Search

Explicitly sharing individual level data in genomics studies has many merits comparing to sharing summary statistics, including more strict QCs, common statistical analyses, relative identification and improved statistical power in GWAS, but it is hampered by privacy or ethical constraints. In this study, we developed encG-reg, a regression approach that can detect relatives of various degrees based on encrypted genomic data, which is immune of ethical constraints. The encryption properties of encG-reg are based on the random matrix theory by masking the original genotypic matrix without sacrificing precision of individual-level genotype data. We established a connection between the dimension of a random matrix, which masked genotype matrices, and the required precision of a study for encrypted genotype data. encG-reg has false positive and false negative rates equivalent to sharing original individual level data, and is computationally efficient when searching relatives. We split the UK Biobank into their respective centers, and then encrypted the genotype data. We observed that the relatives estimated using encG-reg was equivalently accurate with the estimation by KING, which is a widely used software but requires original genotype data. In a more complex application, we launched a finely devised multi-center collaboration across 5 research institutes in China, covering 9 cohorts of 54,092 GWAS samples. encG-reg again identified true relatives existing across the cohorts with even different ethnic backgrounds and genotypic qualities. Our study clearly demonstrates that encrypted genomic data can be used for data sharing without loss of information or data sharing barrier. Author summary: Estimating pairwise genetic relatedness within a single cohort is straightforward. However, in practice, related samples are often distributed across different cohorts, making it challenging to estimate inter-cohort relatedness. In this study, we propose a method called encrypted genotype regression (encG-reg), which provides an unbiased estimation of inter-cohort relatedness using encrypted genotypes. The genotype matrix of each cohort is masked by a random matrix, which acts similarly to a private key in a cryptographic scheme. This masking process produces encrypted genotypes, which are a projection of the original genotype matrix. We derive the expectation and particularly the sampling variance for encG-reg, the latter involves eighth-order moments calculation. encG-reg allows us to accurately identify relatedness across cohorts, even for large-scale biobank data. To demonstrate the efficacy of encG-reg, we verified it in a multi-ethnicity UK Biobank dataset comprising 485,158 samples. For this case, we successfully tracked down to the 1st-degree relatedness (such as full sibs and parent-offspring). Furthermore, we used encG-reg in a collaboration involving 9 Chinese cohorts, encompassing a total of 54,092 samples from 5 genomic centers. It is worth noting that if the number of effective markers is sufficient encG-reg has the potential to detect even more distant degrees of relatedness beyond what we demonstrated. [ABSTRACT FROM AUTHOR]

Lung cancer is one of the most common malignant tumours. Patients are frequently at risk of frailty as lung cancer progresses. The meta-analysis aims to explore the impact of frailty on the long-term prognosis and the incidence of short-term chemotherapy toxicity in patients with lung cancer. This study was designed adhered to the criteria of Cochrane Handbook for Systematic Reviews. Systematic searches were performed on PubMed, Embase, Web of Science and Cochrane Library databases for relevant studies until December 2022. The outcome measures were overall survival, progression-free survival, chemotherapy toxicity and all-cause mortality. We then performed sensitivity analyses, subgroup analyses and evidence quality. This meta-analysis was performed using Review Manager V.5.4 software. Of the included studies, six were retrospective and five were prospective. There was a statistically significant difference between the frail and non-frail groups in overall survival (HR 2.27, 95% CI 1.24 to 4.15, p=0.008), all-cause mortality (HR 1.63, 95% CI 1.00 to 2.65, p=0.05) and chemotherapy toxicity (OR 3.73, 95% CI 1.99 to 7.00, p<0.0001). We conducted a sensitivity analysis, and the result was stable. The study revealed frail group had shorter survival and experienced more severe adverse effects than the non-frail group. Frailty affects the long-term prognosis and the incidence of short-term chemotherapy toxicity of patients with lung cancer. Consequently, medical professionals should focus on frailty screening in patients with lung cancer and implement active intervention measures. PROSPERO registration number is CRD42023398606., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: This review describes the comprehensive portrait of tumor microenvironment (TME). Additionally, we provided a panoramic perspective on the transformation and functions of the diverse constituents in TME, and the underlying mechanisms of drug resistance, beginning with the immune cells and metabolic dynamics within TME. Lastly, we summarized the most auspicious potential therapeutic strategies., Results: TME is a unique realm crafted by malignant cells to withstand the onslaught of endogenous and exogenous therapies. Recent research has revealed many small-molecule immunotherapies exhibiting auspicious outcomes in preclinical investigations. Furthermore, some pro-immune mechanisms have emerged as a potential avenue. With the advent of nanosystems and precision targeting, targeted therapy has now transcended the "comfort zone" erected by cancer cells within TME., Conclusion: The ceaseless metamorphosis of TME fosters the intransigent resilience and proliferation of tumors. However, existing therapies have yet to surmount the formidable obstacles posed by TME. Therefore, scientists should investigate potential avenues for therapeutic intervention and design innovative pharmacological and clinical technologies., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Objective: To study the difference in age estimation based on quantitative analysis of DNA methylation by MassARRAY and pyrosequencing techniques., Methods: The methylation levels of 9 CpG sites from two independent whole blood sample sets (containing 65 and 62 samples) were detected using MassARRAY and pyrosequencing techniques. Z-score transformation was used to remove the batch effects of different techniques, and a linear regression model was used for age prediction., Results: For age prediction using the MassARRAY system, the 65 samples showed a mean absolute difference (MAD) of 2.49 years before Z-score transformation of the data and 2.44 years after the transformation, similar to the results in the 62 samples (MAD of 3.36 years before and 3.42 years after Z-score transformation). For data typed from pyrosequencing, the 65 samples showed a MAD of 4.20 years before and 2.76 years after data Z-score transformation, also similar to the results in the 62 samples (MAD of 3.92 years before and 3.63 years after data transformation)., Conclusions: Z-score transformation can effectively reduce the system batch effect between MassARRAY and pyrosequencing. Data from the MassARRAY system allows direct age estimation without further data processing, while the pyrosequencing data may increase the error in age estimation, which can be corrected by Z-score transformation of the data.