Personalized medicine最新文献

Objective: To study the connection between lncRNA CASC11 plasma expression and clinical characteristics as well as prognoses of patients. Methods: Sixty lung adenocarcinoma (LUAD) patients and 60 healthy adults participated in this research. LncRNA CASC11 expression was detected by quantitative real-time PCR and the relationships between lncRNA CASC11 plasma expression and patient prognosis and clinical characteristics were analyzed. Results: LncRNA CASC11 was highly expressed in LUAD tissues and plasma (p < 0.05). Receiver operating characteristic curve analysis indicated the diagnostic value of lncRNA CASC11 in LUAD (p < 0.0001). Higher plasma lncRNA CASC11 expression indicated worse patient prognoses (p < 0.05). Plasma lncRNA CASC11 level was also closely related to tumor differentiation and tumor-node-metastasis stage (p < 0.05). Conclusion: Plasma lncRNA CASC11 may be a potential biomarker for LUAD diagnosis and prognosis.

Hepatocellular carcinoma (HCC) and pancreatic cancer (PC) are among serious malignancies with no proper biomarker suffering from poor prognosis and late onset. Regulation of long noncoding RNAs (lncRNAs) is disturbed in tumors, making them appropriate diagnostic markers or therapeutic targets in systemic therapies. The expression and function of some significant lncRNAs are under the influence of SNPs, highlighting their key role in carcinogenesis. This review assesses the associations between SNPs in lncRNAs and HCC and PC risk. A panel of cancer-associated SNPs in lncRNA genes could help evaluate the clinical use of lncRNAs, including their role as diagnostic markers and therapeutic targets. Nonetheless, more large-scale surveys on various ethnic groups are required to validate results.

Aim: Transcriptional regulation is actively involved in the onset and progression of various diseases. This study used the feature-engineering approach model-based quantitative transcription regulation to quantitatively measure the correlation between mRNA and transcription factors in a reference dataset of chronic lymphocytic leukemia (CLL) transcriptomes. Methods: A comprehensive investigation of transcriptional regulation changes in CLL was conducted using 973 samples in six independent datasets. Results & conclusion: Seven mRNAs were detected to have significantly differential model-based quantitative transcription regulation values but no differential expression between CLL patients and controls. We called these genes 'dark biomarkers' because their original expression levels did not show differential changes in the CLL patients. The overlapping lncRNAs might have contributed their transcripts to the expression miscalculations of these dark biomarkers.

Aim: To evaluate the ex vivo efficacy of chemotherapy, immunotherapy and targeted agents with the oncogram method in patients with bladder cancer and determine the most appropriate personalized treatment agent using immune markers. Materials & methods: Bladder cancer tissues were obtained from each patient. After cultivation, cell cultures were divided into 12 groups for each patient and 11 drugs were administered. Cell viability and immunohistochemistry expression were examined. Results: A good response rate was determined to be a 23% viability drop. The nivolumab good response rate was slightly better in PD-L1-positive patients and the ipilimumab good response rate was slightly better in tumoral CTLA-4-positive cases. Interestingly, the cetuximab response was worse in EGFR-positive cases. Conclusion: Although good responses of drug groups after their ex vivo application by using oncogram were found to be higher than control group, this outcome differed on a per patient basis.

Hypothyroidism in the setting of cancer is a puzzling entity due to the dual role of the thyroid hormones (TH) in cancer - promoting versus inhibitory - and the complexity of the hypothyroidism itself. The present review provides a comprehensive overview of the personalized approach to hypothyroidism in patients with solid nonthyroid cancer, focusing on current challenges, unmet needs and future perspectives. Major electronic databases were searched from January 2011 until March 2022. The milestones of the refinement of such a personalized approach are prompt diagnosis, proper TH replacement and development of interventions and/or pharmaceutical agents to exploit hypothyroidism or, on the contrary, TH replacement as an anticancer strategy. Further elucidation of the dual role of TH in cancer - especially of the interference of TH signaling with the hallmarks of cancer - is anticipated to inform decision-making and optimize patient selection.

Hyperlipidemia is a significant risk factor for cardiovascular disease morbidity and mortality. The lipid-lowering drugs are considered the cornerstone of primary and secondary prevention of atherosclerotic cardiovascular disease. Unfortunately, the lack of efficacy and associated adverse effects, ranging from mild-to-moderate to potentially life-threatening, lead to therapy discontinuation. Numerous reports support the role of gene polymorphisms in drugs' pharmacokinetic parameters and their associated adverse reactions. Therefore, this study aims to understand the pharmacogenomics of lipid-lowering drugs and the impact of genetic variants of key genes on the drugs' efficacy and toxicity. Indeed, genetically guided lipid-lowering therapy enhances overall safety, improves drug adherence and achieves long-term therapy.

A patient with acute myeloid leukemia presented various episodes of febrile neutropenia, for which there was no positive response to antibiotic treatments. Following an episode of bacteremia by extensively drug-resistant Klebsiella pneumoniae, amikacin was prescribed, pharmacokinetic analyses of its plasma concentrations were performed and the dosage interval was narrowed to 12 and 8 h in order to counteract the reduced postantibiotic effect due to the patient being immunocompromised. The patient responded positively, with procalcitonin decreasing and body temperature normalizing. Recovery was finally achieved, without renal or auditory damage. This case proposes tightening dosage intervals for aminoglycosides as an effective strategy in immunocompromised patients. Aminoglycosides are given over extended intervals (24 h), considering concentration-dependent effectiveness, nephrotoxicity and postantibiotic effect. Leukocytes appear to play a determining role in the postantibiotic effect, with no proposed dosing strategy for strongly immunocompromised patients.

Aim: This study aimed to identify DPYD variants and the related but previously unexplored phenotype (plasma uracil, dihydrouracil [DHU], and the DHU-to-uracil ratio) in a healthy adult Indian population. Methods: Healthy adult volunteers (n = 100) had their uracil and DHU levels measured and were genotyped for selected variants. Results: Among the nine variants studied, c.1906-14763G>A and c.85T>C were the most prevalent. Participants with any of the variants except for c.85T>C and c.1627A>G had a significantly lower DHU-to-uracil ratio and those with c.1905+1G>A variant had significantly increased uracil concentration compared with wild-type. Conclusion: Participants with five variants were identified as having altered phenotypic measures, and 40% of the intermediate metabolizers had their phenotype in the terminal population percentiles.

The effectiveness and side effects of Type 2 diabetes (T2D) medication are related to individual genetic background. SNPs CYP3A4 and CYP2C19 were introduced to machine-learning models to improve the performance of T2D medication prediction. Two multilabel classification models, ML-KNN and WRank-SVM, trained with clinical data and CYP3A4/CYP2C19 SNPs were evaluated. Prediction performance was evaluated with Hamming loss, one-error, coverage, ranking loss and average precision. The average precision of ML-KNN and WRank-SVM using clinical data was 92.74% and 92.9%, respectively. Combined with CYP2C19*2*3, the average precision dropped to 88.84% and 89.93%, respectively. While combined with CYP3A4*1G, the average precision was enhanced to 97.96% and 97.82%, respectively. Results suggest that CYP3A4*1G can improve the performance of ML-KNN and WRank-SVM models in predicting T2D medication performance.