Dynamic expression of SNAI2 in prostate cancer predicts tumor progression and drug sensitivity

Zeb1-controlled metabolic plasticity enables remodeling of chromatin accessibility in the development of neuroendocrine prostate cancer

Cell plasticity has been found to play a critical role in tumor progression and therapy resistance. However, our understanding of the characteristics and markers of plastic cellular states during cancer cell lineage transition remains limited. In this study, multi-omics analyses show that prostate cancer cells undergo an intermediate state marked by Zeb1 expression with epithelial-mesenchymal transition (EMT), stemness, and neuroendocrine features during the development of neuroendocrine prostate cancer (NEPC). Organoid-formation assays and in vivo lineage tracing experiments demonstrate that Zeb1+ epithelioid cells are putative cells of origin for NEPC. Mechanistically, Zeb1 transcriptionally regulates the expression of several key glycolytic enzymes, thereby predisposing tumor cells to utilize glycolysis for energy metabolism. During this process, lactate accumulation-mediated histone lactylation enhances chromatin accessibility and cellular plasticity including induction of neuro-gene expression, which promotes NEPC development. Collectively, Zeb1-driven metabolic rewiring enables the epigenetic reprogramming of prostate cancer cells to license the adeno-to-neuroendocrine lineage transition.

Regulation of Hippo-YAP/CTGF signaling by combining an HDAC inhibitor and 5-fluorouracil in gastric cancer cells

Histone deacetylase (HDAC) inhibitors diminish carcinogenesis, metastasis, and cancer cell proliferation by inducing death in cancer cells. Tissue regeneration and organ development are highly dependent on the Hippo signaling pathway. Targeting the dysregulated hippo pathway is an excellent approach for cancer treatment. According to the results of this study, the combination of panobinostat, a histone deacetylase inhibitor, and 5-fluorouracil (5-FU), a chemotherapy drug, can act synergistically to induce apoptosis in gastric cancer cells. The combination of panobinostat and 5-FU was more effective in inhibiting cell viability than either treatment alone by elevating the protein levels of cleaved PARP and cleaved caspase-9. By specifically targeting E-cadherin, vimentin, and MMP-9, the combination of panobinostat and 5-FU significantly inhibited cell migration. Additionally, panobinostat significantly increased the anticancer effects of 5-FU by activating Hippo signaling (Mst 1 and 2, Sav1, and Mob1) and inhibiting the Akt signaling pathway. As a consequence, there was a decrease in the amount of Yap protein. The combination therapy of panobinostat with 5-FU dramatically slowed the spread of gastric cancer in a xenograft animal model by deactivating the Akt pathway and supporting the Hippo pathway. Since combination treatment exhibits much higher anti-tumor potential than 5-FU alone, panobinostat effectively potentiates the anti-tumor efficacy of 5-FU. As a result, it is believed that panobinostat and 5-FU combination therapy will be useful as supplemental chemotherapy in the future.

Tensin 1 regulated by hepatic leukemia factor represses the progression of prostate cancer

Hepatic leukemia factor (HLF), a transcription factor, is dysregulated in many cancers. This study investigates the function of HLF in prostate cancer (PCa) and its relation to tensin 1 (TNS1). Clinical tissues were collected from 24 PCa patients. Duke University 145 (DU145) and PC3 cells overexpressing HLF were established. HLF signaling was downregulated in PCa tissues compared to adjacent tissues and in DU145 and PC3 cells compared to prostate epithelial cells RWPE-1 or prostate stromal cells (WPMY-1). PCa cell lines with overexpression of HLF had reduced proliferative, migratory, and invasive activity, increased apoptosis, and cell mitosis mostly in the G0/G1 phase. HLF induced the TNS1 transcription to activate the p53 pathway. Depletion of TNS1 reversed the anti-tumor effects of HLF on PCa cells and tumor growth and metastasis in vivo. In summary, our findings suggest that HLF suppressed PCa progression by upregulating TNS1 expression and inducing the p53 pathway activation, which might provide insights into novel strategies for combating PCa.

Radiotranscriptomics identified new mRNAs and miRNA markers for distinguishing prostate cancer from benign prostatic hyperplasia

The present study investigated ultrasound (US) phenotypes reflecting prostate cancer (PCa)-related genetic mutations. Herein, integration of radiotranscriptomic data, US and contrast-enhanced ultrasound (CEUS) radiomic images, and RNA sequencing was performed with the aim of significantly improving the accuracy of PCa prognosis. We performed radiotranscriptomic analysis of clinical, imaging, and two genomic (mRNA and microRNA expression) datasets from 48 and 22 men with PCa and benign prostatic hyperplasia (BPH), respectively. Twenty-three US texture features and four microvascular perfusion features were associated with various patterns of 52 differentially expressed genes related to PCa (p < 0.05); 17 overexpressed genes were associated with two key texture features. Twelve overexpressed genes were identified using microvascular perfusion features. Furthermore, mRNA and miRNA biomarkers could be used to distinguish between PCa and BPH. Compared with RNA sequencing, B-mode and CEUS features reflected genomic alterations associated with hormone receptor status, angiogenesis, and prognosis in patients with PCa. These findings indicate the potential of US to assess biomarker levels in patients with PCa.

Comprehensively prognostic and immunological analysis of snail family transcriptional repressor 2 in pan-cancer and identification in pancreatic carcinoma

Background

Snail family transcriptional repressor 2 (SNAI2) is a transcription factor that induces epithelial to mesenchymal transition in neoplastic epithelial cells. It is closely related to the progression of various malignancies. However, the significance of SNAI2 in human pan-cancer is still largely unknown.

Methods

The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) databases were taken to examine the SNAI2 expression pattern in tissues and cancer cells. The link between SNAI2 gene expression levels and prognosis, as well as immune cell infiltration, was investigated using the Kaplan-Meier technique and Spearman correlation analysis. We also explored the expression and distribution of SNAI2 in various tumor tissues and cells by the THPA (Human Protein Atlas) database. We further investigated the relationship between SNAI2 expression levels and immunotherapy response in various clinical immunotherapy cohorts. Finally, the immunoblot was used to quantify the SNAI2 expression levels, and the proliferative and invasive ability of pancreatic cancer cells was determined by colony formation and transwell assays.

Results

We discovered heterogeneity in SNAI2 expression in different tumor tissues and cancer cell lines by exploring public datasets. The genomic alteration of SNAI2 existed in most cancers. Also, SNAI2 exhibits prognosis predictive ability in various cancers. SNAI2 was significantly correlated with immune-activated hallmarks, cancer immune cell infiltrations, and immunoregulators. It's worth noting that SNAI2 expression is significantly related to the effectiveness of clinical immunotherapy. SNAI2 expression was also found to have a high correlation with the DNA mismatch repair (MMR) genes and DNA methylation in many cancers. Finally, the knockdown of SNAI2 significantly weakened the proliferative and invasive ability of pancreatic cancer cells.

Conclusion

These findings suggested that SNAI2 could be used as a biomarker in human pan-cancer to detect immune infiltration and poor prognosis, which provides a new idea for cancer treatment.

Lipid metabolism-related miRNAs with potential diagnostic roles in prostate cancer

BACKGROUND

Prostate cancer (PCa), the second most prevalent solid tumor among men worldwide, has caused greatly increasing mortality in PCa patients. The effects of lipid metabolism on tumor growth have been explored, but the mechanistic details of the association of lipid metabolism disorders with PCa remain largely elusive.

METHODS

The RNA sequencing data of the GSE45604 and The Cancer Genome Atlas-Prostate Adenocarcinoma (TCGA-PRAD) datasets were extracted from the Gene Expression Omnibus (GEO) and UCSC Xena databases, respectively. The Molecular Signatures Database (MSigDB) was utilized to identify lipid metabolism-related genes. The limma R package was used to identify differentially expressed lipid metabolism-related genes (DE-LMRGs) and differentially expressed microRNAs (DEMs). Moreover, least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (XGBoost), and support vector machine-recursive feature elimination (SVM-RFE) were applied to select signature miRNAs and construct a lipid metabolism-related diagnostic model. The expression levels of selected differentially expressed lipid metabolism-related miRNAs (DE-LMRMs) in PCa and benign prostate hyperplasia (BPH) specimens were verified using quantitative real-time polymerase chain reaction (qRT‒PCR). Furthermore, a transcription factor (TF)-miRNA‒mRNA network was constructed. Eventually, Kaplan‒Meier (KM) curves were plotted to illustrate the associations between signature miRNA-related mRNAs and TFs and overall survival (OS) along with biochemical recurrence-free survival (BCR).

RESULTS

Forty-seven LMRMs were screened based on the correlation analysis of 29 DE-LMRGs and 56 DEMs, in which 27 LMRMs were stably expressed in the GSE45604 dataset. Subsequently, receiver operating characteristic (ROC) curves and machine learning methods were employed to develop a lipid metabolism-related diagnostic signature, which may be of diagnostic value for PCa patients. qRT‒PCR results showed that all seven key DE-LMRMs were differentially expressed between PCa and BPH tissues. Eventually, a TF-miRNA‒mRNA network was constructed.

CONCLUSIONS

These results suggested that 7 key diagnostic miRNAs were closely related to PCa pathological processes and provided new targets for the diagnosis and treatment of PCa. Moreover, CLIC6 and SCNN1A linked to miR-200c-3p had good prognostic potential and provided valuable insights into the pathogenesis of PCa.

Construction and validation of a novel algorithm based on oncosis-related lncRNAs comprising the immune landscape and prediction of colorectal cancer prognosis

Colorectal cancer (CRC) has high morbidity and mortality, particularly if diagnosed at an advanced stage. Although there have been several studies on CRC, few have investigated the relationship between oncosis and CRC. Thus, the purpose of the present study was to identify oncosis-related long noncoding RNAs (lncRNAs) and to establish a clinical prognostic model. Original data were acquired from The Cancer Genome Atlas database and PubMed. Differentially expressed oncosis-related lncRNAs (DEorlncRNAs) were identified and were subsequently formed into pairs. Next, a series of tests and analyses, including both univariate and multivariate analyses, as well as Lasso and Cox regression analyses, were performed to establish a receiver operating characteristic curve. A cut-off point was subsequently used to divide the samples into groups labelled as high- or low-risk. Thus, a model was established and evaluated in several dimensions. Six pairs of DEorlncRNAs associated with prognosis according to the algorithm were screened out and the CRC cases were divided into high- and low-risk groups. Significant differences between patients in the different risk groups were observed for several traits, including survival outcomes, clinical pathology characteristics, immune cell infiltration status and drug sensitivity. In addition, PCR and flow cytometry were performed to further verify the model. In summary, a new risk model algorithm based on six pairs of DEorlncRNAs in CRC, which does not require specific data regarding the level of gene expression, was established and validated. This algorithm may be used to predict patient prognosis, immune cell infiltration and drug sensitivity.

A novel anoikis-related prognostic signature associated with prognosis and immune infiltration landscape in clear cell renal cell carcinoma

Background: Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of renal cell carcinoma (RCC). Anoikis plays an essential function in tumourigenesis, whereas the role of anoikis in ccRCC remains unclear. Methods: Anoikis-related genes (ARGs) were collected from the MSigDB database. According to univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) algorithm was utilized to select the ARGs associated with the overall rate (OS). Multivariate Cox regression analysis was conducted to identify 5 prognostic ARGs, and a risk model was established. The Kaplan-Meier survival analysis was used to evaluate the OS rate of ccRCC patients. Gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and Gene set enrichment analysis (GSVA) were utilized to investigate the molecular mechanism of patients in the low- and high-risk group. ESTIMATE, CIBERSOT, and single sample gene set enrichment analysis (ssGSEA) algorithms were conducted to estimate the immune infiltration landscape. Consensus clustering analysis was performed to divide the patients into different subgroups. Results: A fresh risk model was constructed based on the 5 prognostic ARGs (CHEK2, PDK4, ZNF304, SNAI2, SRC). The Kaplan-Meier survival analysis indicated that the OS rate of patients with a low-risk score was significantly higher than those with a high-risk score. Consensus clustering analysis successfully clustered the patients into two subgroups, with a remarkable difference in immune infiltration landscape and prognosis. The ESTIMATE, CIBERSORT, and ssGSEA results illustrated a significant gap in immune infiltration landscape of patients in the low- and high-risk group. Enrichment analysis and GSVA revealed that immune-related signaling pathways might mediate the role of ARGs in ccRCC. The nomogram results illustrated that the ARGs prognostic signature was an independent prognostic predictor that distinguished it from other clinical characteristics. TIDE score showed a promising immunotherapy response of ccRCC patients in different risk subgroups and cluster subgroups. Conclusion: Our study revealed that ARGs play a carcinogenic role in ccRCC. Additionally, we firstly integrated multiple ARGs to establish a risk-predictive model. This study highlights that ARGs could be implemented as a stratification factor for individualized and precise treatment in ccRCC patients.

Identification of candidate hub genes correlated with the pathogenesis, diagnosis, and prognosis of prostate cancer by integrated bioinformatics analysis

Background

Prostate cancer (PCa) has the second highest morbidity and mortality rates in men. Concurrently, novel diagnostic and prognostic biomarkers of PCa remain crucial.

Methods

This study utilized integrated bioinformatics method to identify and validate the potential hub genes with high diagnostic and prognostic value for PCa.

Results

Four Gene Expression Omnibus (GEO) datasets including 123 PCa samples and 76 normal samples were screened and a total of 368 differentially expressed genes (DEGs), including 120 up-regulated DEGs and 248 down-regulated DEGs, were identified. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs were majorly enriched in focal adhesion, chemical carcinogenesis, drug metabolism, and cytochrome P450 pathways. Then, 11 hub genes were identified from the protein-protein interaction (PPI) network of the DEGs; 7 of the 11 genes showed the ability of distinguishing PCa from normal prostate based on receiver operating characteristic (ROC) curve analysis. And 5 of the 11 genes were correlated with clinical attributes. Lower CAV1, KRT5, SNAI2 and MYLK expression level were associated with higer Gleason score, advanced pathological T stage and N stage. Lower KRT5 and MYLK expression level were significantly correlated with poor disease-free survival, and lower KRT5 and PTGS2 expression level were significantly related to biochemical recurrence (BCR) status of PCa patients.

Conclusions

In conclusion, CAV1, KRT5, SNAI2, and MYLK show potential clinical diagnostic and prognostic value and could be used as novel candidate biomarkers and therapeutic targets for PCa.