Isocitrate dehydrogenase 1 (IDH1) mutations have gained interest because of their association with malignancies, including cholangiocarcinoma and acute myeloid leukemia. Ivosidenib, an inhibitor of IDH1 mutations, inhibits the formation of the oncometabolite D-2-HG, restoring normal cellular turnover and inhibiting tumorigenesis. In July 2024, a literature search was done using these databases: PubMed, Cochrane Library, and Embase. Studies were to show the safety and efficacy of ivosidenib using 95% confidence intervals (CIs). Preferred Reporting Items for Systematic reviews and Meta-Analyses flow guidelines were followed. Four articles involving 533 patients were included. The objective response rate (ORR) and progression-free survival (PFS) were significantly improved in the control group where risk ratio was 0.79, 95% CI: 0.71-0.89, Z = 4.05, a P value less than 0.001 for PFS, and odds ratio was 0.45, 95% CI: 0.30-0.68, Z value of 3.86, and P = 0.001 for ORR. The safety profile was favorable. Overall survival (OS) did not change significantly within the groups, as indicated by a P value of 0.78, risk ratio of 0.98, 95% CI: 0.83-1.15, and Z = 0.27. Ivosidenib demonstrated a PFS advantage and improved ORR with a favorable safety profile, but no effect on the OS. Evidence is suggestive of its plausibility for clinical usage as an adjunct therapy.

Accurate breast cancer risk prediction is essential for early detection and personalized prevention strategies. While traditional models, such as Gail and Tyrer-Cuzick, are widely utilized, machine learning-based approaches may offer enhanced predictive performance. This systematic review and meta-analysis compare the accuracy of traditional statistical models and machine learning models in breast cancer risk prediction.

With the widespread application of machine learning (ML) in the diagnosis and treatment of colorectal cancer (CRC), some studies have investigated the use of ML techniques for the diagnosis of KRAS (Kirsten rat sarcoma) mutation. Nevertheless, there is scarce evidence from evidence-based medicine to substantiate its efficacy.

Myasthenia gravis (MG) is an autoimmune neuromuscular disorder characterized by fluctuating muscle weakness. MicroRNAs (miRNAs) have emerged as potential biomarkers for MG diagnosis, offering noninvasive and reliable detection. This systematic review and meta-analysis evaluated the diagnostic accuracy of miRNAs in MG. A comprehensive search of PubMed, Embase, and Google Scholar was conducted up to March 9, 2025. Eligible studies assessing miRNAs as MG biomarkers were selected on the basis of predefined criteria. Pooled sensitivity, specificity, and diagnostic odds ratios (DORs) were calculated via random effects model. Heterogeneity was assessed via I2, and publication bias was evaluated via Deeks' funnel plot. Nine studies including 1,797 participants were analysed. The pooled sensitivity and specificity were 0.80 (95% CI: 0.75-0.84) and 0.71 (95% CI: 0.65-0.77), respectively, with an area under the curve (AUC) of 0.83. Bivariate heterogeneity analysis indicated moderate variability, the cause of which were identified using subgroup analysis with region, clinical subtypes and seropositivity as subgroups. miRNAs demonstrate strong diagnostic potential for MG, with good sensitivity and specificity. However, standardized methodologies and further validation in large, multicentre studies is warranted.

HER2-positive breast cancer accounts for 15-20% of all breast cancers and is characterised by HER2 (also known as ERBB2) amplification. Although HER2-targeted therapies have markedly improved outcomes, current clinical-pathological variables and pathological complete response after neoadjuvant therapy are insufficient to fully capture biological heterogeneity and guide personalised treatment. HER2DX is a genomic test that integrates tumour biology and clinical data to stratify risk. Here, we conducted an individual patient-level meta-analysis to evaluate the association between the HER2DX risk score and survival outcomes in early-stage HER2-positive breast cancer.

Colorectal cancer pathogenesis is a multifactorial process, with genetic factors playing a significant role in cancer development. A review of published meta-analyses on MTHFR gene polymorphisms and colorectal cancer susceptibility showed inconsistent findings and failed to assess the reliability of statistically significant results. Case-control studies were manually searched in databases to investigate the association between MTHFR gene polymorphisms and colorectal cancer. The study assessed the strength of association for the five gene models by calculating odds ratios (ORs) and 95% confidence intervals (CIs). The study results were also analyzed for the source of heterogeneity, sensitivity, publication bias, and false-positive report probability (FPRP) test. Additionally, extensive subgroup analyses were conducted to investigate the impact of confounding factors on the associations. The study suggests that MTHFR C677T gene polymorphism reduces the risk of colorectal cancer in Asian and mixed-race populations, while increasing the risk of Colorectal cancer in the Indian ethnic group. MTHFR A1298C may play a protective role in the development of colorectal cancer. These findings provide valuable insights for the early diagnosis and prevention of Colorectal cancer. However, further studies are required to confirm the association, which may offer additional information for the early diagnosis and prevention of Colorectal cancer.

Gastrointestinal stromal tumors (GISTs) are rare mesenchymal neoplasms of the digestive tract, most commonly originating in the stomach or small intestine, and driven by activating mutations in the KIT or PDGFRA genes. Liquid biopsy has emerged as a promising, minimally invasive technique to detect and monitor circulating tumor DNA (ctDNA), offering real-time insights into tumor dynamics and treatment response. Specifically, detecting KIT/PDGFRA mutations in ctDNA may aid in assessing prognosis, therapeutic response, and resistance. However, the clinical utility of this approach remains unclear. To address this, we conducted a systematic review and meta-analysis to evaluate the prognostic relevance of ctDNA mutations in GIST patients by comparing survival outcomes between those with KIT/PDGFRA mutations and those with wild-type profiles or no detectable ctDNA.

Gastric cancer is an aggressive and heterogeneous disease, primarily sporadic, with only 1-3% of cases being hereditary. However, gastric cancer is a component of several hereditary cancer syndromes. The BRCA1 and BRCA2 genes encode key DNA repair proteins involved in homologous recombination. Studies suggest a significantly increased risk of gastric cancer in first-degree relatives of BRCA1/2 mutation carriers.

Prostate cancer (CaP) is the most commonly diagnosed malignant tumor and the leading cause of cancer-related deaths among men. Due to their potential functional significance, microRNA genes are considered promising candidates for identifying cancer-related genetic biomarkers. This study investigates the association between microRNA-196a2 (rs11614913), microRNA-146a (rs2910164), and microRNA-149 (rs2292832) and the risk of prostate cancer among males in the Jammu region of Jammu and Kashmir (J&K).

Machine learning (ML) algorithms hold the potential to outperform the selection of patients for immunotherapy (ICIs) compared to previous biomarker studies. We analyzed the predictive performance of ML models and compared them to traditional clinical biomarkers (TCBs) in the field of gastrointestinal (GI) cancers. The study has been registered in PROSPERO (number: CRD42023465917). A systematic search of PubMed was conducted to identify studies applying different ML algorithms to GI cancer patients treated with ICIs using tumor RNA gene expression profiles. The outcomes included were response to immunotherapy (ITR) or survival. Additionally, we compared the ML methodology details and predictive power inherent in the published gene sets using 5-fold cross-validation and logistic regression (LR), on an available well-defined ICI-treated metastatic gastric cancer (GC) cohort (n = 45). A set of standard clinical ICI biomarkers (MLH, MSH, and CD8 genes, plus PMS2 and PD-L1)) and de-novo calculated principal components (PCs) of the original datasets were also included as additional points of comparison. Nine articles were identified as eligible to meet the inclusion criteria. Three were pan-cancer studies, five assessed GC, and one studied colorectal cancer (CRC). Classification and regression models were used to predict ICI efficacy. Next, using LR, we validated the predictive power of applied ML algorithms on RNA signatures, using their reported receiver operating characteristics (ROC) analysis area under the curve (AUC) values on a well-defined ICI-treated gastric cancer (GC) dataset (n = 45). In two cases our method has outperformed the published results (reported/LR comparison: 0.74/0.831, 0.67/0.735). Besides the published studies, we have included two benchmarks: a set of TCBs and using principal components based on the whole dataset (PCA, 99% explained variance, 40 components). Interestingly, a study using a selected gene set (immuno-oncology panel) with AUC = 0.83 was the only one that outperformed the TCB (AUC = 0.8) and the PCA (AUC =0.81) results. Cross-validation of the predictive performance of these genes on the same GC dataset and an investigation of their prognostic role on a collated multi-cohort GC dataset of n = 375 resected, or chemotherapy-treated patients revealed that genes mannose-6-phosphate receptor (M6PR), Indoleamine 2,3-Dioxygenase 1 (IDO1), Neuropilin-1 (NRP1), and MAGEA3 performed similarly, or better than established biomarkers like PD-L1 and MSI. We found an immuno-oncology panel with an AUC = 0.83 that outperformed the clinical benchmark or the PC results. We recommend further investigation and experimental validation in the case of M6PR, IDO1, NRP1, and MAGEA3 expressions based on their strong predictive power in GC ITR. Well-designed studies with larger sample sizes and nonlinear ML models might help improve biomarker selections.

Background: A growing number of studies are exploring the association between HOTAIR rs920778 polymorphisms and cancer risk, but to date, there has been controversy and uncertainty. Preliminary evidence suggests that this polymorphism may influence cancer susceptibility, particularly in Asian populations and specific cancer types such as cervical cancer (CC) and breast cancer (BC). We therefore conducted an updated meta-analysis to accurately assess the association of the HOTAIR rs920778 polymorphism with cancer risk. Method: Comprehensive literature searches were performed in PubMed, Embase, and Web of Science up to September 8, 2023. Inclusion criteria included case-control studies with allele frequency data for both cases and controls. A total of 29 case-control studies were selected for quantitative analysis. Crude odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using Stata software (Version 11) to evaluate the association between the rs920778 polymorphism and cancer risk. Heterogeneity and publication bias were assessed using chi-square tests, I2 statistics, and funnel plots with Egger's test. Results: Our analysis of the results found a significant association between the rs920778 polymorphism and cancer susceptibility. In Asian populations, all five genetic models of the rs920778 polymorphism have been shown to increase overall cancer susceptibility. At the same time, we performed stratified analyses based on cancer type and found that all genetic models revealed significantly increased susceptibility to CC in Asian populations. Conversely, the heterozygote model of rs920778 demonstrated significantly reduced susceptibility to BC, with consistent effects across racial groups. Conclusions: Our meta-analysis demonstrated that the HOTAIR rs920778 polymorphism may be a risk factor for cancer but may serve as a protective factor for BC. Future studies require larger sample sizes and gene function analysis, suggesting that the rs920778 polymorphism could serve as a genetic biomarker to guide targeted therapies or cancer screening.

Endometrial cancer is the most common gynaecological cancer in high-income countries. In addition to environmental risk factors, genetic predisposition contributes towards endometrial cancer development but is still incompletely defined.

IntroductionThe MYCN oncogene promotes tumor cell proliferation in neuroblastoma, and its amplification is a well-established marker of poor prognosis. Radiomics-based approaches have shown promise in noninvasively determining MYCN amplification status; however, their diagnostic performance has varied significantly across studies. This systematic review and meta-analysis aimed to quantitatively evaluate the diagnostic accuracy of radiomics-based machine learning models for determining MYCN amplification in neuroblastoma and to critically assess the methodological quality of the included studies.MethodsA systematic search of articles published between January 1, 2000, and June 30, 2024, was conducted across PubMed, Embase, Web of Science, and the Cochrane Library. The articles focused on using radiomics to determine MYCN amplification in neuroblastoma. Methodological quality was assessed using the Radiomics Quality Score (RQS), METhodological RadiomICs Score (METRICS), and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tools. A meta-analysis of validation performance was performed on studies with Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis statement Type 2a or higher.ResultsNine studies with 851 patients were included, and seven studies with 217 patients in the validation set were eligible for meta-analysis. The RQS scores ranged from 10 to 16 (mean 12), and METRICS scores ranged from 28.8% to 78.4% (mean 59.7%). QUADAS-2 assessment indicated that most studies had a low or unclear risk of bias. The pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were 0.78, 0.92, 9.45, and 0.24, respectively. The area under the summary receiver operating characteristic curve was 0.94 (95% confidence interval: 0.91-0.95).ConclusionDespite variability in study design and bias risk, radiomics shows promise as a non-invasive method for detecting MYCN amplification in neuroblastoma. Further refinement and validation in multicenter studies with larger sample sizes are needed to enhance its clinical applicability.

The effectiveness of PD-1 inhibitors for treating endometrial cancer (EC) remains a topic of debate. Guidelines lack consistency regarding the preferred treatments for advanced cases, as well as for patients experiencing metastasis or recurrence. Thus, our goal was to assess the efficacy of Dostarlimab, a PD-1 inhibitor, in EC by incorporating data from clinical trials to create a more comprehensive database.

Circulating tumour DNA (ctDNA) has become a noninvasive biomarker for dynamic monitoring of tumours. However, available evidence on postoperative ctDNA in patients with non-small cell lung cancer (NSCLC) is limited. This systematic review and meta-analysis aims to appraise the prognostic value of postoperative ctDNA in NSCLC.

Malignant pleural effusion (MPE) is a common complication of advanced malignancies, requiring differentiation from benign pleural effusion for appropriate management. Cytology and biopsy have limitations, necessitating more sensitive, less invasive diagnostic techniques. The objective of this study was to evaluate the diagnostic accuracy of methylated SHOX2 (short-stature homeobox 2) and RASSF1A (Ras association domain family member 1A) genes in detecting MPE.

OBJECTIVE: Breast cancer is the most frequently diagnosed malignancy in women and a leading cause of cancer-related mortality. Conventional prognostic tools may not fully capture disease outcomes. MicroRNA (miR) expression has emerged as a potential prognostic factor, though findings remain inconsistent. This systematic review and meta-analysis assess the prognostic role of miR-190, miR-221, and miR-381 in predicting overall survival (OS) among breast cancer patients. MATERIALS AND METHODS: A comprehensive literature search in PubMed, Embase, and Scopus identified relevant studies. Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated to assess the relationship between miR expression and OS. Subgroup analyses were conducted to explore potential sources of heterogeneity. RESULTS: Four studies on miR-221, four on miR-190, and three on miR-381 met inclusion criteria. High miR-190 expression was significantly associated with improved OS (HR: 0.63; 95% CI: 0.47-0.84), as was miR-381 (HR: 0.64; 95% CI: 0.52-0.79). No significant association was found between miR-221 expression and OS (HR: 1.12; 95% CI: 0.86-1.46). Subgroup analysis reinforced these findings, and Newcastle-Ottawa scale assessment indicated low publication bias in 10 out of 11. CONCLUSIONS: Elevated miR-190 and miR-381 levels are associated with improved OS in breast cancer, whereas the prognostic role of miR-221 remains unclear. These findings underscore the potential of miR-190 and miR-381 as prognostic biomarkers.

The cytogenetic abnormalities translocations t(4;14) and t(14;16) and the deletion of chromosome 17p in newly-diagnosed multiple myeloma are associated with poor disease prognosis and are traditionally deemed as "high-risk". However, in the setting of relapsed/refractory multiple myeloma (RRMM) their effect is less characterized. A systematic search was conducted in the PubMed database (end-of-search: 20 August 2024) for randomized controlled trials on anti-myeloma therapies for RRMM that reported outcomes for standard-risk and high-risk patient subgroups. A total of 28 studies were included; 23 reported progression-free survival (PFS) and 8 overall survival (OS) outcomes. Per overall analysis, high-risk cytogenetics were not associated with impaired treatment efficacy compared to standard-risk in terms of both PFS and OS. Among 9 treatment subgroups, high-risk patients on anti-BCMA therapies seemed to exhibit a 18% lower risk of a PFS event compared to the overall treatment effect for this population, but results were not significant. In the subgroup analyses, deletion 17p seemed to have the biggest impact on treatment efficacy, but results were not statistically significant. Overall, the presence of high-risk cytogenetics at study entry in RRMM did not alter treatment efficacy. Novel tools are needed to improve risk stratification at myeloma relapse.

The role of the androgen receptor (AR) in breast cancer (BC) remains incompletely understood. Here, we conducted a meta-analysis of large-scale microarray transcriptomic datasets to evaluate whether the mRNA expression levels of the androgen receptor gene, relative to those of the estrogen receptor gene (AR/ESR1 ratio) and the progesterone receptor gene (AR/PGR ratio), can help differentiate BC tumor subtypes. Additionally, we used qRT-PCR assays to assess the mRNA levels of the AR/ESR1 and AR/PGR ratios in four cell lines representative of different BC subtypes (MCF7, BT474, MDA-MB453, and MDA-MB231), as well as in breast tissue from a small group of patients (11 cases) stratified by estrogen receptor (ER) status. Our results showed that higher AR gene expression relative to ESR1 and PGR (≥ 2.0 and ≥ 1.54, respectively) were associated with BC patients classified under the Luminal B and HER2-enriched subtypes. Positive values of AR/ESR1 and AR/PGR ratios were also observed in the ER-negative (ER-) cell line MDA-MB453, as well as in tumor tissue from ER- BC patients. Our findings confirm that higher or even positive AR/ESR1 and AR/PGR ratios may be associated with BC cases exhibiting more aggressive clinical and biological features, leading to a worse prognosis.

Estrogen exposure is the principal indicator of reproductive health and aging in women. We sought to identify what genetic factors affected lifetime estrogen exposure in a population of East Asian women.