Cancer Biomarkers最新文献

BackgroundAcute myeloid leukemia (AML) prognosis varies greatly, underscoring the need for novel biomarkers to improve patient stratification. T-cell leukemia translocation-associated gene (TCTA) has emerged as a potential player in hematological malignancies, yet its role in AML remains unexplored.ObjectiveTo investigate the prognostic significance of TCTA in AML and elucidate its functional mechanisms.MethodsRNA sequencing data from 173 AML patients (TCGA) and 70 normal controls (GTEx) were analyzed. Patients were categorized into high and low TCTA expression groups. Bioinformatics tools assessed Gene Ontology, KEGG pathways, and immune infiltration and constructed a nomogram predicting 1-5-year overall survival (OS).ResultsHigh TCTA expression correlated with significantly reduced OS (P < 0.001), with multivariate analysis identifying TCTA expression alongside age and cytogenetic risk as independent OS predictors. Receiver operating characteristic analysis validated TCTA's diagnostic potential. Enrichment analyses implicated TCTA in pathways critical to AML, such as hematopoiesis, p53 signaling, and DNA methylation, with a notable association with natural killer (NK) cell activity.ConclusionsElevated TCTA expression signifies poor prognosis in AML, positioning it as a promising prognostic biomarker. Its involvement in key AML-related pathways highlights TCTA's functional relevance and potential as a therapeutic target in AML management.

Background15-Hydroxyprostaglandin dehydrogenase (15-PGDH, gene symbol HPGD) is considered a tumor suppressor, and its expression is often proportional to the anticancer response. However, the clinical significance of HPGD/15-PGDH in predicting immune response and its diagnosis and prognosis value in cervical cancer remains unclear.ObjectiveThis study aims to explore the clinical significance of HPGD/15-PGDH in predicting carcinogenesis, prognosis, and sensitivity to immuno- and chemotherapy in cervical cancer.MethodsA comprehensive evaluation of the diagnostic, treatment-sensitive, and prognostic value of HPGD/15-PGDH in cervical cancer was conducted by bioinformatics analysis of public databases and validation of real cohort data.ResultsBioinformatics analysis showed that HPGD expression was decreased in cervical cancer and did not independently predict patient prognosis. Low HPGD expression was linked to resistance to certain chemotherapies, potentially due to immunosuppression triggered by low HPGD levels. Validation in clinical samples from the local hospital confirmed the decreased 15-PGDH expression and increased COX-2 expression in HPV16-positive cervical cancer patients and increased immune suppression during cancer progression.ConclusionsHPGD/15-PGDH is a potential biomarker for predicting the progression, immune response, and chemotherapy sensitivity of cervical cancer, with implications that it is of great value for the diagnosis and individual-based treatment of cervical cancer.

ObjectiveLung adenocarcinoma (LUAD) is a common and highly heterogeneous malignancy cancer with increasing morbidity and mortality. Dysregulation of fatty acid metabolism (FAM) has been identified as a key regulator of LUAD progression. Our purpose was to establish a risk model of FAM-related genes to provide a reference for the prognosis prediction of LUAD.MethodsFirstly, we screened FAM-related differentially expressed genes (DEGs) based on the Cancer Genome Atlas (TCGA) database, and identified the prognostic signatures by Cox-regression analysis. The least absolute shrinkage and selection operator algorithm (LASSO) was used to obtain the formula for risk model. And the analysis of Gene Expression Omnibus (GEO) dataset used to verify. Nomogram was produced for individualized prediction in clinical treatment. Immune cell function and drug sensitivity analysis used to screen potential therapeutic drugs.ResultsPatients in low-risk had better overall survival (OS). High-risk patients exhibit higher TMB and lower TIDE scores, and they are more likely to benefit from immunotherapy. The analysis of GEO verified that risk model has a high prediction accuracy.ConclusionThe risk model based on 17 FAM-related DEGs is of great value in predicting the prognosis of LUAD, and these prognostic signatures may be potential therapeutic targets for LUAD.

Background: P-Element-induced wimpy testis (PIWI) proteins, when in combination with PIWI-interacting RNA (piRNA), are engaged in the epigenetic regulation of gene expression in germline cells. Different types of tumour cells have been found to exhibit abnormal expression of piRNA, PIWIL-mRNAs, and proteins. We aimed to determine the mRNA expression profiles of PIWIL1, PIWIL2, PIWIL3, & PIWIL4, in hepatocellular carcinoma patients, and to associate their expression patterns with clinicopathological features.

Methods: The expression patterns of PIWIL1, PIWIL2, PIWIL3, PIWIL4 mRNA, was assessed via real-time quantitative polymerase chain reaction (RT-QPCR), on tissue and serum samples from HCC patients, their impact for diagnosis was evaluated by ROC curves, prognostic utility was determined, and In Silico analysis was conducted for predicted variant detection, association with HCC microRNAs and Network Analysis.

Results: Expression levels were significantly higher in both HCC tissue and serum samples than in their respective controls (p< 0.001). Additionally, the diagnostic performance was assessed, Risk determination was found to be statistically significant.

Conclusion: PIWIL mRNAs are overexpressed in HCC tissue and serum samples, the expression patterns could be valuable molecular markers for HCC, due to their association with age, tumour grade and pattern. To the best of our knowledge, our study is the first to report the expression levels of all PIWIL mRNA and to suggest their remarkable values as diagnostic and prognostic biomarkers, in addition to their correlation to HCC development. Additionally, a therapeutic opportunity might be also suggested through in silico miRNA prediction for HCC and PIWIL genes through DDX4 and miR-124-3p.

Background: Endogenous retroviruses, previously deemed "junk" DNA, have gained attention in recent scientific studies. These inherited genomic elements are now recognized for their potential roles in diseases, especially cancer, highlighting their value as potential diagnostic or therapeutic targets.

Objective: This research aims to explore the association between human endogenous retroviruses (HERV) and gastric cancer, focusing on discerning HERV expression patterns and understanding their implications in gastric cancer pathology.

Methods: A quantitative analysis of HERV expression was conducted, employing Support Vector Machine (SVM) and AdaBoost algorithms to identify discriminative HERVs. The co-regulation network between protein-coding genes and HERVs was constructed using the Weighted Gene Co-expression Network Analysis (WGCNA).

Results: Three distinct HERVs (LTR16A|72|451, LTR91|636|874, LTR27D|87|222) were identified as significantly different. Strong correlations were found between HERVs, and gene sets enriched in the inflammatory pathway.

Conclusions: HERVs appear to influence abnormal inflammatory responses, suggesting a pivotal role in gastric cancer development.

Background: Breast cancer is the most commonly occurring cancer worldwide and is the main cause of death from cancer in women. Novel biomarkers are highly warranted for this disease.

Objective: Evaluation of novel long non-coding RNAs biomarkers for breast cancer.

Methods: The study comprised the analysis of the expression of 71 candidate lncRNAs via screening, six of which (four underexpressed, two overexpressed) were validated and analyzed by qPCR in tumor tissues associated with NST breast carcinomas, compared with the benign samples and with respect to their clinicopathological characteristics.

Results: The results indicated the tumor suppressor roles of PTENP1, GNG12-AS1, MEG3 and MAGI2-AS3. Low levels of both PTENP1 and GNG12-AS1 were associated with worsened progression-free and overall survival rates. The reduced expression of GNG12-AS1 was linked to the advanced stage. A higher grade was associated with the lower expression of PTENP1, GNG12-AS1 and MAGI2-AS3. Reduced levels of both MEG3 and PTENP1 were linked to Ki-67 positivity. The NRSN2-AS1 and UCA1 lncRNAs were overexpressed; higher levels of UCA1 were associated with multifocality.

Conclusions: The results suggest that the investigated lncRNAs may play important roles in breast cancer and comprise a potential factor that should be further evaluated in clinical studies.

Objectives: We aimed to analyze lncRNAs, miRNAs, and mRNA expression profiles of bladder cancer (BC) patients, thereby establishing a gene signature-based risk model for predicting prognosis of patients with BC.

Methods: We downloaded the expression data of lncRNAs, miRNAs and mRNA from The Cancer Genome Atlas (TCGA) as training cohort including 19 healthy control samples and 401 BC samples. The differentially expressed RNAs (DERs) were screened using limma package, and the competing endogenous RNAs (ceRNA) regulatory network was constructed and visualized by the cytoscape. Candidate DERs were screened to construct the risk score model and nomogram for predicting the overall survival (OS) time and prognosis of BC patients. The prognostic value was verified using a validation cohort in GSE13507.

Results: Based on 13 selected. lncRNAs, miRNAs and mRNA screened using L1-penalized algorithm, BC patients were classified into two groups: high-risk group (including 201 patients ) and low risk group (including 200 patients). The high-risk group's OS time ( hazard ratio [HR], 2.160; 95% CI, 1.586 to 2.942; P= 5.678e-07) was poorer than that of low-risk groups' (HR, 1.675; 95% CI, 1.037 to 2.713; P= 3.393 e-02) in the training cohort. The area under curve (AUC) for training and validation datasets were 0.852. Younger patients (age ⩽ 60 years) had an improved OS than the patients with advanced age (age > 60 years) (HR 1.033, 95% CI 1.017 to 1.049; p= 2.544E-05). We built a predictive model based on the TCGA cohort by using nomograms, including clinicopathological factors such as age, recurrence rate, and prognostic score.

Conclusions: The risk model based on 13 DERs patterns could well predict the prognosis for patients with BC.