Minimal residual disease profiling predicts pathological complete response in esophageal squamous cell carcinoma.

Accurate presurgical prediction of pathological complete response (pCR) can guide treatment decisions, potentially avoiding unnecessary surgeries and improving the quality of life for cancer patients. We developed a minimal residual disease (MRD) profiling approach with enhanced sensitivity and specificity for detecting minimal tumor DNA from cell-free DNA (cfDNA). The approach was validated in two independent esophageal squamous cell carcinoma (ESCC) cohorts. In a cohort undergoing neoadjuvant, surgical, and adjuvant therapy (NAT cohort), presurgical MRD status precisely predicted pCR. All MRD-negative cases (10/10) were confirmed as pCR by pathological evaluation on the resected tissues. In contrast, MRD-positive cases included all the 27 non-pCR cases and only one pCR case (10/10 vs 1/28, P < 0.0001, Fisher's exact test). In a definitive radiotherapy cohort (dRT cohort), post-dRT MRD status was closely correlated with patient prognosis. All MRD-negative patients (25/25) remained progression-free during the follow-up period, while 23 of the 26 MRD-positive patients experienced disease progression (25/25 vs 3/26, P < 0.0001, Fisher's exact test; progression-free survival, P < 0.0001, log-rank test). The MRD profiling approach effectively predicted the ESCC patients who would achieve pCR with surgery and those likely to remain progression-free without surgery. This suggests that the cancer cells in these MRD-negative patients have been effectively eliminated and they could be suitable candidates for a watch-and-wait strategy, potentially avoiding unnecessary surgery.

TTN Mutation in Endometrial Endometrioid Carcinoma Is Associated with Poor Clinical Outcomes and High Tumor Mutation Burden.

Endometrioid endometrial carcinoma (EEC) stands as a prevalent gynecologic malignancy in developed regions. However, predicting relapse cases remains challenging, necessitating the identification of a novel biomarker for EEC relapse. The assessment of tumor mutational burden (TMB) is pivotal for immunotherapy in EEC patients. However, both whole-exome sequencing (WES) and targeted sequencing encountered application-related difficulties. In light of this, standardized and simplified techniques for TMB measurement are imperative. In this study, we employed WES on 25 EEC patients (12 relapsed cases and 13 non-relapsed cases) who accepted hysterectomy surgery (CHCAMS cohort). We additionally obtained a total of 391 tumor samples with clinicopathological features from TCGA website to broaden the study cohort. In the CHCAMS cohort, the TTN mutant group showed shorter progression-free survival (p < 0.001) and overall survival (p < 0.001) than TTN wild-type group. Additionally, we discovered that the number of TTN mutations per sample was significantly linked with TMB-WES in CHCAMS cohort and TCGA cohort (p < 0.05). And the number of TTN mutations per sample in POLE mutant group was greater than in the POLE wild-type group (p < 0.0001). In conclusion, TTN mutation may serve as a biomarker for EEC prognosis. TTN mutation is also associated with WES-TMB, and could be a simplified TMB measurement technique.

Dynamic Link Prediction for Discovery of New Impactful COVID-19 Research Approaches.

In fighting the COVID-19 pandemic, the main challenges include the lack of prior research and the urgency to find effective solutions. It is essential to accurately and rapidly summarize the relevant research work and explore potential solutions for diagnosis, treatment and prevention of COVID-19. It is a daunting task to summarize the numerous existing research works and to assess their effectiveness. This paper explores the discovery of new COVID-19 research approaches based on dynamic link prediction, which analyze the dynamic topological network of keywords to predict possible connections of research concepts. A dynamic link prediction method based on multi-granularity feature fusion is proposed. Firstly, a multi-granularity temporal feature fusion method is adopted to extract the temporal evolution of different order subgraphs. Secondly, a hierarchical feature weighting method is proposed to emphasize actively evolving nodes. Thirdly, a semantic repetition sampling mechanism is designed to avoid the negative effect of semantically equivalent medical entities on the real structure of the graph, and to capture the real topological structure features. Experiments are performed on the COVID-19 Open Research Dataset to assess the performance of the model. The results show that the proposed model performs significantly better than existing state-of-the-art models, thereby confirming the effectiveness of the proposed method for the discovery of new COVID-19 research approaches.

OsteoporosAtlas: a human osteoporosis-related gene database.

BACKGROUND: Osteoporosis is a common, complex disease of bone with a strong heritable component, characterized by low bone mineral density, microarchitectural deterioration of bone tissue and an increased risk of fracture. Due to limited drug selection for osteoporosis and increasing morbidity, mortality of osteoporotic fractures, osteoporosis has become a major health burden in aging societies. Current researches for identifying specific loci or genes involved in osteoporosis contribute to a greater understanding of the pathogenesis of osteoporosis and the development of better diagnosis, prevention and treatment strategies. However, little is known about how most causal genes work and interact to influence osteoporosis. Therefore, it is greatly significant to collect and analyze the studies involved in osteoporosis-related genes. Unfortunately, the information about all these osteoporosis-related genes is scattered in a large amount of extensive literature. Currently, there is no specialized database for easily accessing relevant information about osteoporosis-related genes and miRNAs. METHODS: We extracted data from literature abstracts in PubMed by text-mining and manual curation. Moreover, a local MySQL database containing all the data was developed with PHP on a Windows server. RESULTS: OsteoporosAtlas (http://biokb.ncpsb.org/osteoporosis/), the first specialized database for easily accessing relevant information such as osteoporosis-related genes and miRNAs, was constructed and served for researchers. OsteoporosAtlas enables users to retrieve, browse and download osteoporosis-related genes and miRNAs. Gene ontology and pathway analyses were integrated into OsteoporosAtlas. It currently includes 617 human encoding genes, 131 human non-coding miRNAs, and 128 functional roles. We think that OsteoporosAtlas will be an important bioinformatics resource to facilitate a better understanding of the pathogenesis of osteoporosis and developing better diagnosis, prevention and treatment strategies.

UVGD 1.0: a gene-centric database bridging ultraviolet radiation and molecular biology effects in organisms.

Objectives: Exposing to ultraviolet for a certain time will trigger some significant molecular biology effects in an organism. In the past few decades, varied ultraviolet-associated biological effects as well as their related genes, have been discovered under biologists' efforts. However, information about ultraviolet-related genes is dispersed in thousands of scientific papers, and there is still no study emphasizing on the systematic collection of ultraviolet-related genes. Methods: We collected ultraviolet-related genes and built this gene-centric database UVGD based on literature mining and manual curation. Literature mining was based on the ultraviolet-related abstracts downloaded from PubMed, and we obtained sentences in which ultraviolet keywords and genes co-occur at single-sentence level by using bio-entity recognizer. After that, manual curation was implemented in order to identify whether the genes are related to ultraviolet or not. Results: We built the ultraviolet-related knowledge base UVGD 1.0 (URL: http://biokb.ncpsb.org/UVGD/ ), which contains 663 ultraviolet-related genes, together with 17 associated biological processes, 117 associated phenotypes, and 2628 MeSH terms. Conclusion: UVGD is helpful to understand the ultraviolet-related biological processes in organisms and we believe it would be useful for biologists to study the responding mechanisms to ultraviolet.