Abstract:Pancreatic cancer (PC) is a malignant tumor disease, whose molecular mechanism is not fully understood. Sodium channel epithelial 1α subunit (SCNN1A) serves an important role in tumor progression. The current study explored the role of homeobox D9 (HOXD9) and SCNN1A in the progression of PC. The expression of SCNN1A and HOXD9 in PC samples was predicted on online databases and detected in PC cell lines. The association between SCNN1A expression and PC prognosis was examined by the Gene Expression Profiling Int… Show more
“…However, CLIC6 has no reported significance in tumorigenesis and tumor progression to date. SCNN1A promotes ovarian and pancreatic cancer cell proliferation and migration and inhibits osteosarcoma growth [48,49]. Nevertheless, the specific biological functions of CLIC6 and SCNN1A in PCa warrant further investigation.…”
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.
“…However, CLIC6 has no reported significance in tumorigenesis and tumor progression to date. SCNN1A promotes ovarian and pancreatic cancer cell proliferation and migration and inhibits osteosarcoma growth [48,49]. Nevertheless, the specific biological functions of CLIC6 and SCNN1A in PCa warrant further investigation.…”
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.
“…SCNN1A downregulation inhibited cell proliferation, migration and invasion, and induced cell apoptosis. Another study showed that SCNN1A upregulation by homeobox D9 (HOXD9) induced cell proliferation and migration in pancreatic cancer cells [ 52 ]. Cai et al demonstrated that inhibition of Serine Protease 8 (PRSS8)/SCNN1A by sterol regulatory element binding protein 2 (SREBF2) reduced the cell proliferation, migration, and epithelial–mesenchymal transformation of ovarian cancer [ 53 ].…”
Glioblastoma multiforme (GBM) is a grade IV, highly malignant brain tumor. Because of the heterogeneity of GBM, a multitarget drug is a rational strategy for GBM treatment. Histone deacetylase inhibitors (HDACis) regulate the expression of numerous genes involved in cell death, apoptosis, and tumorigenesis. We found that the HDAC4/HDAC5 inhibitor LMK235 at 0.5 µM significantly reduced the cell viability and colony formation of patient-derived, temozolomide-resistant GBM P#5 TMZ-R, U-87 MG, and T98G cells. Moreover, LMK235 also significantly increased TUBA acetylation, which is an indicator of HDAC inhibition. Interestingly, LMK235 induced MAP1LC3 robust readout and puncta accumulation but did not enhance PARP1 cleavage or the proportion of annexin V-positive cells, suggesting that LMK235-induced cell death occurred via autophagy activation. Further RNA-seq analysis after LMK235 treatment showed that 597 different expression genes compared to control. After bioinformatic analysis by KEGG and STRING, we focused on 34 genes and validated their mRNA expression by qPCR. Further validation showed that 2 µM LMK235 significantly reduced the mRNA and protein expression of SCNN1A. Cell viability of SCNN1A-silenced cells were reduced, but cells were rescued while treated with an autophagy inhibitor bafilomycin A1. Conclusively, SCNN1A plays a role in LMK235-induced autophagy and cell death in GBM cells.
“…Bioinformatic analysis of the gene expression in the tissues from the patients with lung adenocarcinoma revealed possible implications of ASIC1, ASIC2, ASIC3, ASIC4, α-ENaC, and γ-ENaC in the disease progression ( Figure 9A ). Indeed, all these channels are involved in acidosis-induced tumor progression: ASIC1 and α-ENaC mediate the epithelial–mesenchymal transition of pancreatic cancer ( 19 , 40 ), while ASIC2 promotes the invasion and metastasis of colorectal cancer ( 41 ). Interestingly, ASIC3, which is down-regulated in lung adenocarcinoma samples, may also drive the migration of pancreatic cancer cells ( 19 ), so its role in cancer progression may depend on the carcinoma type.…”
Lung cancer is one of the most common cancer types in the world. Despite existing treatment strategies, overall patient survival remains low and new targeted therapies are required. Acidification of the tumor microenvironment drives the growth and metastasis of many cancers. Acid sensors such as acid-sensing ion channels (ASICs) may become promising targets for lung cancer therapy. Previously, we showed that inhibition of the ASIC1 channels by a recombinant analogue of mambalgin-2 from Dendroaspis polylepis controls oncogenic processes in leukemia, glioma, and melanoma cells. Here, we studied the effects and molecular targets of mambalgin-2 in lung adenocarcinoma A549 and Lewis cells, lung transformed WI-38 fibroblasts, and lung normal HLF fibroblasts. We found that mambalgin-2 inhibits the growth and migration of A549, metastatic Lewis P29 cells, and WI-38 cells, but not of normal fibroblasts. A549, Lewis, and WI-38 cells expressed different ASIC and ENaC subunits, while normal fibroblasts did not at all. Mambalgin-2 induced G2/M cell cycle arrest and apoptosis in lung adenocarcinoma cells. In line, acidification-evoked inward currents were observed only in A549 and WI-38 cells. Gene knockdown showed that the anti-proliferative and anti-migratory activity of mambalgin-2 is dependent on the expression of ASIC1a, α-ENaC, and γ-ENaC. Using affinity extraction and immunoprecipitation, mambalgin-2 targeting of ASIC1a/α-ENaC/γ-ENaC heteromeric channels in A549 cells was shown. Electrophysiology studies in Xenopus oocytes revealed that mambalgin-2 inhibits the ASIC1a/α-ENaC/γ-ENaC channels with higher efficacy than the ASIC1a channels, pointing on the heteromeric channels as a primary target of the toxin in cancer cells. Finally, bioinformatics analysis showed that the increased expression of ASIC1 and γ-ENaC correlates with a worse survival prognosis for patients with lung adenocarcinoma. Thus, the ASIC1a/α-ENaC/γ-ENaC heterotrimer can be considered a marker of cell oncogenicity and its targeting is promising for the design of new selective cancer therapeutics.
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