RPS21 expression to lower survival rates across multiple metrics (OS, PFS, RFS, DSS). Mutation analysis via the cBioPortal showed that primarily amplifications in RPS21 are associated with a poorer prognosis. Tissue microarrays confirmed higher RPS21 levels in tumor samples, which were associated with more advanced clinical stages and grades. Experimental interventions involving lentiviral knockdown or overexpression of RPS21 significantly altered HCC cell proliferation and migration. These findings were supported by mouse models, which showed impacts on tumor growth and metastasis. Further mechanistic studies indicated that RPS21 modulates the ubiquitination and stability of GPX4, a key player in ferroptosis and oxidative stress regulation in HCC cells. This comprehensive study, which merges bioinformatic analysis with laboratory research, positions RPS21 as a viable target for HCC therapy and opens new pathways for understanding and treating liver cancer.

Quantifying UV-induced photodamage for longitudinal live-cell imaging applications of deep-UV microscopy

Deep-UV microscopy enables high-resolution, label-free molecular imaging by leveraging biomolecular absorption properties in the UV spectrum. Recent advances in UV-imaging hardware have renewed interest in this technique for quantitative live cell imaging applications. However, UV-induced photodamage remains a concern for longitudinal dynamic imaging studies. Here, we quantify UV phototoxicity with several cell types at notable UV wavelengths. We find that the fluence required for cell death via UV phototoxicity with continuous UV exposure varies with cell type and wavelength from ∼0.5µJ/µm2 to 2µJ/µm2, but is independent of typical illumination power/radiant flux of UV microscopy (e.g., 0.1-20 nW/µm2). We also show results from fractionation studies that reveal cell repair following UV exposure, which increases the tolerance to UV radiation by a factor of 2 or more, depending on the fractionation paradigm. Results further show that UV tolerance exceeds ANSI guidelines for maximum permissible exposure. Finally, we calculate imaging limits for a typical application of UV microscopy, such as hematology analysis. Together, this work provides UV fluence thresholds that can serve as guidelines for nondestructive, longitudinal, and dynamic deep-UV microscopy experiments.

Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States

Prostate cancer is a significant health concern and the most commonly diagnosed cancer in men worldwide. Understanding the complex process of prostate tumor evolution and progression is crucial for improved diagnosis, treatments, and patient outcomes. Previous studies have focused on unraveling the dynamics of prostate cancer evolution using phylogenetic or lineage analysis approaches. However, those approaches have limitations in capturing the complete disease process or incorporating genomic and transcriptomic variations comprehensively. In this study, we applied a novel computational approach to derive a prostate cancer progression model using multidimensional data from 497 prostate tumor samples and 52 tumor-adjacent normal samples obtained from The Cancer Genome Atlas study. The model was validated using data from an independent cohort of 545 primary tumor samples. By integrating transcriptomic and genomic data, our model provides a comprehensive view of prostate tumor progression, identifies crucial signaling pathways and genetic events, and uncovers distinct transcription signatures associated with disease progression. Our findings have significant implications for cancer research and hold promise for guiding personalized treatment strategies in prostate cancer.

SIGNIFICANCE

We developed and validated a progression model of prostate cancer using >1,000 tumor and normal tissue samples. The model provided a comprehensive view of prostate tumor evolution and progression.

Low FHL1 expression indicates a good prognosis and drug sensitivity in ovarian cancer

Chemotherapy resistance is the main reason for the poor prognosis of ovarian cancer (OC). FHL1 is an important tumour regulator, but its relationship with the prognosis, drug resistance, and tumour microenvironment of OC is unknown. Immunohistochemistry was used to determine FHL1 expression in OC. Kaplan‒Meier plotter was used for survival analysis. The value of gene expression in predicting drug resistance was estimated using the area under the curve (AUC). Bivariate correlation was used to determine the coexpression of two genes. Functional cluster and pathway enrichment were used to uncover hidden signalling pathways. The relationship between gene levels and the tumour microenvironment was visualised through the ggstatsplot and pheatmap packages. The mRNA and protein levels of FHL1 were downregulated in 426 and 100 OC tissues, respectively. Low FHL1 expression was correlated with good progression-free survival (PFS), postprogression survival, and overall survival (OS) in 1815 OC patients, and was further confirmed to be associated with good OS by immunohistochemistry in 152 OC tissues. Furthermore, FHL1 was downregulated in drug-sensitive tissues, while its high expression predicted drug resistance (AUC > 0.65). Mechanistically, FHL1 was coexpressed with FLNC, CAV1, PPP1R12B, and FLNA at the mRNA and protein levels in 558 and 174 OC tissues, respectively, and their expression was downregulated in OC. Additionally, very strong coexpression of FHL1 with the four genes was identified in at least 23 different tumours. Low expression of the four genes was associated with good PFS, and the combination of FHL1 with the four genes provided better prognostic power. Meanwhile, the expression of all five genes was strongly and positively associated with the abundance of macrophages. Low FHL1 expression acts as a favourable factor in OC, probably via positive coexpression with FLNC, CAV1, PPP1R12B, and FLNA.

Identification of novel N7-methylguanine-related gene signatures associated with ulcerative colitis and the association with biological therapy

OBJECTIVE

Ulcerative colitis (UC) is an inflammatory disease characterized by recurrent episodes of chronic intestinal inflammation. It is closely associated with immune dysregulation in the intestines. However, the mechanisms underlying the role of immune-related N7-methylguanosine (m7G) internal modification in UC remain unclear.

METHODS

We conducted a screening of differentially expressed genes (DEGs) associated with m7G and performed immune infiltration analysis. We then investigated the correlation between m7G-related DEGs and immune cells or pathways. To further explore the functional implications, we conducted functional enrichment analysis to identify gene modules that strongly correlated with hub gene expression. In addition, we constructed a miRNA regulatory network for the hub genes in UC. Furthermore, we examined the association between hub genes and disease remission in UC patients undergoing biologic therapy.

RESULTS

We obtained 13 m7G-related DEGs and conducted an in-depth analysis of immune infiltration. Among them, we identified five hub genes (NUDT7, NUDT12, POLR2H, QKI, and PRKCB) that showed diagnostic potential for UC. Through WGCNA and KEGG analysis, we found that gene modules strongly correlated with m7G hub gene expression were enriched in inflammation-related pathways. Furthermore, Kaplan-Meier survival analysis revealed a significant association between changes in hub gene expression levels and disease remission in UC patients undergoing biologic therapy.

CONCLUSION

The findings of this study demonstrate that five m7G-related DEGs, including the m7G-modified recognition protein QKI, play a key role in the occurrence and progression of UC intestinal inflammation, which is closely related to intestinal immunity. These results provide valuable insights into the mechanisms of m7G modification in UC development and offer new perspectives for exploring novel therapeutic targets for UC.

Never a dull enzyme, RNA polymerase II

RNA polymerase II (Pol II) is composed of 12 subunits that collaborate to synthesize mRNA within the nucleus. Pol II is widely recognized as a passive holoenzyme, with the molecular functions of its subunits largely ignored. Recent studies employing auxin-inducible degron (AID) and multi-omics techniques have revealed that the functional diversity of Pol II is achieved through the differential contributions of its subunits to various transcriptional and post-transcriptional processes. By regulating these processes in a coordinated manner through its subunits, Pol II can optimize its activity for diverse biological functions. Here, we review recent progress in understanding Pol II subunits and their dysregulation in diseases, Pol II heterogeneity, Pol II clusters and the regulatory roles of RNA polymerases.

Ribosomal protein L22-like1 (RPL22L1) mediates sorafenib sensitivity via ERK in hepatocellular carcinoma

Precision medicine in hepatocellular carcinoma (HCC) relies on validated biomarkers that help subgroup patients for targeted treatment. Here, we identified a novel candidate oncogene, ribosomal protein L22-like1 (RPL22L1), which was markedly elevated in HCC, contributed to HCC malignancy and adverse patient survival. Functional studies indicated RPL22L1 overexpression accelerated cell proliferation, migration, invasion and sorafenib resistance. Mechanism studies revealed that RPL22L1 activated ERK to induce atypical epithelial-to-mesenchymal transition (EMT) progress. Importantly, the ERK inhibitor (ERKi) could potentiate sorafenib efficiency in RPL22L1-high HCC cells. In summary, these data uncover RPL22L1 is a potential marker to guide precision therapy for utilizing ERKi to enhance the sorafenib efficacy in RPL22L1-high HCC patients.

Environmental Phenol and Paraben Exposure Risks and Their Potential Influence on the Gene Expression Involved in the Prognosis of Prostate Cancer

Prostate cancer (PCa) is one of the leading malignant tumors in US men. The lack of understanding of the molecular pathology on the risk of food supply chain exposures of environmental phenol (EP) and paraben (PB) chemicals limits the prevention, diagnosis, and treatment options. This research aims to utilize a risk assessment approach to demonstrate the association of EP and PB exposures detected in the urine samples along with PCa in US men (NHANES data 2005−2015). Further, we employ integrated bioinformatics to examine how EP and PB exposure influences the molecular pathways associated with the progression of PCa. The odds ratio, multiple regression model, and Pearson coefficients were used to evaluate goodness-of-fit analyses. The results demonstrated associations of EPs, PBs, and their metabolites, qualitative and quantitative variables, with PCa. The genes responsive to EP and PB exposures were identified using the Comparative Toxicogenomic Database (CTD). DAVID.6.8, GO, and KEGG enrichment analyses were used to delineate their roles in prostate carcinogenesis. The plug-in CytoHubba and MCODE completed identification of the hub genes in Cytoscape software for their roles in the PCa prognosis. It was then validated by using the UALCAN database by evaluating the expression levels and predictive values of the identified hub genes in prostate cancer prognosis using TCGA data. We demonstrate a significant association of higher levels of EPs and PBs in the urine samples, categorical and numerical confounders, with self-reported PCa cases. The higher expression levels of the hub genes (BUB1B, TOP2A, UBE2C, RRM2, and CENPF) in the aggressive stages (Gleason score > 8) of PCa tissues indicate their potential role(s) in the carcinogenic pathways. Our results present an innovative approach to extrapolate and validate hub genes responsive to the EPs and PBs, which may contribute to the severity of the disease prognosis, especially in the older population of US men.

Association of STAT3, PTPRT, TNK2-AS1, LINC-ROR Genes Expression Level with Prostate Cancer and Benign Prostatic Hyperplasia

Background: Prostate cancer (PCa) and benign prostate hyperplasia (BPH) are highly prevalent heterogeneous disorders among men. Since angiogenesis is the key step in cancer progression, the deregulation of genes involved in this process may play a role in cancer development. Objectives: We evaluated the expression level of 4 angiogenesis-related genes including signal transducer and activator of transcription 3 (STAT3), protein tyrosine phosphatase receptor type T (PTPRT), TNK2 antisense RNA 1 (TNK2-AS1), and long intergenic non-protein coding rna-regulator of reprogramming (LINC-ROR) in patients with PCa and BPH. Methods: The expression level of STAT3, PTPRT, TNK2-AS1, and LINC-ROR genes in tumoral and adjacent non-cancerous tissue (ANCT) samples of 50 PCa patients and tissue samples from 50 BPH patients were evaluated, using the real-time PCR method. The statistical analysis was performed to evaluate the association between genes expression and clinicopathological characteristics of patients with PCa. Results: The expression level of STAT3 and LINC-ROR was upregulated in tumoral tissues compared to ANCTs (P < 0.0001 for both). Only the expression level of STAT3 in PCa was higher than in BPH tissues (P = 0.001). The elevated expression of STAT3 was associated with the higher grade group of the tumor (P = 0.03). Also, the high expression level of PTPRT and LINC-ROR genes was associated with a higher stage of cancer in patients with PCa (P = 0.002, P = 0.0001 respectively). The STAT3 gene transcript level had an excellent diagnostic power for discrimination between tumoral tissue and the ANCTs with an area under the curve (AUC) of 0.93. Conclusions: The higher expression of STAT3 and LINC-ROR suggested a role in the pathogenesis of PCa in higher stages. Also, STAT3 expression level could be suggested as a potential biomarker for PCa in combination with PSA level.

Deregulation of ribosomal proteins in human cancers

The ribosome, the site for protein synthesis, is composed of ribosomal RNAs (rRNAs) and ribosomal proteins (RPs). The latter have been shown to have many ribosomal and extraribosomal functions. RPs are implicated in a variety of pathological processes, especially tumorigenesis and cell transformation. In this review, we will focus on the recent advances that shed light on the effects of RPs deregulation in different types of cancer and their roles in regulating the tumor cell fate.

Synergistic Tumor Cytolysis by NK Cells in Combination With a Pan-HDAC Inhibitor, Panobinostat

Histone deacetylases (HDAC) are frequently overexpressed in tumors, and their inhibition has shown promising anti-tumor effects. However, the synergistic effects of HDAC inhibition with immune cell therapy have not been fully explored. Natural killer (NK) cells are cytotoxic lymphocytes for anti-tumor immune surveillance, with immunotherapy potential. We showed that a pan-HDAC inhibitor, panobinostat, alone demonstrated anti-tumor and anti-proliferative activities on all tested tumors in vitro. Additionally, panobinostat co-treatment or pretreatment synergized with NK cells to mediate tumor cell cytolysis. Mechanistically, panobinostat treatment increased the expression of cell adhesion and tight junction-related genes, promoted conjugation formation between NK and tumor cells, and modulates NK cell-activating receptors and ligands on tumor cells, contributing to the increased tumor cytolysis. Finally, panobinostat therapy led to better tumor control and synergized with anti-PD-L1 therapy. Our data highlights the anti-tumor potential of HDAC inhibition through tumor-intrinsic toxicity and enhancement of NK -based immunotherapy.

Correlations Between the Characteristics of Alternative Splicing Events, Prognosis, and the Immune Microenvironment in Breast Cancer

Objective

Alternative splicing (AS) is the mechanism by which a few genes encode numerous proteins, and it redefines the concept of gene expression regulation. Recent studies showed that dysregulation of AS was an important cause of tumorigenesis and microenvironment formation. Therefore, we performed a systematic analysis to examine the role of AS in breast cancer (Breast Cancer, BrCa) progression.

Methods

The present study included 993 BrCa patients from The Cancer Genome Atlas (TCGA) database in the genome-wide analysis of AS events. We used differential and prognostic analyses and found differentially expressed alternative splicing (DEAS) events and independent prognostic factors related to patients' overall survival (OS) and disease-free survival (DFS). We divided the patients into two groups based on these AS events and analyzed their clinical features, molecular subtyping and immune characteristics. We also constructed a splicing factor (SF) regulation network for key AS events and verified the existence of AS events in tissue samples using real-time quantitative PCR.

Results

A total of 678 AS events were identified as differentially expressed, of which 13 and 10 AS events were independent prognostic factors of patients' OS and DFS, respectively. Unsupervised clustering analysis based on these prognostic factors indicated that the Cluster 1 group had a better prognosis and more immune cell infiltration. SFs were significantly related to the expression of AS events, and AA-RPS21 was significantly upregulated in tumors.

Conclusion

Alternative splicing expands the mechanism of breast cancer progression from a new perspective. Notably, alternative splicing may affect the patient's prognosis by affecting the infiltration of immune cells. Our research provides important guidance for subsequent studies of AS in breast cancer.

Biomarker Identification through Multiomics Data Analysis of Prostate Cancer Prognostication Using a Deep Learning Model and Similarity Network Fusion

Simple Summary

Around 30% of men treated with adjuvant therapy experience recurrences of prostate cancer (PC). Current monitoring of the relapse of PC requires regular postoperative prostate-specific antigen (PSA) value follow-up. Our study aims to identify potential multiomics biomarkers using modern computational analytic methods, deep learning (DL), similarity network fusion (SNF), and the Cancer Genome Atlas (TCGA) prostate adenocarcinoma (PRAD) dataset. Six significantly intersected omics biomarkers from the two models, TELO2, ZMYND19, miR-143, miR-378a, cg00687383 (MED4), and cg02318866 (JMJD6; METTL23) were collected for multiomics panel construction. The difference between the Kaplan–Meier curves of high and low recurrence-risk groups generated from the multiomics panels and clinical information achieve p-value = 2.97 × 10⁻¹⁵ and C-index = 0.713, and the prediction performance of five-year recurrence achieves AUC = 0.789. The results show that the multiomics panel provided valuable biomarkers for the early detection of high-risk recurrent patients, and integrating multiomics data gave us the power to detect the complex mechanisms of cancer among the interactions of different genetic and epigenetic factors.

Abstract

This study is to identify potential multiomics biomarkers for the early detection of the prognostic recurrence of PC patients. A total of 494 prostate adenocarcinoma (PRAD) patients (60-recurrent included) from the Cancer Genome Atlas (TCGA) portal were analyzed using the autoencoder model and similarity network fusion. Then, multiomics panels were constructed according to the intersected omics biomarkers identified from the two models. Six intersected omics biomarkers, TELO2, ZMYND19, miR-143, miR-378a, cg00687383 (MED4), and cg02318866 (JMJD6; METTL23), were collected for multiomics panel construction. The difference between the Kaplan–Meier curves of high and low recurrence-risk groups generated from the multiomics panel achieved p-value = 5.33 × 10⁻⁹, which is better than the former study (p-value = 5 × 10⁻⁷). Additionally, when evaluating the selected multiomics biomarkers with clinical information (Gleason score, age, and cancer stage), a high-performance prediction model was generated with C-index = 0.713, p-value = 2.97 × 10⁻¹⁵, and AUC = 0.789. The risk score generated from the selected multiomics biomarkers worked as an effective indicator for the prediction of PRAD recurrence. This study helps us to understand the etiology and pathways of PRAD and further benefits both patients and physicians with potential prognostic biomarkers when making clinical decisions after surgical treatment.

Prognostic Implication of the Expression Level of PECAM-1 in Non-small Cell Lung Cancer

Background: Lung cancer is a malignant disease that threatens human health. Hence, it is crucial to identify effective prognostic factors and treatment targets. Single-cell RNA sequencing can quantify the expression profiles of transcripts in individual cells. Methods: GSE117570 profiles were downloaded from the Gene Expression Omnibus database. Key ligand-receptor genes in the tumor and the normal groups were screened to identify integrated differentially expressed genes (DEGs) from the GSE118370 and The Cancer Genome Atlas Lung Adenocarcinoma databases. DEGs associated with more ligand-receptor pairs were selected as candidate DEGs for Gene Ontology (GO) functional annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and survival analysis. In addition, we conducted validation immunohistochemical experiments on postoperative specimens of 30 patients with lung cancer. Results: A total of 18 candidate DEGs were identified from the tumor and the normal groups. The analysis of the GO biological process revealed that these DEGs were mainly enriched in wound healing, in response to wounding, cell migration, cell motility, and regulation of cell motility, while the KEGG pathway analysis found that these DEGs were mainly enriched in proteoglycans in cancer, bladder cancer, malaria, tyrosine kinase inhibitor resistance in Epidermal Growth Factor Receptor (EGFR), and the ERBB signaling pathway. Survival analysis showed that a high, rather than a low, expression of platelet endothelial cell adhesion molecule-1 (PECAM-1) was associated with improved survival. Similarly, in postoperative patients with lung cancer, we found that the overall survival of the PECAM-1 high-expression group shows a better trend than the PECAM-1 low-expression group (p = 0.172). Conclusions: The candidate DEGs identified in this study may play some important roles in the occurrence and development of lung cancer, especially PECAM-1, which may present potential prognostic biomarkers for the outcome.

Genomic pan-cancer classification using image-based deep learning

Accurate cancer type classification based on genetic mutation can significantly facilitate cancer-related diagnosis. However, existing methods usually use feature selection combined with simple classifiers to quantify key mutated genes, resulting in poor classification performance. To circumvent this problem, a novel image-based deep learning strategy is employed to distinguish different types of cancer. Unlike conventional methods, we first convert gene mutation data containing single nucleotide polymorphisms, insertions and deletions into a genetic mutation map, and then apply the deep learning networks to classify different cancer types based on the mutation map. We outline these methods and present results obtained in training VGG-16, Inception-v3, ResNet-50 and Inception-ResNet-v2 neural networks to classify 36 types of cancer from 9047 patient samples. Our approach achieves overall higher accuracy (over 95%) compared with other widely adopted classification methods. Furthermore, we demonstrate the application of a Guided Grad-CAM visualization to generate heatmaps and identify the top-ranked tumor-type-specific genes and pathways. Experimental results on prostate and breast cancer demonstrate our method can be applied to various types of cancer. Powered by the deep learning, this approach can potentially provide a new solution for pan-cancer classification and cancer driver gene discovery. The source code and datasets supporting the study is available at https://github.com/yetaoyu/Genomic-pan-cancer-classification.

Microbial Characteristics and Safety of Dairy Manure ComPosting for Reuse as Dairy Bedding

Changes in bacterial community, phenotype, metabolic function, and pathogenic bacteria content in recycled manure solids (RMS) were analyzed by 16S rRNA sequencing, Bugbase, picrost2, and qPCR, respectively. The data from RMS bedding were compared to those of sand bedding and rice husk bedding. The results show that the proportion of potentially pathogenic bacteria among the manure flora of RMS after dry and wet separation, after composting, and after sun-cure storage was 74.00%, 26.03%, and 49.067%, respectively. Compared to RMS bedding, the proportion of potentially pathogenic microorganisms in sand bedding and rice husk bedding was higher. The picrust2 analyses show that the level of lipopolysaccharide biosynthesis changed significantly during RMS processing. In addition, the qPCR results show that composting could effectively reduce the detection and quantification of pathogens, except Streptococcus uberis, in RMS bedding. In general, composting is an essential step to improve the safety of bedding materials in the process of fecal treatment. However, at the same time, RMS bedding may increase the risk of mastitis caused by Streptococcus uberis.

An in-silico method leads to recognition of hub genes and crucial pathways in survival of patients with breast cancer

Breast cancer is a highly heterogeneous disorder characterized by dysregulation of expression of numerous genes and cascades. In the current study, we aim to use a system biology strategy to identify key genes and signaling pathways in breast cancer. We have retrieved data of two microarray datasets (GSE65194 and GSE45827) from the NCBI Gene Expression Omnibus database. R package was used for identification of differentially expressed genes (DEGs), assessment of gene ontology and pathway enrichment evaluation. The DEGs were integrated to construct a protein-protein interaction network. Next, hub genes were recognized using the Cytoscape software and lncRNA-mRNA co-expression analysis was performed to evaluate the potential roles of lncRNAs. Finally, the clinical importance of the obtained genes was assessed using Kaplan-Meier survival analysis. In the present study, 887 DEGs including 730 upregulated and 157 downregulated DEGs were detected between breast cancer and normal samples. By combining the results of functional analysis, MCODE, CytoNCA and CytoHubba 2 hub genes including MAD2L1 and CCNB1 were selected. We also identified 12 lncRNAs with significant correlation with MAD2L1 and CCNB1 genes. According to The Kaplan-Meier plotter database MAD2L1, CCNA2, RAD51-AS1 and LINC01089 have the most prediction potential among all candidate hub genes. Our study offers a framework for recognition of mRNA-lncRNA network in breast cancer and detection of important pathways that could be used as therapeutic targets in this kind of cancer.

MiR-25-3p Serves as an Oncogenic MicroRNA by Downregulating the Expression of Merlin in Osteosarcoma

PURPOSE

Moesin-ezrin-radixin-like protein (Merlin) has been identified as a tumor suppressor in several types of cancers. However, the biological function of Merlin in osteosarcoma remains unclear. MicroRNAs (miRNAs) can influence cancer progression by targeting oncogenes or anti-oncogenes. In this study, we sought to evaluate the regulation of Merlin expression by miR-25-3p and the role of the miR-25-3p/Merlin axis in osteosarcoma progression, with the aim of identifying a potential therapeutic target for osteosarcoma.

MATERIALS AND METHODS

TCGA (The Cancer Genome Atlas) database was used to analyze the correlation between Merlin expression and prognosis. RT-qPCR and Western blotting analyses were performed to compare Merlin expression between normal and malignant cells. A dual-luciferase reporter assay was performed to evaluate the direct targeting of Merlin by miR-25-3p. We overexpressed miR-25-3p, or/and Merlin, in U-2 OS and 143B cells, and studied their cellular functions in vitro. MTT and colony formation assays were performed to determine the effects on cell growth. EdU and cell cycle assays were performed to analyze the effects in cell replication. We used annexin V-fluorescein isothiocyanate and propidium iodide to stain apoptotic cells, and analyzed the cells using flow cytometry. The effects on cell metastasis were studied in wound healing and transwell assays. Lastly, the underlying mechanism was determined in RT-qPCR and Western blotting experiments.

RESULTS

Low Merlin expression was linked to poor prognosis. miR-25-3p was observed to directly target Merlin and downregulate its expression. miR-25-3p promoted cell growth, migration, and invasion, and inhibited apoptosis induced by cisplatin. Moreover, the overexpression of Merlin reversed the abovementioned effects of miR-25-3p. Further, the miR-25-3p/Merlin axis was observed to play an important role in the Hippo pathway, and regulated the expression of genes such as BIRC5, CTGF, and CYR61.

CONCLUSION

miR-25-3p functions as an oncogenic microRNA in osteosarcoma by targeting Merlin, and may serve as a potential therapeutic target for osteosarcoma.

Metabolic reprogramming-associated genes predict overall survival for rectal cancer

Metabolic reprogramming has become a hot topic recently in the regulation of tumour biology. Although hundreds of altered metabolic genes have been reported to be associated with tumour development and progression, the important prognostic role of these metabolic genes remains unknown. We downloaded messenger RNA expression profiles and clinicopathological data from The Cancer Genome Atlas and the Gene Expression Omnibus database to uncover the prognostic role of these metabolic genes. Univariate Cox regression analysis and lasso Cox regression model were utilized in this study to screen prognostic associated metabolic genes. Patients with high‐risk demonstrated significantly poorer survival outcomes than patients with low‐risk in the TCGA database. Also, patients with high‐risk still showed significantly poorer survival outcomes than patients with low‐risk in the GEO database. What is more, gene set enrichment analyses were performed in this study to uncover significantly enriched GO terms and pathways in order to help identify potential underlying mechanisms. Our study identified some survival‐related metabolic genes for rectal cancer prognosis prediction. These genes might play essential roles in the regulation of metabolic microenvironment and in providing significant potential biomarkers in metabolic treatment.

SIRPB1 promotes prostate cancer cell proliferation via Akt activation

BACKGROUND

Signal regulatory protein β1 (SIRPB1) is a signal regulatory protein member of the immunoglobulin superfamily and is capable of modulating receptor tyrosine kinase-coupled signaling. Copy number variations at the SIRPB1 locus were previously reported to associate with prostate cancer aggressiveness in patients, however, the role of SIRPB1 in prostate carcinogenesis is unknown.

METHODS

Fluorescence in situ hybridization and laser-capture microdissection coupled with quantitative polymerase chain reaction was utilized to determine SIRPB1 gene amplification and messenger RNA expression in prostate cancer specimens. The effect of knockdown of SIRPB1 by RNA interference in PC3 prostate cancer cells on cell growth in colony formation assays and cell mobility in wound-healing, transwell assays, and cell cycle analysis was determined. Overexpression of SIPRB1 in C4-2 prostate cancer cells on cell migration, invasion, colony formation and cell cycle progression and tumor take rate in xenografts was also determined. Western blot assay of potential downstream SIRPB1 pathways was also performed.

RESULTS

SIRPB1 gene amplification was detected in up to 37.5% of prostate cancer specimens based on in silico analysis of several publicly available datasets. SIRPB1 gene amplification and overexpression were detected in prostate cancer specimens. The knockdown of SIRPB1 significantly suppressed cell growth in colony formation assays and cell mobility. SIRPB1 knockdown also induced cell cycle arrest during the G₀ /G₁ phase and enhancement of apoptosis. Conversely, overexpression of SIPRB1 in C4-2 prostate cancer cells significantly enhanced cell migration, invasion, colony formation, and cell cycle progression and increased C4-2 xenograft tumor take rate in nude mice. Finally, this study presented evidence for SIRPB1 regulation of Akt phosphorylation and showed that Akt inhibition could abolish SIRPB1 stimulation of prostate cancer cell proliferation.

CONCLUSIONS

These results suggest that SIRPB1 is a potential oncogene capable of activating Akt signaling to stimulate prostate cancer proliferation and could be a biomarker for patients at risk of developing aggressive prostate cancer.

Identification of potential biomarkers and pivotal biological pathways for prostate cancer using bioinformatics analysis methods

Background

Prostate cancer (PCa) is a common urinary malignancy, whose molecular mechanism has not been fully elucidated. We aimed to screen for key genes and biological pathways related to PCa using bioinformatics method.

Methods

Differentially expressed genes (DEGs) were filtered out from the GSE103512 dataset and subjected to the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein–protein interactions (PPI) network was constructed, following by the identification of hub genes. The results of former studies were compared with ours. The relative expression levels of hub genes were examined in The Cancer Genome Atlas (TCGA) and Oncomine public databases. The University of California Santa Cruz Xena online tools were used to study whether the expression of hub genes was correlated with the survival of PCa patients from TCGA cohorts.

Results

Totally, 252 (186 upregulated and 66 downregulated) DEGs were identified. GO analysis enriched mainly in “oxidation-reduction process” and “positive regulation of transcription from RNA polymerase II promoter”; KEGG pathway analysis enriched mostly in “metabolic pathways” and “protein digestion and absorption.” Kallikrein-related peptidase 3, cadherin 1 (CDH1), Kallikrein-related peptidase 2 (KLK2), forkhead box A1 (FOXA1), and epithelial cell adhesion molecule (EPCAM) were identified as hub genes from the PPI network. CDH1, FOXA1, and EPCAM were validated by other relevant gene expression omnibus datasets. All hub genes were validated by both TCGA and Oncomine except KLK2. Two additional top DEGs (ABCC4 and SLPI) were found to be associated with the prognosis of PCa patients.

Conclusions

This study excavated the key genes and pathways in PCa, which might be biomarkers for diagnosis, prognosis, and potential therapeutic targets.

Identification of potential diagnostic and prognostic biomarkers for prostate cancer

Prostate cancer (PCa) is one of the most common malignant tumors worldwide. The aim of the present study was to determine potential diagnostic and prognostic biomarkers for PCa. The GSE103512 dataset was downloaded, and the differentially expressed genes (DEGs) were screened. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) analyses of DEGs were performed. The result of GO analysis suggested that the DEGs were mostly enriched in ‘carboxylic acid catabolic process’, ‘cell apoptosis’, ‘cell proliferation’ and ‘cell migration’. KEGG analysis results indicated that the DEGs were mostly concentrated in ‘metabolic pathways’, ‘ECM-receptor interaction’, the ‘PI3K-Akt pathway’ and ‘focal adhesion’. The PPI analysis results showed that Golgi membrane protein 1 (GOLM1), melanoma inhibitory activity member 3 (MIA3), ATP citrate lyase (ACLY) and G protein subunit β2 (GNB2) were the key genes in PCa, and the Module analysis revealed that they were associated with ‘ECM-receptor interaction’, ‘focal adhesion’, the ‘PI3K-Akt pathway’ and the ‘metabolic pathway’. Subsequently, the gene expression was confirmed using Gene Expression Profiling Interactive Analysis and the Human Protein Atlas. The results demonstrated that GOLM1 and ACLY expression was significantly upregulated (P<0.05) in PCa compared with that in normal tissues. Receiver operating characteristic and survival analyses were performed. The results showed that area under the curve values of these genes all exceeded 0.85, and high expression of these genes was associated with poor survival in patients with PCa. In conclusion, this study identified GOLM1 and ACLY in PCa, which may be potential diagnostic and prognostic biomarker of PCa.

Identification of Potential Biomarkers with Diagnostic Value in Pituitary Adenomas Using Prediction Analysis for Microarrays Method

Pituitary adenomas are the most common intrasellar tumors. Patients should be identified at an early stage so that effective treatment can be implemented. The study aims at detecting the potential biomarkers with diagnostic value of pituitary adenomas. Using a total of seven gene expression profiles (GEPs) of the datasets from the Gene Expression Omnibus (GEO) database, we first screened 1980 significant differentially expressed genes (DEGs). Then, we employed the prediction analysis for microarray (PAM) algorithm to identify 340 significant DEGs able to differ pituitary tumor from normal samples, which include 208 upregulated DEGs and 132 downregulated DEGs. DAVID database was used to carry out the enrichment analysis on Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathways. We found that upregulated candidates were enriched in protein folding and metabolic pathways. Downregulated DEGs saw a significant enrichment in insulin receptor signaling pathway and hedgehog signaling pathway. Based on the protein-protein interaction (PPI) network as well as module analysis, we determined ten hub genes including PHLPP, ENO2, ACTR1A, EHHADH, EHMT2, FOXO1, DLD, CCT2, CSNK1D, and CETN2 that could be potential biomarkers with diagnostic value in pituitary adenomas. In conclusion, the study contributes to reliable and potential molecular biomarkers with diagnostic value. Moreover, these potential biomarkers may be used for prognosis and new therapeutic targets for the pituitary adenomas.

Twiner: correlation-based regularization for identifying common cancer gene signatures

Background

Breast and prostate cancers are typical examples of hormone-dependent cancers, showing remarkable similarities at the hormone-related signaling pathways level, and exhibiting a high tropism to bone. While the identification of genes playing a specific role in each cancer type brings invaluable insights for gene therapy research by targeting disease-specific cell functions not accounted so far, identifying a common gene signature to breast and prostate cancers could unravel new targets to tackle shared hormone-dependent disease features, like bone relapse. This would potentially allow the development of new targeted therapies directed to genes regulating both cancer types, with a consequent positive impact in cancer management and health economics.

Results

We address the challenge of extracting gene signatures from transcriptomic data of prostate adenocarcinoma (PRAD) and breast invasive carcinoma (BRCA) samples, particularly estrogen positive (ER+), and androgen positive (AR+) triple-negative breast cancer (TNBC), using sparse logistic regression. The introduction of gene network information based on the distances between BRCA and PRAD correlation matrices is investigated, through the proposed twin networks recovery (twiner) penalty, as a strategy to ensure similarly correlated gene features in two diseases to be less penalized during the feature selection procedure.

Conclusions

Our analysis led to the identification of genes that show a similar correlation pattern in BRCA and PRAD transcriptomic data, and are selected as key players in the classification of breast and prostate samples into ER+ BRCA/AR+ TNBC/PRAD tumor and normal tissues, and also associated with survival time distributions. The results obtained are supported by the literature and are expected to unveil the similarities between the diseases, disclose common disease biomarkers, and help in the definition of new strategies for more effective therapies.