Robust Signature Research Articles

Predictive liver lipid biomarker signature of Acetyl-coenzyme A carboxylase inhibitor related developmental toxicity in non-pregnant female Han Wistar rats – Lipidomics biomarker discovery and validation

IntroductionAcetyl-coenzyme A carboxylase (ACCase) inhibition is an attractive herbicide target. However, issues with fetal developmental toxicity identified at the late stages of the development process can halt progression of previously promising candidates. ObjectivesTo select and verify predictive lipid biomarkers of ACCase inhibition activity in vivo using liver samples obtained from early stage 7 day repeat dose studies in non-pregnant female Han Wistar rats that could be translated to developmental toxicity endpoints discovered during late-stage studies to provide an early screening tool. MethodsLiver samples from eight rat repeat dose studies, exposed to six ACCase inhibitors from three different chemistries and one alternative mode of action (MoA) that also perturbs lipid biochemistry, were analysed using liquid chromatography – high resolution accurate mass – mass spectrometry. Multivariate and univariate data analysis methods were used for biomarker discovery and validation. ResultsA biomarker signature consisting of sixteen lipids biomarkers were selected. Verification of the signature as indicative of ACCase inhibition was established by demonstrating consistent perturbations in the biomarkers using two different ACCase inhibitor chemistries and the lack thereof with an alternate MoA. The fold change profile pattern was predictive of which test substance doses would or would not cause developmental toxicity. ConclusionA strategy for selecting and verifying a robust signature of lipid biomarkers for predicting a toxicological end point has been described and demonstrated. Differences in lipidomic profiles correlated with developmental toxicity suggesting that indicators of a molecular initiation event resulting in pup developmental toxicity can be predicted from short term, toxicity studies conducted in non-pregnant adult female Han Wistar rats.

Risk stratification based on DNA damage-repair-related signature reflects the microenvironmental feature, metabolic status and therapeutic response of breast cancer.

DNA damage-repair machinery participates in maintaining genomic integrity and affects tumorigenesis. Molecular signatures based on DNA damage-repair-related genes (DRGs) capable of comprehensively indicating the prognosis, tumor immunometabolic profile and therapeutic responsiveness of breast cancer (BRCA) patients are still lacking. Integrating public datasets and bioinformatics algorithms, we developed a robust prognostic signature based on 27 DRGs. Multiple patient cohorts identified significant differences in various types of survival between high- and low-risk patients stratified by the signature. The signature correlated well with clinicopathological factors and could serve as an independent prognostic indicator for BRCA patients. Furthermore, low-risk tumors were characterized by more infiltrated CD8+ T cells, follicular helper T cells, M1 macrophages, activated NK cells and resting dendritic cells, and fewer M0 and M2 macrophages. The favorable immune infiltration patterns of low-risk tumors were also accompanied by specific metabolic profiles, decreased DNA replication, and enhanced antitumor immunity. Low-risk patients may respond better to immunotherapy, and experience improved outcomes with conventional chemotherapy or targeted medicine. Real-world immunotherapy and chemotherapy cohorts verified the predictive results. Additionally, four small molecule compounds promising to target high-risk tumors were predicted. In vitro experiments confirmed the high expression of GNPNAT1 and MORF4L2 in BRCA tissues and their association with immune cells, and the knockdown of these two DRGs suppressed the proliferation of human BRCA cells. In summary, this DNA damage-repair-related signature performed well in predicting patient prognosis, immunometabolic profiles and therapeutic sensitivity, hopefully contributing to precision medicine and new target discovery of BRCA.

Toward a statistical validation of brain signatures as robust measures of behavioral substrates.

The "brain signature of cognition" concept has garnered interest as a data-driven, exploratory approach to better understand key brain regions involved in specific cognitive functions, with the potential to maximally characterize brain substrates of behavioral outcomes. Previously we presented a method for computing signatures of episodic memory. However, to be a robust brain measure, the signature approach requires a rigorous validation of model performance across a variety of cohorts. Here we report validation results and provide an example of extending it to a second behavioral domain. In each of two discovery data cohorts, we derived regional brain gray matter thickness associations for two domains: neuropsychological and everyday cognition memory. We computed regional association to outcome in 40 randomly selected discovery subsets of size 400 in each cohort. We generated spatial overlap frequency maps and defined high-frequency regions as "consensus" signature masks. Using separate validation datasets, we evaluated replicability of cohort-based consensus model fits and explanatory power by comparing signature model fits with each other and with competing theory-based models. Spatial replications produced convergent consensus signature regions. Consensus signature model fits were highly correlated in 50 random subsets of each validation cohort, indicating high replicability. In comparisons over each full cohort, signature models outperformed other models. In this validation study, we produced signature models that replicated model fits to outcome and outperformed other commonly used measures. Signatures in two memory domains suggested strongly shared brain substrates. Robust brain signatures may therefore be achievable, yielding reliable and useful measures for modeling substrates of behavioral domains.

Deciphering novel common gene signatures for rheumatoid arthritis and systemic lupus erythematosus by integrative analysis of transcriptomic profiles.

Rheumatoid Arthritis (RA) and Systemic Lupus Erythematosus (SLE) are the two highly prevalent debilitating and sometimes life-threatening systemic inflammatory autoimmune diseases. The etiology and pathogenesis of RA and SLE are interconnected in several ways, with limited knowledge about the underlying molecular mechanisms. With the motivation to better understand shared biological mechanisms and determine novel therapeutic targets, we explored common molecular disease signatures by performing a meta-analysis of publicly available microarray gene expression datasets of RA and SLE. We performed an integrated, multi-cohort analysis of 1088 transcriptomic profiles from 14 independent studies to identify common gene signatures. We identified sixty-two genes common among RA and SLE, out of which fifty-nine genes (21 upregulated and 38 downregulated) had similar expression profiles in the diseases. However, antagonistic expression profiles were observed for ACVR2A, FAM135A, and MAPRE1 genes. Thirty genes common between RA and SLE were proposed as robust gene signatures, with persistent expression in all the studies and cell types. These gene signatures were found to be involved in innate as well as adaptive immune responses, bone development and growth. In conclusion, our analysis of multicohort and multiple microarray datasets would provide the basis for understanding the common mechanisms of pathogenesis and exploring these gene signatures for their diagnostic and therapeutic potential.

A novel immune-related model to predict prognosis and responsiveness to checkpoint and angiogenesis blockade therapy in advanced renal cancer.

Immune checkpoint blockade (ICB) and anti-angiogenic drug combination has prolonged the survival of patients with advanced renal cell carcinoma (RCC). However, not all patients receive clinical benefits from this intervention. In this study, we aimed to establish a promising immune-related prognostic model to stratify the patients responding to ICB and anti-angiogenic drug combination and facilitate the development of personalized therapies for patients with RCC. Based on clinical annotations and RNA-sequencing (RNA-seq) data of 407 patients with advanced RCC from the IMmotion151 cohort, nine immune-associated differentially expressed genes (DEGs) between responders and non-responders to atezolizumab (anti-programmed death-ligand 1 antibody) plus bevacizumab (anti-vascular endothelial growth factor antibody) treatment were identified via weighted gene co-expression network analysis. We also conducted single-sample gene set enrichment analysis to develop a novel immune-related risk score (IRS) model and further estimate the prognosis of patients with RCC by predicting their sensitivity to chemotherapy and responsiveness to immunotherapy. IRS model was further validated using the JAVELIN Renal 101 cohort, the E-MTAB-3218 cohort, the IMvigor210 and GSE78220 cohort. Predictive significance of the IRS model for advanced RCC was assessed using receiver operating characteristic curves. The IRS model was constructed using nine immune-associated DEGs: SPINK5, SEMA3E, ROBO2, BMP5, ORM1, CRP, CTSE, PMCH and CCL3L1. Advanced RCC patients with high IRS had a high risk of undesirable clinical outcomes (hazard ratio = 1.91; 95% confidence interval = 1.43-2.55; P < 0.0001). Transcriptome analysis revealed that the IRS-low group exhibited significantly high expression levels of CD8+ T effectors, antigen-processing machinery, and immune checkpoints, whereas the epithelial-mesenchymal transition pathway was enriched in the IRS-high group. IRS model effectively differentiated the responders from non-responders to ICB combined with angiogenesis blockade therapy or immunotherapy alone, with area under the curve values of 0.822 in the IMmotion151 cohort, 0.751 in the JAVELIN Renal 101 cohort, and 0.776 in the E-MTAB-3218 cohort. IRS model is a reliable and robust immune signature that can be used for patient selection to optimize the efficacy of ICB plus anti-angiogenic drug therapies in patients with advanced RCC.

Expression pattern, tumor immune landscape, and prognostic value of N7‑methylguanosine regulators in bladder urothelial carcinoma.

N7-Methylguanosine (m7G) modification is important in post-transcriptional regulation. dysregulation of m7G RNA modification has been reported to be markedly associated with cancer. However, its importance in bladder urothelial carcinoma (BLCA) remains poorly characterized. The present study systematically analyzed mRNA gene expression data and clinical information from The Cancer Genome Atlas and further constructed robust risk signatures for the four regulators of m7G RNA modification (nudix hydrolase 11, gem nuclear organelle-associated protein 5, eukaryotic translation initiation factor 3 subunit D and cytoplasmic FMR1 interacting protein 1). The differential expression and cell function of m7G-related genes in bladder cancer cells were verified by reverse transcription-quantitative PCR, Cell Counting Kit-8 and colony formation assays. The four-gene-based model could accurately predict the prognosis of BLCA. Nomogram-based clinical decisions had a higher net benefit compared with that of individual predictors. Through immune infiltration analysis, it was found that immune cell infiltration affected the prognosis of patients with BLCA. Finally, the present study identified potential therapeutics that differ between high and low-risk groups based on four genes. In summary, the current findings revealed an essential role for m7G RNA modification regulators in BLCA, and developed risk signatures as promising prognostic markers in patients with BLCA.

Development and Experimental Validation of a Novel Prognostic Signature for Gastric Cancer

Gastric cancer is a malignant tumor with high morbidity and mortality. Therefore, the accurate recognition of prognostic molecular markers is the key to improving treatment efficacy and prognosis. In this study, we developed a stable and robust signature through a series of processes using machine-learning approaches. This PRGS was further experimentally validated in clinical samples and a gastric cancer cell line. The PRGS is an independent risk factor for overall survival that performs reliably and has a robust utility. Notably, PRGS proteins promote cancer cell proliferation by regulating the cell cycle. Besides, the high-risk group displayed a lower tumor purity, higher immune cell infiltration, and lower oncogenic mutation than the low-PRGS group. This PRGS could be a powerful and robust tool to improve clinical outcomes for individual gastric cancer patients.

Insights into pulmonary phosphate homeostasis and osteoclastogenesis emerge from the study of pulmonary alveolar microlithiasis

Pulmonary alveolar microlithiasis is an autosomal recessive lung disease caused by a deficiency in the pulmonary epithelial Npt2b sodium-phosphate co-transporter that results in accumulation of phosphate and formation of hydroxyapatite microliths in the alveolar space. The single cell transcriptomic analysis of a pulmonary alveolar microlithiasis lung explant showing a robust osteoclast gene signature in alveolar monocytes and the finding that calcium phosphate microliths contain a rich protein and lipid matrix that includes bone resorbing osteoclast enzymes and other proteins suggested a role for osteoclast-like cells in the host response to microliths. While investigating the mechanisms of microlith clearance, we found that Npt2b modulates pulmonary phosphate homeostasis through effects on alternative phosphate transporter activity and alveolar osteoprotegerin, and that microliths induce osteoclast formation and activation in a receptor activator of nuclear factor-κB ligand and dietary phosphate dependent manner. This work reveals that Npt2b and pulmonary osteoclast-like cells play key roles in pulmonary homeostasis and suggest potential new therapeutic targets for the treatment of lung disease.

First-in-human phase I study of the OX40 agonist GSK3174998 with or without pembrolizumab in patients with selected advanced solid tumors (ENGAGE-1)

BackgroundThe phase I first-in-human study ENGAGE-1 evaluated the humanized IgG1 OX40 agonistic monoclonal antibody GSK3174998 alone (Part 1 (P1)) or in combination with pembrolizumab (Part 2 (P2)) in patients with...

A transcriptomic intratumour heterogeneity-free signature overcomes sampling bias in prognostic risk classification for hepatocellular carcinoma

Background & AimsIntratumour heterogeneity (ITH) fosters the vulnerability of RNA expression-based biomarkers derived from a single biopsy to tumour sampling bias, and is regarded as an unaddressed confounding factor for patient precision stratification using molecular biomarkers. This study aimed to identify an ITH-free predictive biomarker in hepatocellular carcinoma (HCC). MethodsWe interrogated the confounding effect of ITH on performance of molecular biomarkers and quantified transcriptomic heterogeneity utilising three multiregional HCC transcriptome datasets involving 142 tumoural regions from 30 patients. A de novo strategy based on the heterogeneity metrics was devised to develop a surveillant biomarker (a utility gadget using RNA; AUGUR) using three datasets involving 715 liver samples from 509 patients with HCC. The performance of AUGUR was assessed in seven cross-platform HCC cohorts that encompassed 1,206 patients. ResultsAn average discordance rate of 39.9% at the level of individual patients was observed applying 13 published prognostic signatures to classify tumour regions. We partitioned genes into four heterogeneity quadrants, from which we developed and validated a reproducible robust ITH-free expression signature AUGUR that showed significant positive associations with adverse features of HCC. High AUGUR risk increased the risk of disease progression and mortality independent of established clinicopathological indices, which maintained concordance across seven cohorts. Moreover, AUGUR compared favourably to the discriminative ability, prognostic accuracy, and patient risk concordant rates of 13 published signatures. Finally, a well-calibrated predictive nomogram integrating AUGUR and tumour-node-metastasis (TNM) stage was established, which generated a numerical probability of mortality. ConclusionsWe constructed and validated an ITH-free AUGUR and nomogram that overcame sampling bias and provided reliable prognostic information for patients with HCC. Impact and ImplicationsIntratumour heterogeneity (ITH) is prevalent in hepatocellular carcinoma (HCC), and is regarded as an unaddressed confounding factor for biomarker design and application. We examined the confounding effect of transcriptomic ITH in patient risk classification, and found existing molecular biomarkers of HCC were vulnerable to tumour sampling bias. We then developed an ITH-free expression biomarker (a utility gadget using RNA; AUGUR) that overcame clinical sampling bias and maintained prognostic reproducibility and generalisability across multiple HCC patient cohorts from different commercial platforms. Furthermore, we established and validated a well-calibrated nomogram based on AUGUR and tumour-node-metastasis (TNM) stage that provided an individualised prognostic information for patients with HCC.

Deep next-generation proteomics and network analysis reveal systemic and tissue-specific patterns in Fabry disease

Fabry disease (FD) is an X-linked lysosomal rare disease due to a deficiency of α-galactosidase A activity. The accumulation of glycosphingolipids mainly affects the kidney, heart, and central nervous system, considerably reducing life expectancy. Although the accumulation of undegraded substrate is considered the primary cause of FD, it is established that secondary dysfunctions at the cellular, tissue, and organ levels ultimately give rise to the clinical phenotype. To parse this biological complexity, a large-scale deep plasma targeted proteomic profiling has been performed. We analyzed the plasma protein profiles of FD deeply phenotyped patients (n = 55) compared to controls (n = 30) using next-generation plasma proteomics including 1463 proteins. Systems biology and machine learning approaches have been used. The analysis enabled the identification of proteomic profiles that unambiguously separated FD patients from controls (615 differentially expressed proteins, 476 upregulated, and 139 downregulated) and 365 proteins are newly reported. We observed functional remodeling of several processes, such as cytokine-mediated pathways, extracellular matrix, and vacuolar/lysosomal proteome. Using network strategies, we probed patient-specific tissue metabolic remodeling and described a robust predictive consensus protein signature including 17 proteins CD200, SPINT1, CD34, FGFR2, GRN, ERBB4, AXL, ADAM15, PTPRM, IL13RA1, NBL1, NOTCH1, VASN, ROR1, AMBP, CCN3, and HAVCR2. Our findings highlight the pro-inflammatory cytokines' involvement in FD pathogenesis along with extracellular matrix remodeling. The study shows a tissue-wide metabolic remodeling connection to plasma proteomics in FD. These results will facilitate further studies to understand the molecular mechanisms in FD to pave the way for better diagnostics and therapeutics.

Single-cell transcriptome dynamics of the autotaxin-lysophosphatidic acid axis during muscle regeneration reveal proliferative effects in mesenchymal fibro-adipogenic progenitors.

Lysophosphatidic acid is a growth factor-like bioactive phospholipid recognising LPA receptors and mediating signalling pathways that regulate embryonic development, wound healing, carcinogenesis, and fibrosis, via effects on cell migration, proliferation and differentiation. Extracellular LPA is generated from lysophospholipids by the secreted hydrolase-ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2; also, AUTOTAXIN/ATX) and metabolised by different membrane-bound phospholipid phosphatases (PLPPs). Here, we use public bulk and single-cell RNA sequencing datasets to explore the expression of Lpar 1-6, Enpp2, and Plpp genes under skeletal muscle homeostasis and regeneration conditions. We show that the skeletal muscle system dynamically expresses the Enpp2-Lpar-Plpp gene axis, with Lpar1 being the highest expressed member among LPARs. Lpar1 was expressed by mesenchymal fibro-adipogenic progenitors and tenocytes, whereas FAPs mainly expressed Enpp2. Clustering of FAPs identified populations representing distinct cell states with robust Lpar1 and Enpp2 transcriptome signatures in homeostatic cells expressing higher levels of markers Dpp4 and Hsd11b1. However, tissue injury induced transient repression of Lpar genes and Enpp2. The role of LPA in modulating the fate and differentiation of tissue-resident FAPs has not yet been explored. Ex vivo, LPAR1/3 and ENPP2 inhibition significantly decreased the cell-cycle activity of FAPs and impaired fibro-adipogenic differentiation, implicating LPA signalling in the modulation of the proliferative and differentiative fate of FAPs. Together, our results demonstrate the importance of the ENPP2-LPAR-PLPP axis in different muscle cell types and FAP lineage populations in homeostasis and injury, paving the way for further research on the role of this signalling pathway in skeletal muscle homeostasis and regeneration, and that of other organs and tissues, in vivo.

Novel Gene Signatures Promote Epithelial-Mesenchymal Transition (EMT) in Glucose Deprivation-Based Microenvironment to Predict Recurrence-Free Survival in Hepatocellular Carcinoma.

Current research studies have suggested that glucose deprivation (GD)-based tumor microenvironment (TME) can promote epithelial-mesenchymal transition (EMT) of tumor cells, leading to tumor invasion and metastasis. However, no one has yet studied detailedly the synthetic studies that include GD features in TME with EMT status. In our research, we comprehensively developed and validated a robust signature regarding GD and EMT status to provide prognostic value for patients with liver cancer. GD and EMT status were estimated with transcriptomic profiles based on WGCNA and t-SNE algorithms. Two cohorts of training (TCGA_LIHC) and validation (GSE76427) datasets were analyzed with the Cox regression and logistic regression analyses. We identified a 2-mRNA signature to establish a GD-EMT-based gene risk model for the prediction of HCC relapse. Patients with significant GD-EMT status were divided into two subgroups: GDlow/EMTlow and GDhigh/EMThigh, with the latter having significantly worse recurrence-free survival (P < 0.01). We employed the least absolute shrinkage and selection operator (LASSO) technique as a method for HNF4A and SLC2A4 filtering and constructing a risk score for risk stratification. In the multivariate analysis, this risk score predicted recurrence-free survival (RFS) in both the discovery and validation cohorts and remained valid in patients stratified by TNM stage and age at diagnosis. The nomogram that combines risk score and TNM stage as well as age produces improved performance and net benefits in the analysis of calibration and decision curves in training and validation groups. The GD-EMT-based signature predictive model may provide a prognosis classifier for HCC patients with a high risk of postoperative recurrence to decrease the relapse rate.

Entropy production of multivariate Ornstein-Uhlenbeck processes correlates with consciousness levels in the human brain.

Consciousness is supported by complex patterns of brain activity which are indicative of irreversible nonequilibrium dynamics. While the framework of stochastic thermodynamics has facilitated the understanding of physical systems of this kind, its application to infer the level of consciousness from empirical data remains elusive. We faced this challenge by calculating entropy production in a multivariate Ornstein-Uhlenbeck process fitted to Functional magnetic resonance imaging brain activity recordings. To test this approach, we focused on the transition from wakefulness to deep sleep, revealing a monotonous relationship between entropy production and the level of consciousness. Our results constitute robust signatures of consciousness while also advancing our understanding of the link between consciousness and complexity from the fundamental perspective of statistical physics.

Signatures of contextual interference in implicit sensorimotor adaptation.

Contextual interference refers to the phenomenon whereby a blocked practice schedule results in faster acquisition but poorer retention of new motor skills compared to a random practice schedule. While contextual interference has been observed under a broad range of tasks, it remains unclear if this effect generalizes to the implicit and automatic recalibration of an overlearned motor skill. To address this question, we compared blocked and random practice schedules in a visuomotor rotation task that isolates implicit adaptation. In experiment 1, we found robust signatures of contextual interference in implicit adaptation: compared to participants tested under a blocked training schedule, participants tested under a random training schedule exhibited a reduced rate of learning during the training phase but better retention during a subsequent no-feedback assessment phase. In experiment 2, we again observed an advantage in retention following random practice and showed that this result was not due to a change in context between the training and assessment phases (e.g. a blocked training schedule followed by a random assessment schedule). Taken together, these results indicate that contextual interference is not limited to the acquisition of new motor skills but also applies to the implicit adaptation of established motor skills.

An immune genes signature for predicting mortality in sepsis patients.

A growing body of evidence indicates that the immune system plays a central role in sepsis. By analyzing immune genes, we sought to establish a robust gene signature and develop a nomogram that could predict mortality in patients with sepsis. Herein, data were extracted from the Gene Expression Omnibus and Biological Information Database of Sepsis (BIDOS) databases. We enrolled 479 participants with complete survival data using the GSE65682 dataset, and grouped them randomly into training (n = 240) and internal validation (n = 239) sets based on a 1:1 proportion. GSE95233 was set as the external validation dataset (n=51). We validated the expression and prognostic value of the immune genes using the BIDOS database. We established a prognostic immune genes signature (including ADRB2, CTSG, CX3CR1, CXCR6, IL4R, LTB, and TMSB10) via LASSO and Cox regression analyses in the training set. Based on the training and validation sets, the Receiver Operating Characteristic curves and Kaplan-Meier analysis revealed that the immune risk signature has good predictive power in predicting sepsis mortality risk. The external validation cases also showed that mortality rates in the high-risk group were higher than those in the low-risk group. Subsequently, a nomogram integrating the combined immune risk score and other clinical features was developed. Finally, a web-based calculator was built to facilitate a convenient clinical application of the nomogram. In summary, the signature based on the immune gene holds potential as a novel prognostic predictor for sepsis.

Combining multiple cell death pathway-related risk scores to develop neuroblastoma cell death signature.

Cell death plays an important role in tumourigenesis and progression; nevertheless, the clinical significance of cell death-related genes in neuroblastoma remains incompletely understood. We separately constructed the corresponding risk scores for each of the eight cell death pathways separately and assessed their predictive performance. Through Cox regression analysis, these eight risk scores were integrated to obtain final cell death risk scores (CDRS). We evaluated the predictive performance of CDRS in multiple datasets and compared its accuracy with the clinical characteristics of patients and some existing prognostic models for neuroblastoma. We then explored the differences in immune infiltration between the high and low CDRS groups, and the significance of CDRS on EFS and disease progression. All eight risk scores have high predictive accuracy, with the Immunogenic-RS being the most accurate and the cuproptosis-RS the least accurate. Model genes are mainly enriched in a variety of cancer-related pathways and are closely related to the clinical characteristics. CDRS showed superior and robust predictive performance in multiple datasets and was more accurate than the clinical characteristics of patients and some existing prognostic models for neuroblastoma. High CDRS group featured distinct immune cold tumor profiles and may have poorer immune checkpoint inhibitor efficacy. CDRS had significance in predicting EFS and disease progression. We integrated risk scores associated with multiple cell death pathways to develop a high-performing and robust neuroblastoma signature. CDRS was a promising tool that may help with risk assessment and prediction of overall prognosis, and thus improve clinical outcomes.

Quantum oscillations as a robust fingerprint of chiral anomaly in nonlinear response in Weyl semimetals

In view of searching the signature of the celebrated chiral anomaly (CA) in Weyl semimetals (WSMs) in ongoing experiments, quantum oscillation in linear response regime has been considered as an important signature in the magneto transports in WSMs, due to its unique relation to CA. Investigating the nonlinear planar effects (NPEs) starting from the semiclassical regime to the ultra-quantum limit within the framework of Boltzmann transport theory incorporating Landau quantization, we here propose the quantum oscillations in NPEs can serve as a robust signature of CA in WSMs. By obtaining analytical expressions, we show that the quantum oscillations of the nonlinear effects exhibit two different period scales in 1/B (B is the magnetic field) compared to the linear responses where only one period scale exists. We find that these quantum oscillations in NPEs are attributed to the deviation of chiral chemical potential (CCP), which is proportional to the finite band tilt as well as transverse electric field and therefore, directly linked to CA in WSMs. In addition, we also show that the CA-induced nonlinear magneto conductivity is linear and independent in the magnetic field in the semiclassical and ultraquantum regimes, respectively. We conclude that the proposed behaviors of NPEs in different regimes uniquely signify the existence of CA and therefore, can serve as a probe of identifying CA in WSMs in experiments.

A composite score based on immune-related gene prognostic index and m6A risk score of head and neck squamous cell carcinoma.

Background: Immunotherapy has been demonstrated favorable in head and neck squamous cell carcinoma (HNSCC). Studies indicated that immune-related gene prognostic index (IRGPI) was a robust signature, and N6-methyladenosine (m6A) methylation had a significant impact on the tumor immune microenvironment (TIME) and immunotherapy of head and neck squamous cell carcinoma. Thus, combining indicated that immune-related gene prognostic index with m6A status should offer a better predictive power for immune responses. Methods: Head and neck squamous cell carcinoma samples from the cancer genome atlas (TCGA, n = 498) and gene expression omnibus database (GSE65858, n = 270) were used in this study. Cox regression analysis was used to construct the indicated that immune-related gene prognostic index through immune-related hub genes which were identified by weighted gene co-expression network analysis (WGCNA). The m6A risk score was constructed by least absolute shrinkage and selection operator (LASSO) regression analysis. Principal component analysis was used to construct a composite score, and systematically correlate subgroups according to tumor immune microenvironment cell-infiltrating characteristics. Results: A composite score was determined based on indicated that immune-related gene prognostic index and m6A risk score. Head and neck squamous cell carcinoma patients in the cancer genome atlas were divided into four subgroups: A (IRGPI-High&m6A-risk-High, n = 127), B (IRGPI-High&m6A-risk-Low, n = 99), C (IRGPI-Low&m6A-risk-High, n = 99), and D (IRGPI-Low&m6A-risk-Low, n = 128), and overall survival (OS) was significantly different between subgroups (p < 0.001). The characteristics of tumor immune microenvironment cell infiltration in the four subgroups were significantly different in subgroups (p < 0.05). The receiver operating characteristic (ROC) curves show the predictive value of composite score for overall survival was superior to other scores. Conclusion: The composite score is a promising prognostic signature which might distinguish immune and molecular characteristics, predict prognosis, and guide more effective immunotherapeutic strategies for head and neck squamous cell carcinoma.

DNA methylation signature classification of rare disorders using publicly available methylation data.

Disease-specific DNA methylation patterns (DNAm signatures) have been established for an increasing number of genetic disorders and represent a valuable tool for classification of genetic variants of uncertain significance (VUS). Sample size and batch effects are critical issues for establishing DNAm signatures, but their impact on the sensitivity and specificity of an already established DNAm signature has not previously been tested. Here, we assessed whether publicly available DNAm data can be employed to generate a binary machine learning classifier for VUS classification, and used variants in KMT2D, the gene associated with Kabuki syndrome, together with an existing DNAm signature as proof-of-concept. Using publicly available methylation data for training, a classifier for KMT2D variants was generated, and individuals with molecularly confirmed Kabuki syndrome and unaffected individuals could be correctly classified. The present study documents the clinical utility of a robust DNAm signature even for few affected individuals, and most importantly, underlines the importance of data sharing for improved diagnosis of rare genetic disorders.