Search Results (20)

Background: Gleason score (GS) 10 prostate cancer (PC) is a highly aggressive localized disease. Despite advances in treating high-risk PC, the clinical outcomes and molecular underpinnings of GS 10 remain unclear. This study aimed to determine whether GS 10 PC has distinct clinical outcomes from other “high-risk” cancers (i.e., Gleason 8–9) and identify genomic alterations driving its aggressive phenotype. Methods: A retrospective review of The Cancer Genome Atlas database identified patients with GS 8–10 PC who underwent radical prostatectomy. Clinical factors were compared between GS 10 and GS 8–9 cohorts. Time to biochemical recurrence (BCR) was analyzed using Kaplan–Meier and Cox regression. RNA sequencing identified differentially expressed genes, and protein–protein interaction networks identified hub genes. Results: Of 192 patients, 13 (6.8%) had GS 10 PC. After median follow-up of 37.87 months, GS 10 status was associated with significantly lower time to BCR (AHR, 2.67; 95% CI, 1.18–6.02; p = 0.018) compared to GS 8–9. Multiple genes (e.g., RAD54L, FAAH, AATK, MAST2) showed higher alteration frequencies, and high expression of RAD54L, MAST2, and CCHCR1 correlated with shorter disease-free survival. Six overlapping hub genes (CD8A, CDC20, E2F1, IL10, TNF, VCAM1) were overexpressed in GS 10 tumors, reflecting key pathways in tumor progression. Conclusions: GS 10 PC confers inferior time to BCR and displays a distinct genomic landscape compared to GS 8–9 disease, highlighting the need for biomarker-driven therapeutic strategies. Further studies are needed to validate these genomic targets and improve management for this very high-risk population. Full article

Background: CD59, a GPI-anchored membrane protein, protects cancer cells from complement-dependent cytotoxicity (CDC) by inhibiting the formation of the membrane attack complex (MAC). It has been demonstrated to be overexpressed in most solid tumors, where it facilitates tumor cell escape from complement surveillance. The role of CD59 in cancer growth and interactions between CD59 and immune cells that modulate immune evasion has not been well explored. Methods: Using cancer patient database from The Cancer Genome Atlas (TCGA) and other public databases, we analyzed CD59 expression, its prognostic significance, and its association with immune cell infiltration in the tumor microenvironment, identifying associated genomic and functional networks and validating findings with invitro cell-line experimental data. Results: This article describes the abundant expression of CD59 in multiple tumors such as cervical squamous cell carcinoma (CESC), kidney renal cell carcinoma (KIRC), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), and stomach adenocarcinoma (STAD), as well as in pan-cancer, using The Cancer Genome Atlas (TCGA) database and confirmed using multiple cancer cell lines. The expression of CD59 significantly alters the overall survival (OS) of patients with multiple malignancies such as CESC, GBM, HNSC, and STAD. Further, the correlation between CD59 and Treg and/or MDSC in the tumor microenvironment (TME) has shown to be strongly associated with poor outcomes in CESC, GBM, HNSC, and STAD as these tumors express high FOXP3 compared to KIRC. Moreover, unfavorable outcomes were strongly associated with the expression of CD59 and M2 tumor-associated macrophage infiltration in the TME via the IL10/pSTAT3 pathway in CESC and GBM but not in KIRC. In addition, TGFβ1-dominant cancers such as CESC, GBM, and HNSC showed a high correlation between CD59 and TGFβ1, leading to suppression of cytotoxic T cell activity. Conclusion: Overall, the correlation between CD59 and immune cells predicts its prognosis as unfavorable in CESC, GBM, HNSC, and STAD while being favorable in KIRC. Full article

Prostate cancer has substantial heterogeneity in clinical outcomes and therapeutic responses, posing challenges in predicting disease progression and tailoring treatment strategies. Recent studies have highlighted the potential prognostic value of evaluating the tumor microenvironment, including the presence of a histologically overt stromal response (HOST-response) characterized by peri-glandular stromal changes and architectural distortions. This retrospective study examined patient records from The Cancer Genome Atlas database to identify genomic alterations associated with the HOST-response in prostate cancer. Among 348 patients who underwent radical prostatectomy, 160 (45.98%) were identified as having a HOST-response. A gene expression analysis revealed 1263 genes with significantly higher expression in patients with a HOST-response. A protein–protein interaction network analysis identified seven hub genes (KIF2C, CENPA, CDC20, UBE2C, ESPL1, KIF23, and PLK1) highly interconnected in the network. A functional enrichment analysis revealed alterations in the cell division, cytoskeletal organization, cytokinesis, and interleukin-16 signaling pathways in patients with a HOST-response, suggesting dysregulated proliferation and inflammation. The distinct molecular signature associated with the HOST-response provides insights into the tumor–stroma interactions driving adverse outcomes and potential targets for tailored therapeutic interventions in this subset of patients with prostate cancer. Full article

AML ranks second in the most common types of leukemia diagnosed in both adults and children. Necroptosis is a programmed inflammatory cell death form reported to be an innate immune effector against microbial and viral pathogens and recently has been found to play an eventful role in the oncogenesis, progression, and metastasis of cancer. This study is designed to explore the potential value of necroptosis in predicting prognostic and optimizing the current therapeutic strategies for AML patients. We collected transcriptome and clinical data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases and selected necroptosis-related genes with both differential significance and prognostic value. Six genes (YBX3, ZBP1, CDC37, ALK, BRAF, and BNIP3) were incorporated to generate a risk model with the implementation of multivariate Cox regression. The signature was proven to be an independent prognostic predictor in both training and validation cohorts with hazard ratios (HRs) of 1.51 (95% CI: 1.33–1.72) and 1.57 (95% CI: 1.16–2.12), respectively. Moreover, receiver operating characteristic (ROC) curve was utilized to quantify the predictive performance of the signature and satisfying results were shown with the area under the curve (AUC) up to 0.801 (3-year) and 0.619 (3-year), respectively. In addition, the subtyping of AML patients based on the risk signature demonstrated a significant correlation with the immune cell infiltration and response to immunotherapy. Finally, we incorporated risk signature with the classical clinical features to establish a nomogram which may contribute to the improvement of clinical management. To conclude, this study identified a necroptosis-related signature as a novel biomarker to improve the risk stratification, to inform the immunotherapy efficacy, and to indicate the therapeutic option of targeted therapy. Full article

In Taiwan, a combination of hepatitis B and C infection, economic boom-related food and alcohol overconsumption, and Chinese medicine prescriptions has led to a high rate of hepatocellular carcinoma (HCC). However, the causative factors and underlying tumor biology for this unique HCC environment have not been identified. Wnt and Hippo signaling pathways play an important regulatory role in HCC development, and their functions are generally considered as positive and negative regulators of cell proliferation, respectively. In this study, we characterized the molecular features of HCC using a newly developed classification system based on the expression of the Wnt–Hippo signaling pathway-related genes. RNA sequencing (RNA-Seq) was performed on liver tumor tissues from 100 patients with liver cancer. RNA-Seq data for 272 previously characterized Wnt–Hippo signaling pathway-related genes were used for hierarchical clustering. We analyzed the data in terms of prognostic value, transcriptome features, immune infiltration, and clinical characteristics, and compared the resulting subclasses with previously published classifications. Four subclasses of HCC (HCCW1–4) were identified. Subclass HCCW1 displayed the highest PCDHB4 expression. Subclass HCCW2 displayed lower Edmondson–Steiner grades (I and II) and CTNNB1 mutation frequencies. Subclass HCCW3 was associated with a good prognosis, the highest PCDHGB7 expression, high CD8+ naïve T cells abundance, and relatively low TP53 mutation rates. Subclass HCCW4 was associated with a poor prognosis, the highest PCDHB2 and PCDHB6 expression, a relatively high abundance of Th1 cells, NKT and class-switched memory B cells, relatively low enrichment of cDC, iDC, and CD4+ memory T cells, and high Edmondson–Steiner grades (III and IV). We also identified Wnt–Hippo signaling pathway-related genes that may influence immune cell infiltration. We developed a panel of 272 Wnt–Hippo signaling pathway-related genes to classify HCC into four groups based on Taiwanese HCC and The Cancer Genome Atlas Liver Hepatocellular Carcinoma datasets. This novel molecular classification system may aid the treatment of HCC. Full article

Estrogen and progesterone are the major determinants of the occurrence and development of endometrial cancer (EC), which is one of the most common gynecological cancers worldwide. Our purpose was to develop a novel estrogen/progesterone-related gene signature to better predict the prognosis of EC and help discover effective therapeutic strategies. We downloaded the clinical and RNA-seq data of 397 EC patients from The Cancer Genome Atlas (TCGA) database. The “limma” R package was used to screen for estrogen/progesterone-related differentially expressed genes (DEGs) between EC and normal tissues. Univariate and multivariate Cox proportional hazards regression analyses were applied to identify these DEGs that were associated with prognosis; then, a novel estrogen/progesterone-related prognostic signature comprising CDC25B, GNG3, ITIH3, PRXL2A and SDHB was established. The Kaplan–Meier (KM) survival analysis showed that the low-risk group identified by this signature had significantly longer overall survival (OS) than the high-risk group; the receiver operating characteristic (ROC) and risk distribution curves suggested this signature was an accurate predictor independent of risk factors. A nomogram incorporating the signature risk score and stage was constructed, and the calibration plot suggested it could accurately predict the survival rate. Compared with normal tissues, tumor tissues had increased mRNA levels of GNG3 and PRXL2A and a reduced mRNA level of ITIH3. The knockdown of PRXL2A and GNG3 significantly inhibited the proliferation and colony formation of Ishikawa and AN3CA cells, while the inhibition of PRXL2A expression suppressed xenograft growth. In this study, five estrogen/progesterone-related genes were identified and incorporated into a novel signature, which provided a new classification tool for improved risk assessment and potential molecular targets for EC therapies. Full article

Background: Prior research has identified disparities in anti-hypertensive medication (AHM) non-adherence between Black/African Americans (BAAs) and non-Hispanic Whites (nHWs) but the role of determinants of health in these gaps is unclear. Non-adherence to AHM may be associated with increased mortality (due to heart disease and stroke) and the extent to which such associations are modified by contextual determinants of health may inform future interventions. Methods: We linked the Centers for Disease Control and Prevention (CDC) Atlas of Heart Disease and Stroke (2014–2016) and the 2016 County Health Ranking (CHR) dataset to investigate the associations between AHM non-adherence, mortality, and determinants of health. A proportion of days covered (PDC) with AHM < 80%, was considered as non-adherence. We computed the prevalence rate ratio (PRR)—the ratio of the prevalence among BAAs to that among nHWs—as an index of BAA–nHW disparity. Hierarchical linear models (HLM) were used to assess the role of four pre-defined determinants of health domains—health behaviors, clinical care, social and economic and physical environment—as contributors to BAA–nHW disparities in AHM non-adherence. A Bayesian paradigm framework was used to quantify the associations between AHM non-adherence and mortality (heart disease and stroke) and to assess whether the determinants of health factors moderated these associations. Results: Overall, BAAs were significantly more likely to be non-adherent: PRR = 1.37, 95% Confidence Interval (CI):1.36, 1.37. The four county-level constructs of determinants of health accounted for 24% of the BAA-nHW variation in AHM non-adherence. The clinical care (β = −0.21, p < 0.001) and social and economic (β = −0.11, p < 0.01) domains were significantly inversely associated with the observed BAA–nHW disparity. AHM non-adherence was associated with both heart disease and stroke mortality among both BAAs and nHWs. We observed that the determinants of health, specifically clinical care and physical environment domains, moderated the effects of AHM non-adherence on heart disease mortality among BAAs but not among nHWs. For the AHM non-adherence-stroke mortality association, the determinants of health did not moderate this association among BAAs; the social and economic domain did moderate this association among nHWs. Conclusions: The socioeconomic, clinical care and physical environmental attributes of the places that patients live are significant contributors to BAA–nHW disparities in AHM non-adherence and mortality due to heart diseases and stroke. Full article

Bladder cancer prognosis remains dismal due to lack of appropriate biomarkers that can predict its progression. The study aims to identify novel prognostic biomarkers associated with the progression of bladder cancer by utilizing three Gene Expression Omnibus (GEO) datasets to screen differentially expressed genes (DEGs). A total of 1516 DEGs were identified between non-muscle invasive and muscle invasive bladder cancer specimens. To identify genes of prognostic value, we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A total of seven genes, including CDKN2A, CDC20, CTSV, FOXM1, MAGEA6, KRT23, and S100A9 were confirmed with strong prognostic values in bladder cancer and validated by qRT-PCR conducted in various human bladder cancer cells representing stage-specific disease progression. ULCAN, human protein atlas and The Cancer Genome Atlas datasets were used to confirm the predictive value of these genes in bladder cancer progression. Moreover, Kaplan–Meier analysis and Cox hazard ratio analysis were performed to determine the prognostic role of these genes. Univariate analysis performed on a validation set identified a 3-panel gene set viz. CDKN2A, CTSV and FOXM1 with 95.5% sensitivity and 100% specificity in predicting bladder cancer progression. In summary, our study screened and confirmed a 3-panel biomarker that could accurately predict the progression and prognosis of bladder cancer. Full article

The molecular heterogeneity of gene expression profiles of glioblastoma multiforme (GBM) are the most important prognostic factors for tumor recurrence and drug resistance. Thus, the aim of this study was to identify potential target genes related to temozolomide (TMZ) resistance and GBM recurrence. The genomic data of patients with GBM from The Cancer Genome Atlas (TCGA; 154 primary and 13 recurrent tumors) and a local cohort (29 primary and 4 recurrent tumors), samples from different tumor regions from a local cohort (29 tumor and 25 peritumoral regions), and Gene Expression Omnibus data (GSE84465, single-cell RNA sequencing; 3589 cells) were included in this study. Critical gene signatures were identified based an analysis of differentially expressed genes (DEGs). DEGs were further used to evaluate gene enrichment levels among primary and recurrent GBMs and different tumor regions through gene set enrichment analysis. Protein–protein interactions (PPIs) were incorporated into gene regulatory networks to identify the affected metabolic pathways. The enrichment levels of 135 genes were identified in the peritumoral regions as being risk signatures for tumor recurrence. Fourteen genes (DVL1, PRKACB, ARRB1, APC, MAPK9, CAMK2A, PRKCB, CACNA1A, ERBB4, RASGRF1, NF1, RPS6KA2, MAPK8IP2, and PPM1A) derived from the PPI network of 135 genes were upregulated and involved in the regulation of cancer stem cell (CSC) development and relevant signaling pathways (Notch, Hedgehog, Wnt, and MAPK). The single-cell data analysis results indicated that 14 key genes were mainly expressed in oligodendrocyte progenitor cells, which could produce a CSC niche in the peritumoral region. The enrichment levels of 336 genes were identified as biomarkers for evaluating TMZ resistance in the solid tumor region. Eleven genes (ARID5A, CDC42EP3, CDKN1A, FLT3, JUNB, MAP2K3, MYBPC2, RGS14, RNASEK, TBC1D30, and TXNDC11) derived from the PPI network of 336 genes were upregulated and may be associated with a high risk of TMZ resistance; these genes were identified in both the TCGA and local cohorts. Furthermore, the expression patterns of ARID5A, CDKN1A, and MAP2K3 were identical to the gene signatures of TMZ-resistant cell lines. The identified enrichment levels of the two gene sets expressed in tumor and peritumoral regions are potentially helpful for evaluating TMZ resistance in GBM. Moreover, these key genes could be used as biomarkers, potentially providing new molecular strategies for GBM treatment. Full article

Background: Tongue squamous cell carcinoma (TSCC) patients with high-grade tumors usually suffer from high occurrence and poor prognosis. The current study aimed at finding the biomarkers related to tumor grades and proposing potential therapies by these biomarkers. Methods: The mRNA expression matrix of TSCC samples from The Cancer Genome Atlas (TCGA) database was analyzed to identify hub proteins related to tumor grades. The mRNA expression patterns of these hub proteins between TSCC and adjacent control samples were validated in three independent TSCC data sets (i.e., GSE9844, GSE30784, and GSE13601). The correlation between cell cycle index and immunotherapy efficacy was tested on the IMvigor210 data set. Based on the structure of hub proteins, virtual screening was applied to compounds to find the potential inhibitors. Results: A total of six cell cycle biomarkers (i.e., BUB1, CCNB2, CDC6, CDC20, CDK1, and MCM2) were selected as hub proteins by protein–protein interaction (PPI) analysis. In the validation data sets, the mRNA expression levels of these hub proteins were higher in tumor samples versus normal controls. The cell cycle index was constructed by the mRNA expression levels of these hub proteins, and patients with a high cell cycle index demonstrated favorable drug response to the immunotherapy. Three small molecules (i.e., ZINC100052685, ZINC8214703, and ZINC85537014) were found to bind with hub proteins and selected as drug candidates. Conclusion: The cell cycle index might provide a novel reference for selecting appropriate cancer patient candidates for immunotherapy. The current research might contribute to the development of precision medicine and improve the prognosis of TSCC. Full article

(1) Background: The tumor microenvironment is involved in the growth and proliferation of malignant tumors and in the process of resistance towards systemic and targeted therapies. A correlation between the gene expression profile of the tumor microenvironment and the prognosis of ovarian cancer patients is already known. (2) Methods: Based on data from The Cancer Genome Atlas (379 RNA sequencing samples), we constructed a prognostic 11-gene signature (SNRPA1, CCL19, CXCL11, CDC5L, APCDD1, LPAR2, PI3, PLEKHF1, CCDC80, CPXM1 and CTAG2) for Fédération Internationale de Gynécologie et d’Obstétrique stage III and IV serous ovarian cancer through lasso regression. (3) Results: The established risk score was able to predict the 1-, 3- and 5-year prognoses more accurately than previously known models. (4) Conclusions: We were able to confirm the predictive power of this model when we applied it to cervical and urothelial cancer, supporting its pan-cancer usability. We found that immune checkpoint genes correlate negatively with a higher risk score. Based on this information, we used our risk score to predict the biological response of cancer samples to an anti-programmed death ligand 1 immunotherapy, which could be useful for future clinical studies on immunotherapy in ovarian cancer. Full article

Apurinic/apyrimidinic endodeoxyribonuclease 1 (APEX1) is involved in the DNA damage repair pathways and associates with the metastasis of several human cancers. However, the signaling pathway of APEX1 in cholangiocarcinoma (CCA) has never been reported. In this study, to predict the signaling pathways of APEX1 and related proteins and their functions, the effects of APEX1 gene silencing on APEX1 and related protein expression in CCA cell lines were investigated using mass spectrometry and bioinformatics tools. Bioinformatic analyses predicted that APEX1 might interact with cell division cycle 42 (CDC42) and son of sevenless homolog 1 (SOS1), which are involved in tumor metastasis. RNA and protein expression levels of APEX1 and its related proteins, retrieved from the Gene Expression Profiling Interactive Analysis (GEPIA) and the Human Protein Atlas databases, revealed that their expressions were higher in CCA than in the normal group. Moreover, higher levels of APEX1 expression and its related proteins were correlated with shorter survival time. In conclusion, the signaling pathway of APEX1 in metastasis might be mediated via CDC42 and SOS1. Furthermore, expression of APEX1 and related proteins is able to predict poor survival of CCA patients. Full article

Hepatocellular carcinoma is one of the most common cancer types worldwide. In cases of advanced-stage disease, sorafenib is considered the treatment of choice. However, resistance to sorafenib remains a major obstacle for effective clinical application. Based on integrated phosphoproteomic and The Cancer Genome Atlas (TCGA) data, we identified a transcription factor, Y-box binding protein-1 (YB-1), with elevated phosphorylation of Ser102 in sorafenib-resistant HuH-7^R cells. Phosphoinositide-3-kinase (PI3K) and protein kinase B (AKT) were activated by sorafenib, which, in turn, increased the phosphorylation level of YB-1. In functional analyses, knockdown of YB-1 led to decreased cell migration and invasion in vitro. At the molecular level, inhibition of YB-1 induced suppression of zinc-finger protein SNAI1 (Snail), twist-related protein 1 (Twist1), zinc-finger E-box-binding homeobox 1 (Zeb1), matrix metalloproteinase-2 (MMP-2) and vimentin levels, implying a role of YB-1 in the epithelial-mesenchymal transition (EMT) process in HuH-7^R cells. Additionally, YB-1 contributes to morphological alterations resulting from F-actin rearrangement through Cdc42 activation. Mutation analyses revealed that phosphorylation at S102 affects the migratory and invasive potential of HuH-7^R cells. Our collective findings suggest that sorafenib promotes YB-1 phosphorylation through effect from the EGFR/PI3K/AKT pathway, leading to significant enhancement of hepatocellular carcinoma (HCC) cell metastasis. Elucidation of the specific mechanisms of action of YB-1 may aid in the development of effective strategies to suppress metastasis and overcome resistance. Full article

Analysis of The Cancer Genome Atlas data revealed that alternative splicing (AS) events could serve as prognostic biomarkers in various cancer types. This study examined lung adenocarcinoma (LUAD) tissues for AS and assessed AS events as potential indicators of prognosis in our cohort. RNA sequencing and bioinformatics analysis were performed. We used SUPPA2 to analyze the AS profiles. Using univariate Cox regression analysis, overall survival (OS)-related AS events were identified. Genes relating to the OS-related AS events were imported into Cytoscape, and the CytoHubba application was run. OS-related splicing factors (SFs) were explored using the log-rank test. The relationship between the percent spliced-in value of the OS-related AS events and SF expression was identified by Spearman correlation analysis. We found 1957 OS-related AS events in 1151 genes, and most were protective factors. Alternative first exon splicing was the most frequent type of splicing event. The hub genes in the gene network of the OS-related AS events were FBXW11, FBXL5, KCTD7, UBB and CDC27. The area under the curve of the MIX prediction model was 0.847 for 5-year survival based on seven OS-related AS events. Overexpression of SFs CELF2 and SRSF5 was associated with better OS. We constructed a correlation network between SFs and OS-related AS events. In conclusion, we identified prognostic predictors using AS events that stratified LUAD patients into high- and low-risk groups. The discovery of the splicing networks in this study provides an insight into the underlying mechanisms. Full article

Background: Non-adherence to antihypertensive medication treatment (AHM) is a complex health behavior with determinants that extend beyond the individual patient. The structural and social determinants of health (SDH) that predispose populations to ill health and unhealthy behaviors could be potential barriers to long-term adherence to AHM. However, the role of SDH in AHM non-adherence has been understudied. Therefore, we aimed to define and identify the SDH factors associated with non-adherence to AHM and to quantify the variation in county-level non-adherence to AHM explained by these factors. Methods: Two cross-sectional datasets, the Centers for Disease Control and Prevention (CDC) Atlas of Heart Disease and Stroke (2014–2016 cycle) and the 2016 County Health Rankings (CHR), were linked to create an analytic dataset. Contextual SDH variables were extracted from the CDC-CHR linked dataset. County-level prevalence of AHM non-adherence, based on Medicare fee-for-service beneficiaries’ claims data, was extracted from the CDC Atlas dataset. The CDC measured AHM non-adherence as the proportion of days covered (PDC) with AHM during a 365 day period for Medicare Part D beneficiaries and aggregated these measures at the county level. We applied confirmatory factor analysis (CFA) to identify the constructs of social determinants of AHM non-adherence. AHM non-adherence variation and its social determinants were measured with structural equation models. Results: Among 3000 counties in the U.S., the weighted mean prevalence of AHM non-adherence (PDC < 80%) in 2015 was 25.0%, with a standard deviation (SD) of 18.8%. AHM non-adherence was directly associated with poverty/food insecurity (β = 0.31, P-value < 0.001) and weak social supports (β = 0.27, P-value < 0.001), but inversely with healthy built environment (β = −0.10, P-value = 0.02). These three constructs explained one-third (R² = 30.0%) of the variation in county-level AHM non-adherence. Conclusion: AHM non-adherence varies by geographical location, one-third of which is explained by contextual SDH factors including poverty/food insecurity, weak social supports and healthy built environments. Full article