Current Issue : October-December Volume : 2024 Issue Number : 4 Articles : 5 Articles
Among women, ovarian cancer ranks as the fifth most common cause of cancer-related deaths. This study examined the impact of Hippo signaling pathway on ovarian carcinogenesis. Therefore, the signatures related to Hippo signaling pathway were derived from the molecular signatures database (MSigDB) and were used for further analysis. The Z score-based pathway activation scoring method was employed to investigate the expression patterns of these signatures in the mRNA expression profiles of ovarian cancer cohorts. Compared to other subtype tumors, the results of this study show that the Hippo signaling pathway signatures are dysregulated prominently in serous subtype-specific ovarian carcinogenesis. A receiver operating characteristic (ROC) curvebased results of the Hippo gene set, yes-associated protein 1 (YAP1), and mammalian sterile 20-like kinases 1 (MST1) genes can predict the serous subtype tumors by higher specificity and sensitivity with significant areas under the curve values also further reconfirmed these signaling dysregulations. Moreover, these gene sets were studied further for mutation analysis in the profile of high-grade serous ovarian adenocarcinoma in the cBioPortal database. The OncoPrint results reveal that these Hippo signaling pathway genes are amplified highly during the grade three and stage third or fourth of serous type ovarian tumors. In addition, the results of the Dependency Map (DepMap) plot also clearly show that these genes are amplified significantly across the ovarian cancer cell lines. Finally, overall survival (OS) curve plot investigations also revealed that these gene expressions show poor survival patterns linked to highly expressed conditions in serous subtypes of ovarian cancer patients with significant p-values (p < 0.05). Thus, the current finding would help to develop the targeted therapies treatment for serous subtype ovarian carcinogenesis....
Non-small-cell lung cancer (NSCLC) is the most prevalent type of lung cancer, with extensively characterized mutational spectra. Several biomarkers (such as EGFR, BRAF, KRAS gene mutations, etc.) have emerged as predictive and prognostic markers for NSCLC. Unfortunately, the quality of the available tumor biopsy and/or cytology material is not always adequate to perform the necessary molecular testing, prompting the search for alternatives. Cell-free DNA (cfDNA) found in plasma is emerging as a highly promising avenue or a supplementary method for assessing the efficacy of cancer treatments. This is especially valuable in instances where conventional biopsy specimens, like formalin-fixed, paraffin-embedded (FFPE), or freshly frozen tumor tissues prove inadequate for conducting molecular pathology analyses subsequent to the initial diagnostic procedures. By leveraging cfDNA from plasma, clinicians gain an additional tool to gauge the effectiveness of cancer therapies, thereby enhancing their ability to optimize tailored treatment strategies. In this study, 51 Lithuanian females with NSCLC were analyzed, with adenocarcinoma being the predominant pathology diagnosis in 40 cases (78%). Target mutations were identified in 38 out of 51 patients (74.5%) in tumor tissue samples, while in plasma samples, they were identified in only 10 patients’ samples (19.6%). Even though we did not have enough voluminous plasma samples in our study, gene mutations were detected in plasma from ten women, three of whom were diagnosed with early stages of lung cancer (stages I and II). For these patients, the following mutations were detected: deletion in exon 19 of the EGFR gene and single nucleotide polymorphisms in the TP53 and MET genes. All other women were diagnosed with stages III or IV of lung cancer. This indicates that the later stages of cancer contribute more cfDNA in plasma, making extraction less complicated....
Mutations in the CRB1 gene are associated with a diverse spectrum of retinopathies with phenotypic variability causing severe visual impairment. The CRB1 gene has a role in retinal development and is expressed in the cerebral cortex and hippocampus, but its role in cognition has not been described before. This study compares cognitive function in CRB1 retinopathy individuals with subjects with other retinopathies and the normal population. Methods: Neuropsychological tests of cognitive function were used to test individuals with CRB1 and non-CRB1 retinopathies and compare results with a standardised normative dataset. Results: CRB1 retinopathy subjects significantly outperformed those with non-CRB1 retinopathy in list learning tasks of immediate (p = 0.001) and delayed memory (p = 0.007), tests of semantic verbal fluency (p = 0.017), verbal IQ digit span subtest (p = 0.037), and estimation test of higher execution function (p = 0.020) but not in the remaining tests of cognitive function (p > 0.05). CRB1 retinopathy subjects scored significantly higher than the normal population in all areas of memory testing (p < 0.05) and overall verbal IQ tests (p = 0.0012). Non-CRB1 retinopathy subjects scored significantly higher than the normal population in story recall, verbal fluency, and overall verbal IQ tests (p = 0.0016). Conclusions: Subjects with CRB1 retinopathy may have enhanced cognitive function in areas of memory and learning. Further work is required to understand the role of CRB1 in cognition....
Background Glycometabolism and lipid metabolism are critical in cancer metabolic reprogramming. The primary aim of this study was to develop a prognostic model incorporating glycometabolism and lipid metabolism-related genes (GLRGs) for accurate prognosis assessment in patients with endometrial carcinoma (EC). Methods Data on gene expression and clinical details were obtained from publicly accessible databases. GLRGs were obtained from the Genecards database. Through nonnegative matrix factorization (NMF) clustering, molecular groupings with various GLRG expression patterns were identified. LASSO Cox regression analysis was employed to create a prognostic model. Use rich algorithms such as GSEA, GSVA, xCELL ssGSEA, EPIC,CIBERSORT, MCPcounter, ESTIMATE, TIMER, TIDE, and Oncoppredict to analyze functional pathway characteristics of the forecast signal, immune status, anti-tumor therapy, etc. The expression was assessed using Western blot and quantitative real-time PCR techniques. A total of 113 algorithm combinations were combined to screen out the most significant GLRGs in the signature for in vitro experimental verification, such as colony formation, EdU cell proliferation, wound healing, apoptosis, and Transwell assays. Results A total of 714 GLRGs were found, and 227 of them were identified as prognostic-related genes. And ten GLRGs (AUP1, ESR1, ERLIN2, ASS1, OGDH, BCKDHB, SLC16A1, HK2, LPCAT1 and PGR-AS1) were identified to construct the prognostic model of patients with EC. Based on GLRGs, the risk model’s prognosis and independent prognostic value were established. The signature of GLRGs exhibited a robust correlation with the infiltration of immune cells and the sensitivity to drugs. In cytological experiments, we selected HK2 as candidate gene to verify its value in the occurrence and development of EC. Western blot and qRT-PCR revealed that HK2 was substantially expressed in EC cells. According to in vitro experiments, HK2 knockdown can increase EC cell apoptosis while suppressing EC cell migration, invasion, and proliferation. Conclusion The GLRGs signature constructed in this study demonstrated significant prognostic value for patients with endometrial carcinoma, thereby providing valuable guidance for treatment decisions....
Background Results regarding whether it is essential to incorporate genetic variants into risk prediction models for esophageal cancer (EC) are inconsistent due to the different genetic backgrounds of the populations studied. We aimed to identify single-nucleotide polymorphisms (SNPs) associated with EC among the Chinese population and to evaluate the performance of genetic and non-genetic factors in a risk model for developing EC. Methods A meta-analysis was performed to systematically identify potential SNPs, which were further verified by a case-control study. Three risk models were developed: a genetic model with weighted genetic risk score (wGRS) based on promising SNPs, a non-genetic model with environmental risk factors, and a combined model including both genetic and non-genetic factors. The discrimination ability of the models was compared using the area under the receiver operating characteristic curve (AUC) and the net reclassification index (NRI). The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to assess the goodness-of-fit of the models. Results Five promising SNPs were ultimately utilized to calculate the wGRS. Individuals in the highest quartile of the wGRS had a 4.93-fold (95% confidence interval [CI]: 2.59 to 9.38) increased risk of EC compared with those in the lowest quartile. The genetic or non-genetic model identified EC patients with AUCs ranging from 0.618 to 0.650. The combined model had an AUC of 0.707 (95% CI: 0.669 to 0.743) and was the best-fitting model (AIC = 750.55, BIC = 759.34). The NRI improved when the wGRS was added to the risk model with non-genetic factors only (NRI = 0.082, P = 0.037). Conclusions Among the three risk models for EC, the combined model showed optimal predictive performance and can help to identify individuals at risk of EC for tailored preventive measures....
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