Phenomics (Cham, Switzerland)最新文献

The DEAD-box RNA helicase (DDX) family plays a critical role in the growth and development of multiple organisms. DDX1 is involved in mRNA/rRNA processing and mature, virus replication and transcription, hormone metabolism, tumorigenesis, and tumor development. However, how DDX1 functions in various cancers remains unclear. Here, we explored the potential oncogenic roles of DDX1 across 33 tumors with The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. DDX1 is highly expressed in breast cancer (BRCA), cholangiocarcinoma (CHOL), and colon adenocarcinoma (COAD), but it is lowly expressed in renal cancers, including kidney renal clear cell carcinoma (KIRC), kidney chromophobe (KICH), and kidney renal papillary cell carcinoma (KIRP). Low expression of DDX1 in KIRC is correlated with a good prognosis of overall survival (OS) and disease-free survival (DFS). Highly expressed DDX1 is linked to a poor prognosis of OS for adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), KICH, and liver hepatocellular carcinoma (LIHC). Also, the residue Ser481 of DDX1 had an enhanced phosphorylation level in BRCA and ovarian cancer (OV) but decreased in KIRC. Immune infiltration analysis exhibited that DDX1 expression affected CD8⁺ T cells, and it was significantly associated with MSI (microsatellite instability), TMB (tumor mutational burden), and ICT (immune checkpoint blockade therapy) in tumors. In addition, the depletion of DDX1 dramatically affected the cell viability of human tumor-derived cell lines. DDX1 could affect the DNA repair pathway and the RNA transport/DNA replication processes during tumorigenesis by analyzing the CancerSEA database. Thus, our pan-cancer analysis revealed that DDX1 had complicated impacts on different cancers and might act as a prognostic marker for cancers such as renal cancer.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-021-00034-x.

Serum liver enzymes (alanine aminotransferase [ALT], aspartate aminotransferase [AST], λ-glutamyl transferase [GGT] and alkaline phosphatase [ALP]) are the leading biomarkers to measure liver injury, and they have been reported to be associated with several intrahepatic and extrahepatic diseases in observational studies. We conducted a phenome-wide association study (PheWAS) to identify disease phenotypes associated with genetically predicted liver enzymes based on the UK Biobank cohort. Univariable and multivariable Mendelian randomization (MR) analyses were performed to obtain the causal estimates of associations that detected in PheWAS. Our PheWAS identified 40 out of 1,376 pairs (16, 17, three and four pairs for ALT, AST, GGT and ALP, respectively) of genotype-phenotype associations reaching statistical significance at the 5% false discovery rate threshold. A total of 34 links were further validated in Mendelian randomization analyses. Most of the disease phenotypes that associated with genetically determined ALT level were liver-related, including primary liver cancer and alcoholic liver damage. The disease outcomes associated with genetically determined AST involved a wide range of phenotypic categories including endocrine/metabolic diseases, digestive diseases, and neurological disorder. Genetically predicted GGT level was associated with the risk of other chronic non-alcoholic liver disease, abnormal results of function study of liver, and cholelithiasis. Genetically determined ALP level was associated with pulmonary heart disease, phlebitis and thrombophlebitis of lower extremities, and hypercholesterolemia. Our findings reveal novel links between liver enzymes and disease phenotypes providing insights into the full understanding of the biological roles of liver enzymes.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-021-00033-y.

Phenomics explores the complex interactions among genes, epigenetics, symbiotic microorganisms, diet, and environmental exposure based on the physical, chemical, and biological characteristics of individuals and groups. Increasingly efficient and comprehensive phenotyping techniques have been integrated into modern phenomics-related research. Multicolor flow cytometry technology provides more measurement parameters than conventional flow cytometry. Based on detailed descriptions of cell phenotypes, rare cell populations and cell subsets can be distinguished, new cell phenotypes can be discovered, and cell apoptosis characteristics can be detected, which will expand the potential of cell phenomics research. Based on the enhancements in multicolor flow cytometry hardware, software, reagents, and method design, the present review summarizes the recent advances and applications of multicolor flow cytometry in cell phenomics, illuminating the potential of applying phenomics in future studies.

Women's health is important for society. Despite the known biological and sex-related factors influencing the risk of diseases among women, the network of the full spectrum of diseases in women is underexplored. This study aimed to systematically examine the women-specific temporal pattern (trajectory) of the disease network, including the role of baseline physical examination indexes, and blood and urine biomarkers. In the UK Biobank study, 502,650 participants entered the cohort from 2006 to 2010, and were followed up until 2019 to identify disease incidence via linkage to the patient registers. For those diseases with increased risk among women, conditional logistic regression models were used to estimate odds ratios (ORs), and the binomial test of direction was further used to build disease trajectories. Among 301 diseases, 82 diseases in women had ORs > 1.2 and p < 0.00017 when compared to men, involving mainly diseases in the endocrine, skeletal and digestive systems. Diseases with the highest ORs included breast diseases, osteoporosis, hyperthyroidism, and deformity of the toes. The biomarker and disease trajectories suggested estradiol as a risk predictor for breast cancer, while a high percentage of reticulocyte, body mass index and waist circumference were associated with an increased risk of upper-limb neuropathy. In addition, the risk of cholelithiasis was increased in women diagnosed with dyspepsia and diaphragmatic hernia. In conclusion, women are at an increased risk of endocrine, skeletal and digestive diseases. The biomarker and disease trajectories in women suggested key pathways to a range of adverse outcomes downstream, which may shed light on promising targets for early detection and prevention of these diseases.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-022-00054-1.

Currently, drug resistance of anti-cancer therapy has become the main cause of low survival rate and poor prognosis. Full understanding of drug resistance mechanisms is an urgent request for further development of anti-cancer therapy and improvement of prognosis. Here we present our N-glycoproteomics study of putative N-glycoprotein biomarkers of drug resistance in doxorubicin resistance breast cancer cell line michigan cancer foundation-7 (MCF-7/ADR) relative to parental michigan cancer foundation-7 (MCF-7) cells. Intact N-glycopeptides (IDs) from MCF-7/ADR and MCF-7 cells were enriched with zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC), labeled with stable isotopic diethylation (SIDE), and analyzed with C18-RPLC-MS/MS (HCD with stepped normalized collision energies); these IDs were identified with database search engine GPSeeker, and the differentially expressed intact N-glycopeptides (DEGPs) were quantified with GPSeekerQuan. With target-decoy searches and control of spectrum-level FDR ≤ 1%, 322 intact N-glycopeptides were identified; these intact N-glycopeptides come from the combination of 249 unique peptide backbones (corresponding to 234 intact N-glycoproteins) and 90 monosaccharide compositions (corresponding to 248 putative N-glycosites). The sequence structures of 165 IDs were confirmed with structure-diagnostic fragment ions. With the criteria of observation at least twice among the three technical replicates, ≥ 1.5-fold change and p value < 0.05, 20 DEGPs were quantified, where five of them were up-regulated and 15 of them were down-regulated; the corresponding intact N-glycoproteins as putative markers of drug resistance were discussed.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-021-00029-8.

Lung nodule classification based on low-dose computed tomography (LDCT) images has attracted major attention thanks to the reduced radiation dose and its potential for early diagnosis of lung cancer from LDCT-based lung cancer screening. However, LDCT images suffer from severe noise, largely influencing the performance of lung nodule classification. Current methods combining denoising and classification tasks typically require the corresponding normal-dose CT (NDCT) images as the supervision for the denoising task, which is impractical in the context of clinical diagnosis using LDCT. To jointly train these two tasks in a unified framework without the NDCT images, this paper introduces a novel self-supervised method, termed strided Noise2Neighbors or SN2N, for blind medical image denoising and lung nodule classification, where the supervision is generated from noisy input images. More specifically, the proposed SN2N can construct the supervision information from its neighbors for LDCT denoising, which does not need NDCT images anymore. The proposed SN2N method enables joint training of LDCT denoising and lung nodule classification tasks by using self-supervised loss for denoising and cross-entropy loss for classification. Extensively experimental results on the Mayo LDCT dataset demonstrate that our SN2N achieves competitive performance compared with the supervised learning methods that have paired NDCT images as supervision. Moreover, our results on the LIDC-IDRI dataset show that the joint training of LDCT denoising and lung nodule classification significantly improves the performance of LDCT-based lung nodule classification.

Alternative splicing (AS) in the tumor biological process has provided a novel perspective on carcinogenesis. However, the clinical significance of individual AS patterns of adrenocortical carcinoma (ACC) has been underestimated, and in-depth investigations are lacking. We selected 76 ACC samples from the Cancer Genome Atlas (TCGA) SpliceSeq and SpliceAid2 databases, and 39 ACC samples from Fudan University Shanghai Cancer Center (FUSCC). Prognosis-related AS events (PASEs) and survival analysis were evaluated based on prediction models constructed by machine-learning algorithm. In total, 23,984 AS events and 3,614 PASEs were detected in the patients with ACC. The predicted risk score of each patient suggested that eight PASEs groups were significantly correlated with the clinical outcomes of these patients (p < 0.001). Prognostic models produced AUC values of 0.907 in all PASEs' groups. Eight splicing factors (SFs), including BAG2, CXorf56, DExD-Box Helicase 21 (DDX21), HSPB1, MBNL3, MSI1, RBMXL2, and SEC31B, were identified in regulatory networks of ACC. DDX21 was identified and validated as a novel clinical promoter and therapeutic target in 115 patients with ACC from TCGA and FUSCC cohorts. In conclusion, the strict standards used in this study ensured the systematic discovery of profiles of AS events using genome-wide cohorts. Our findings contribute to a comprehensive understanding of the landscape and underlying mechanism of AS, providing valuable insights into the potential usages of DDX21 for predicting prognosis for patients with ACC.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-021-00026-x.

Mathematical calculation usually requires sustained attention to manipulate numbers in the mind, while listening to light music has a relaxing effect on the brain. The differences in the corresponding brain functional network topologies underlying these behaviors remain rarely known. Here, we systematically examined the brain dynamics of four behaviors (resting with eyes closed and eyes open, tasks of music listening and mental calculation) using 64-channel electroencephalogram (EEG) recordings and graph theory analysis. We developed static and dynamic minimum spanning tree (MST) analysis method and demonstrated that the brain network topology under mental calculation is a more line-like structure with less tree hierarchy and leaf fraction; however, the hub regions, which are mainly located in the frontal, temporal and parietal regions, grow more stable over time. In contrast, music-listening drives the brain to exhibit a highly rich network of star structure, and the hub regions are mainly located in the posterior regions. We then adopted the dynamic dissimilarity of different MSTs over time based on the graph Laplacian and revealed low dissimilarity during mental calculation. These results suggest that the human brain functional connectivity of individuals has unique dynamic diversity and flexibility under various behaviors.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-021-00027-w.

Cardiovascular diseases (CVDs) are a large group of diseases and have become the leading cause of morbidity and mortality worldwide. Although considerable progresses have been made in the diagnosis, treatment and prognosis of CVD, communication barriers between clinicians and researchers still exist because the phenotypes of CVD are complex and diverse in clinical practice and lack of unity. Therefore, it is particularly important to establish a standardized and unified terminology to describe CVD. In recent years, there have been several studies, such as the Human Phenotype Ontology, attempting to provide a standardized description of the disease phenotypes. In the present article, we outline recent advances in the classification of the major types of CVD to retrospectively review the current progresses of phenotypic studies in the cardiovascular field and provide a reference for future cardiovascular research.

β cells are defined by the ability to produce and secret insulin. Recent studies have evaluated that human pancreatic β cells are heterogeneous and demonstrated the transcript alterations of β cell subpopulation in diabetes. Single-cell RNA sequence (scRNA-seq) analysis helps us to refine the cell types signatures and understand the role of the β cells during metabolic challenges and diseases. Here, we construct the pseudotime trajectory of β cells from publicly available scRNA-seq data in health and type 2 diabetes (T2D) based on highly dispersed and highly expressed genes using Monocle2. We identified three major states including 1) Normal branch, 2) Obesity-like branch and 3) T2D-like branch based on biomarker genes and genes that give rise to bifurcation in the trajectory. β cell function-maintain-related genes, insulin expression-related genes, and T2D-related genes enriched in three branches, respectively. Continuous pseudotime spectrum might suggest that β cells transition among different states. The application of pseudotime analysis is conducted to clarify the different cell states, providing novel insights into the pathology of β cells in T2D.

Supplementary information: The online version contains supplementary material is available at 10.1007/s43657-021-00024-z.