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BACKGROUND: Bone morphogenetic protein 1 (BMP1) is a metalloprotease that plays a role in activating both transforming growth factor-ß (TGF-ß) and BMP signaling pathways. TGF-ß has been identified as a factor initiating and facilitating cancer development. Consequently, we propose the hypothesis that dysregulation of BMP1 could potentially contribute to the onset and advancement of human cancers. METHODS: In this research, we aimed to analyze BMP1 expression level and the associated clinical outcomes across various cancers using online cancer OMICS databases, advanced Bioinformatics tools, and molecular analyses. RESULTS: The outcomes of our web server-based expression analysis indicated an up-regulation of BMP1 in a majority of the human cancers examined. External validation using clinical samples also showed higher expression of BMP1. Moreover, heightened BMP1 expression exhibited a noteworthy correlation with reduced overall survival (OS) duration in Bladder Cancer (BLCA), Kidney Renal Clear Cell Carcinoma (KIRC), and Lung Adenocarcinoma (LUAD) patients. This suggests a substantial involvement of the BMP1 gene in the development and progression of these three types of cancers. The major signaling pathways related with BMP1 enriched genes were "ECM-receptor interaction, Amoebiasis, Focal adhesion, Protein digestion and absorption, progesterone-mediated, PI3K-Akt signaling pathway, and platelet activation". Moreover, we also explored some interesting correlations among BMP1 expression and its DNA promoter methylation level, CD8+ T immune cells level, and genetic variations. CONCLUSION: In conclusion, our study has provided some solid basis for BMP1 to be used as a reliable common biomarker for BLCA, KIRC, and LUAD patients.
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BACKGROUND: Head and neck squamous cell carcinoma (HNSC), a prevalent malignant tumor with a low survival rate, is often accompanied by ferroptosis, which is a recently-described type ofprogrammed cell death. Investigating the significance of ferroptosis driver genes in HNSC, this study aimed to assess their diagnostic and prognostic values, as well as their impact on treatment and tumor immune function. The results of this investigation provide novel insight into using ferroptosis-related genes as molecular biomarkers as well as precise chemotherapeutic targets for the therapy of HNSC. METHODOLOGY: A detailed in silico and in vitro experiment-based methodology was adopted to achieve the goals. RESULTS: A total of 233 ferroptosis driver genes were downloaded from the FerrDB database. After comprehensively analyzing these 233 ferroptosis driver genes by various TCGA databases, RNA-sequencing (RNA-seq), and Reverse Transcription Quantitative Real-Time Polymerase Chain Reaction (RT-qPCR) techniques, TP53 (tumor protein 53), PTEN (Phosphatase and TENsin homolog deleted on chromosome 10), KRAS (Ki-ras2 Kirsten rat sarcoma viral oncogene homolog), and HRAS (Harvey Rat sarcoma virus) were identified as differentially expressed hub genes. Interestingly, these hub genes were found to have significant (P < 0.05) variations in their mRNA and protein expressions and effects on overall survival of the HNSC patients. Moreover, targeted bisulfite-sequencing (bisulfite-seq) analysis revealed that promoter hypomethylation pattern was associated with up-regulation of hub genes (TP53, PTEN, KRAS, and HRAS). In addition to this, hub genes were involved in diverse oncogenic pathways. CONCLUSION: Since HNSC pathogenesis is a complex process, using ferroptosis driver hub genes (TP53, PTEN, KRAS, and HRAS) as a diagnostic and prognostic tool, and therapeutically targeting those genes through appropriate drugs could bring a milestone change in the drug discovery and management and survival in HNSC.
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OBJECTIVES: The regulation of various cellular functions such as growth, proliferation, metabolism, and angiogenesis, is dependent on the PI3K pathway. Recent evidence has indicated that kidney renal clear cell carcinoma (KIRC) can be triggered by the deregulation of this pathway. The objective of this research was to investigate 25 genes associated with activation of the PI3K pathway in KIRC and control samples to identify four hub genes that might serve as novel molecular biomarkers and therapeutic targets for treating KIRC. METHODS: Multi-omics in silico and in vitro analysis was employed to find hub genes related to the PI3K pathway that may be biomarkers and therapeutic targets for KIRC. RESULTS: Using STRING software, a protein-protein interaction (PPI) network of 25 PI3K pathway-related genes was developed. Based on the degree scoring method, the top four hub genes were identified using Cytoscape's Cytohubba plug-in. TCGA datasets, KIRC (786-O and A-498), and normal (HK2) cells were used to validate the expression of hub genes. Additionally, further bioinformatic analyses were performed to investigate the mechanisms by which hub genes are involved in the development of KIRC. Out of a total of 25 PI3K pathway-related genes, we developed and validated a diagnostic and prognostic model based on the up-regulation of TP53 (tumor protein 53) and CCND1 (Cyclin D1) and the down-regulation of PTEN (Phosphatase and TENsin homolog deleted on chromosome 10), and GSK3B (Glycogen synthase kinase-3 beta) hub genes. The hub genes included in our model may be a novel therapeutic target for KIRC treatment. Additionally, associations between hub genes and infiltration of immune cells can enhance comprehension of immunotherapy for KIRC. CONCLUSION: We have created a new diagnostic and prognostic model for KIRC patients that uses PI3K pathway-related hub genes (TP53, PTEN, CCND1, and GSK3B). Nevertheless, further experimental studies are required to ascertain the efficacy of our model.
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OBJECTIVES: In this comprehensive breast cancer (BC) study, we aimed to identify, validate, and characterize key biomarkers with significant implications in BC diagnosis, prognosis, and as therapeutic targets. METHODS: Our research strategy involved a multi-level methodology, combining bioinformatic analysis with experimental validation. RESULTS: Initially, we conducted an extensive literature search to identify BC biomarkers, selecting those with reported accuracies exceeding 20% in specificity and sensitivity. This yielded nine candidate biomarkers, which we subsequently analyzed using Cytoscape to identify a few key biomarkers. Based on the degree method, we denoted four key biomarkers, including progesterone receptor (PGR), epidermal growth factor receptor (EGFR), estrogen receptor 1 (ESR1), and Erb-B2 Receptor Tyrosine Kinase 2 (ERBB2). Expression analysis using The Cancer Genome Atlas (TCGA) dataset revealed that PGR and EGFR exhibited significant (p-value < 0.05) down-regulation in BC samples when compared to controls, while ESR1 and ERBB2 showed up-regulation. To strengthen our findings, we collected clinical BC tissue samples from Pakistani patients and performed expression verification using real-time quantitative polymerase chain reaction (RT-qPCR). The results aligned with our initial TCGA dataset analysis, further validating the differential expression of these key biomarkers in BC. Furthermore, we utilized receiver operating characteristic (ROC) curves to demonstrate the diagnostic use of these biomarkers. Our analysis underscored their accuracy and sensitivity as diagnostic markers for BC. Survival analysis using the Kaplan-Meier Plotter tool revealed a prognostic significance of PGR, ESR1, EGFR, and ERBB2. Their expression levels were associated with poor overall survival (OS) of BC patients, shedding light on their roles as prognostic indicators in BC. Lastly, we explored DrugBank to identify drugs that may reverse the expression patterns , and estradiol, decitabine, and carbamazepine were singled out. CONCLUSION: Our study gives valuable insight into BC biomarkers, for diagnosis and prognosis. These findings have implications for BC management using personalized and targeted therapeutic approaches for BC patients.