Personalized medicine最新文献

Introduction: Immune checkpoint including VISTA (V-domain Ig suppressor of T cell activation) and BTLA (B and T lymphocyte attenuator) critically regulate tumor immune evasion, and their genetic polymorphisms correlate multiple malignancies. With HBV vaccination and antiviral therapy advancing, non-B non-C hepatocellular carcinoma (NBNC-HCC) prevalence rises, while reliable early diagnosis biomarkers for NBNC-HCC remain lacking.

Aim: Thus, the present study investigates the associations between VISTA and BTLA polymorphisms and the risk of NBNC-HCC in western Guangxi, China, so as to facilitate targeted high-risk population screening for NBNC-HCC.

Methods: A total of 125 NBNC-HCC patients and 150 healthy individuals were enrolled. Genotyping of VISTA (rs748113, rs10762477, rs3747862) and BTLA (rs3112270, rs1982809, rs1844089) was performed using multiplex PCR combined with targeted sequencing.

Results: The AG vs AA, AG-GG vs AA, and allele G vs A of VISTA rs3747862, as well as the AA vs GG, GA-AA vs GG, AA vs GA-GG, and allele A vs G of BTLA rs1844089, were all associated with a significantly increased risk of NBNC-HCC (p < 0.05).

Conclusion: Given the relatively limited sample size and low statistical power, the observed associations between VISTA and BTLA polymorphisms and the risk of NBNC-HCC should be considered hypothesis-generating and require further systematic validation.

Gastric cancer remains among the most common malignancies. Peritoneal metastases account for a significant share of mortality in gastric cancer; however, early detection is constrained by the limited sensitivity of current diagnostic approaches. Computed tomography represents the primary imaging modality for initial assessment, whereas staging laparoscopy, combined with peritoneal cytology and/or biopsy, plays a crucial role in detecting occult peritoneal metastases and confirming the diagnosis. The extent of peritoneal disease, the feasibility of complete cytoreduction, and the postoperative quality of life remain the primary requirements for deciding surgical intervention. As clinical staging and management remain challenging, non-invasive diagnostic and prognostic biomarkers are needed to improve risk stratification and optimize therapeutic strategies. This review summarizes the blood biomarkers reported during the last 5 years and their potential role in predicting peritoneal metastases. A literature search was conducted in PubMed on 12 March 2026, covering publications from March 2021 to March 2026. Ten eligible original studies were included. Although blood-based biomarkers show promise for improving the detection and management of peritoneal metastases in gastric cancer, the available evidence remains limited and heterogeneous. Further prospective, multicenter studies with external validation are required to establish their clinical utility before their integration into routine practice.

Introduction: Acute ischemic stroke (AIS) is a major cause of mortality and disability. Inflammation contributes to ischemic brain injury, and circulating miR-223 and NLRP3 may reflect inflammatory responses. This study evaluated their expression and diagnostic performance in AIS.

Methods: In this case-control study, serum samples were obtained from 45 patients with AIS and 45 healthy controls. Patient blood samples were collected within 24 hours of hospitalization. miR-223 and NLRP3 expression were quantified by quantitative real-time PCR. Spearman correlation, ROC analysis, and multivariable logistic regression were performed.

Results: Circulating miR-223 and NLRP3 expression levels were significantly higher in AIS than in healthy controls (both p < 0.0001). ROC analysis showed AUCs of 0.9704 for NLRP3 and 0.8953 for miR-223. In multivariable logistic regression including age, gender, creatinine, miR-223, and NLRP3, NLRP3 was independently associated with AIS (adjusted OR = 0.569, 95% CI: 0.394-0.822, p = 0.003), whereas miR-223 was not (adjusted OR = 0.943, 95% CI: 0.638-1.395, p = 0.769). Age, gender, and creatinine were not independently associated with AIS.

Conclusion: miR-223 and NLRP3 showed significant differences and discriminatory performance in AIS. NLRP3 was independently associated with AIS, whereas miR-223 was not. Prospective validation is needed.

High-throughput technologies generate large and complex biological datasets that can challenge conventional methods of analysis and visualization. Artificial intelligence (AI) can support the analysis and integration of these datasets, provide new analytical approaches, and accelerate biological research. However, reliable AI applications depend on high-quality, consistent data. Technical errors, bias, and deficiencies in analytical methods can produce misleading findings and undermine reproducibility. AI-based approaches may help identify errors and improve data quality, but poor-quality or biased datasets, limited reproducibility, and concerns about privacy and ethics remain important barriers to their use in biological research. A literature search was performed in PubMed for papers published between 2015 and 2025. This review summarizes recent advances in AI for biological data analysis, including its main applications and potential benefits. It also discusses methodological, translational, and ethical challenges that may limit implementation. Particular attention is given to data quality and reliability, alongside the responsible use of AI in biological research and its translation to personalized medicine.

Introduction: Gastric cancer (GCa) remains one of the most prevalent malignancies worldwide. Genetic variants in the IL-17A, PRKAA1, PSCA, and MIR196A2 genes have been implicated in GCa susceptibility. The data were grouped from 10,353 cases and 10,260 controls. The heterogeneity between the studies was evaluated using the I² statistic, with values between 60% and 75%. The association between variants and the risk of GCa was evaluated by calculating the odds ratios (ORs) with confidence intervals (CIs) of 95%. Statistical significance was defined as p < 0.05.

Areas covered: We evaluated 17 articles that identified genetic variants related to susceptibility to GCa and compiled the observed results through a meta-analysis. Our results revealed that variants were associated with the risk of developing GCa: IL-17A rs2275913 AA genotype (OR: 1.79; 95% CI: 1.53-2.09; p < 0.00001; I² = 14%), as well as PSCA rs2294008 CT (OR: 1.28; 95% CI: 1.12-1.47; p = 0.0003; I² = 0%) and CT/TT (OR: 1.32; 95% CI: 1.15-1.51; p < 0.0001; I² = 0%).

Expert opinion/commentary: Our findings suggest that rs2275913 (IL-17A) and rs2294008 (PSCA) may be significant as susceptibility markers for GCa, potentially representing relevant candidates for future investigations.

Background: Individuals with type 2 diabetes mellitus (T2DM) show variable responses to metformin-sulfonylurea therapy. Genetic variation in insulin signaling and metabolic regulation, particularly in insulin receptor substrate-1 (IRS1) and pancreastatin (PST), may contribute to this variability. This study assessed the influence of IRS1 (rs1801278) and PST (rs9658664) polymorphisms on baseline metabolic profiles and response to combination therapy.

Methods: We enrolled 202 diabetic individuals receiving metformin-sulfonylurea therapy. Clinical and biochemical parameters were measured at baseline and follow-up. Genotyping for IRS1 (rs1801278) and PST (rs9658664) was performed using PCR-RFLP and statistically analyzed by SPSS (v.27). Exploratory molecular docking analyses were performed to examine mutation-dependent differences in PST interactions with insulin-signaling proteins.

Results: Renal complications were more prevalent among non-responders (p = 0.029). IRS1 and PST genotypes were not associated with therapeutic response. Individuals with IRS1 TT genotype exhibited higher baseline cholesterol, triglyceride, and LDL levels (p = 0.05). Docking analyses suggested mutation-dependent differences in PST interactions with insulin receptor, IRS1, and GRP78. Metformin showed slightly stronger predicted binding to insulin receptor and IRS1 than sulfonylurea.

Conclusion: Although IRS1 and PST polymorphisms did not show association with treatment response, genotype-based metabolic variability and in silico analyses suggest functional effects on insulin-signaling pathways, highlighting the potential of personalized medicine approaches for optimizing antidiabetic therapy.

Objectives: We evaluated the transformative impact of AI modalities on clinical practice, distinguishing between retrospective algorithmic performance and prospective clinical effectiveness.

Methods: Comprehensive searches were executed [databases searched: January 2016 to October 2024] across PubMed/Medline, Scopus, and Web of Science, identifying peer-reviewed original research and clinical trials. Non-peer-reviewed materials and commercial web sources were strictly excluded.

Results: AI technologies-particularly deep learning (DL), natural language processing (NLP), and foundation models-demonstrate high diagnostic accuracy in retrospective cohorts across radiology, dermatology, and pathology. In precision therapeutics, AI accelerates drug discovery and facilitates multi-omics integration for individualized treatment planning. Furthermore, autonomous AI systems have proven cost-effective in nationwide diabetic retinopathy screening programs. However, significant gaps remain between in-silico success and real-world clinical utility. Primary barriers include the lack of prospective randomized trials, pervasive risks of algorithmic bias threatening health equity, the "black box" nature of deep neural networks, and stringent interoperability challenges.

Conclusion: AI serves as a potent computational catalyst for advancing personalized medicine. To transition from retrospective experimental success to standard-of-care clinical integration, future research endeavors must prioritize Explainable AI (XAI), federated learning frameworks for privacy preservation, and rigorous prospective clinical validation under standardized regulatory oversight.

Background: The role of microRNA (miRNA) single-nucleotide polymorphisms (SNPs) in breast cancer has been investigated across various populations; however, no published data exist on the association between miRNA SNP-SNP interactions and breast cancer.

Methods: This study aimed to evaluate the associations of miRNA SNPs (miR-27a, miR-196a-2, miR-146a, and miR-499a) and miRNA SNP-SNP interactions with breast cancer in Yemeni women. A case-control study was conducted in 200 women with breast cancer and 200 normal women.

Results: The dominant models of miR-27a and miR-196a-2 SNPs showed protection against breast cancer (OR = 0.40; 0.66, p = 0.00001; 0.046), whereas the additive models of miR-27a and miR-196a-2 were associated with an increased risk of breast cancer (OR = 2.04; 1.44, p = 0.00002; 0.028, respectively). The interactions of miR-27a AG with miR-196a-2 CC and miR-27a AG with miR-196a-2 CT were significantly associated with an increased risk of breast cancer (OR = 2.19; 4.36, Bonferroni p = 0.04; 0.0016, compared to the reference group miR-27a AA with miR-196a-2 CC, respectively.

Conclusion: The study demonstrates that miRNA SNP-SNP interaction enhanced the risk of breast cancer in Yemeni women and provides information about the genetic risk factors that affect the region's susceptibility to breast cancer.

Background: Association of the BCL11A (rs4671393), HBG2 (rs7482144), and HBG2HBS1L-MYB (rs28384513, rs9399137, and rs4895441) variants with susceptibility to thalassemia was observed.

Methodology: Blood samples from 600 individuals, including 300 thalassemia patients and 300 controls, age- and gender-matched, were collected. DNA was extracted, followed by DNA Amplification.

Results: Results show that the homozygous mutant (GG) of variant rs4671393 of the BCL11A gene showed a strong association with increased risk of thalassemia by 3-fold (OR = 3.05; p = 0.0002), heterozygous (AG) also showed a strong association but with a decreased risk (OR = 0.49; p = 0.0028). The heterozygote (AC) and the mutant of variant rs28384513 of gene HBG2HBS1L-MYB showed a significant association by increasing the risk of thalassemia by 2-fold (OR = 2.07; p = 0.001; OR = 2.79; p = 0.0002, respectively). In the case of rs4895441 of gene HBS1L-MYB, the AG heterozygote showed a significant association by increasing the risk by 2.55-fold (OR = 2.55; p = 0.0002). For gene HBG2, the heterozygote (CT) of the rs7482144 variant significantly decreased the risk of thalassemia (OR = 0.37; p = 0.0001). Its homozygous mutant (TT) also showed a significant association, but with an increased risk of thalassemia by 2-fold (OR = 1.81; p = 0.036).       .

Conclusion: The polymorphisms rs4671393 (BCL11A), rs7482144 (HBG2), and rs28384513, rs9399137, and rs4895441 (HBS1L-MYB) are significantly associated with an increased risk of thalassemia.