Distinct Subtypes Research Articles

Molecular features and prognostic factors of locally advanced microsatellite instability-high gastric cancer.

Microsatellite instability-high (MSI-H) tumors are distinct molecular subtypes in gastric cancer. However, a few studies have comprehensively reported the molecular features of MSI-H tumors and their prognostic factors in locally advanced gastric cancer. This study aimed to clarify the molecular features and prognostic factors of locally advanced MSI-H gastric cancer. This study included 499 patients with locally advanced gastric cancer who underwent radical gastrectomy. We evaluated the MSI status and compared with previously published whole-exome sequencing, panel sequencing, and gene expression profiling data. Clinicopathological characteristics and molecular profiles were compared between patients with MSI-H and microsatellite stable (MSS) gastric cancer. A subgroup analysis of survival was performed in patients with MSI-H gastric cancer. MSI-H tumors were detected in 79 of 499 patients (15.8%). MSI-H tumors were associated with an increased tumor mutational burden, MLH1 downregulation, CD274 (PD-L1) upregulation, and enrichment of cell cycle pathways. Among patients with MSI-H gastric cancer, the disease-specific survival (DSS) tended to be better in the surgery plus tegafur, gimeracil, and oteracil potassium (S-1) adjuvant chemotherapy group than in the surgery alone group, especially for stage III patients. Furthermore, DSS was better in the T cell-inflamed gene expression signature-high group, and it tended to be worse in the non-solid type poorly differentiated adenocarcinoma group. The molecular features and prognostic factors of locally advanced MSI-H gastric cancer were clarified. S-1 adjuvant chemotherapy appears to be beneficial, and the T cell-inflamed gene expression signature and histopathological type are prognostic factors in MSI-H tumors.

A Bispecific Monoclonal Antibody Targeting Psl and PcrV Enhances Neutrophil-Mediated Killing of Pseudomonas aeruginosa in Patients with Bronchiectasis.

Rationale: Pseudomonas aeruginosa infection is associated with worse outcomes in bronchiectasis. Impaired neutrophil antimicrobial responses contribute to bacterial persistence. Gremubamab is a bivalent, bispecific monoclonal antibody targeting Psl exopolysaccharide and the type 3 secretion system component PcrV. Objectives: This study evaluated the efficacy of gremubamab to enhance killing of P. aeruginosa by neutrophils from patients with bronchiectasis and to prevent P. aeruginosa-associated cytotoxicity. Methods: P. aeruginosa isolates from a global bronchiectasis cohort (n = 100) underwent whole-genome sequencing to determine target prevalence. Functional activity of gremubamab against selected isolates was tested invitro and invivo. Patients with bronchiectasis (n = 11) and control subjects (n = 10) were enrolled, and the effect of gremubamab in peripheral blood neutrophil opsonophagocytic killing (OPK) assays against P. aeruginosa was evaluated. Serum antibody titers to Psl and PcrV were determined (n = 30; 19 chronic P. aeruginosa infection, 11 no known P. aeruginosa infection), as was the effect of gremubamab treatment in OPK and anti-cytotoxic activity assays. Measurements and Main Results: Psl and PcrV were conserved in isolates from chronically infected patients with bronchiectasis. Seventy-three of 100 isolates had a full psl locus, and 99 of 100 contained the pcrV gene, with 20 distinct full-length PcrV protein subtypes identified. PcrV subtypes were successfully bound by gremubamab and the monoclonal antibody-mediated potent protective activity against tested isolates. Gremubamab increased bronchiectasis patient neutrophil-mediated OPK (+34.6 ± 8.1%) and phagocytosis (+70.0 ± 48.8%), similar to effects observed in neutrophils from control subjects (OPK, +30.1 ± 7.6%). No evidence of competition between gremubamab and endogenous antibodies was found, with protection against P. aeruginosa-induced cytotoxicity and enhanced OPK demonstrated with and without addition of patient serum. Conclusions: Gremubamab enhanced bronchiectasis patient neutrophil phagocytosis and killing of P. aeruginosa and reduced virulence.

A transparent machine learning algorithm uncovers HbA1c patterns associated with therapeutic inertia in patients with type 2 diabetes and failure of metformin monotherapy

AimsThis study aimed to identify and categorize the determinants influencing the intensification of therapy in Type 2 Diabetes (T2D) patients with suboptimal blood glucose control despite metformin monotherapy. MethodsEmploying the Logic Learning Machine (LLM), an advanced artificial intelligence system, we scrutinized electronic health records of 1.5 million patients treated in 271 diabetes clinics affiliated with the Italian Association of Medical Diabetologists from 2005 to 2019. Inclusion criteria comprised patients on metformin monotherapy with two consecutive mean HbA1c levels exceeding 7.0%. The cohort was divided into “inertia-NO” (20,067 patients with prompt intensification) and “inertia-YES” (13,029 patients without timely intensification). ResultsThe LLM model demonstrated robust discriminatory ability among the two groups (ROC-AUC = 0.81, accuracy = 0.71, precision = 0.80, recall = 0.71, F1 score = 0.75). The main novelty of our results is indeed the identification of two main distinct subtypes of therapeutic inertia. The first exhibited a gradual but steady HbA1c increase, while the second featured a moderate, non-uniform rise with substantial fluctuations. ConclusionsOur analysis sheds light on the significant impact of HbA1c levels over time on therapeutic inertia in patients with T2D, emphasizing the importance of early intervention in the presence of specific HbA1c patterns.

Comprehensive Analysis of Craniopharyngioma: Epidemiology, Clinical Characteristics, Management Strategies, and Role of Radiotherapy.

Craniopharyngiomas pose challenges in diagnosis and management due to their rare occurrence and diverse clinical manifestations. This study aimed to provide a comprehensive analysis of cranio-pharyngioma, including its epidemiological trends, clinical presentations, radiological characteristics, surgical interventions, and the role of radiotherapy. A retrospective observational study was conducted on 23 patients diagnosed with craniopharyngioma at our hospital from August 2017 to July 2019. Data regarding demographics, clinical presentation, radiological findings, surgical interventions, and adjuvant therapies were collected and analyzed. Craniopharyngiomas exhibited a bimodal age distribution, with peaks in childhood and late adulthood. Clinical presentations varied between pediatric and adult patients, with headache and nausea/vomiting predominant in children, and visual disturbances and hypogonadism more common in adults. Radiological imaging revealed predominantly suprasellar localization and varying tumor consistency. Surgical resection was the primary treatment modality, with post-operative complications including diabetes insipidus and cerebrospinal fluid leak. Histological analysis showed distinct subtypes, with the adamantinomatous subtype predominant in children and the papillary subtype in adults. Adjuvant radiotherapy was administered in cases of incomplete resection or tumor recurrence. This study provides comprehensive insights into the epidemiology, clinical characteristics, radiological features, surgical interventions, and role of radiotherapy in craniopharyngioma management. Understanding these aspects is crucial for tailoring optimal treatment strategies and improving patient outcomes in this complex clinical scenario.

Dual-trajectory of TyG levels and lifestyle scores and their associations with ischemic stroke in a non-diabetic population: a cohort study

BackgroundThe Triglyceride-glucose (TyG) index, a surrogate marker of insulin resistance, has been implicated in the risk of ischemic stroke. However, the interplay between TyG levels, lifestyle factors, and their collective impact on stroke risk in non-diabetic populations remains inadequately explored. This study aims to evaluate the association of ischemic stroke with the joint development of the TyG index and lifestyle in the non-diabetic population.MethodsIn this prospective cohort study, data was collected across three consecutive biennial surveys of the Kailuan Study from 2006 to 2011. The dual-trajectory model was used to determine the temporal development of TyG levels and lifestyle scores. Statistical analysis involved Cox regression models to evaluate the association between TyG-lifestyle trajectories and ischemic stroke risk, adjusting for potential confounders.ResultsA total of 44,403 participants were included, with five distinct TyG levels and lifestyle scores trajectory subtypes identified. In the multivariable-adjusted analyses, significant differences in ischemic stroke risk among the trajectory subtypes. Group 5, characterized by the highest TyG levels and moderate lifestyle scores, exhibited the greatest ischemic stroke risk (HR = 1.81, 95% CI: 1.51–2.18), while group 4, with moderate TyG levels and higher lifestyle scores, demonstrated the lowest risk (HR = 1.19, 95% CI: 1.04–1.37), compared with group 3. Participants with elevated TyG levels were at an increased risk of ischemic stroke in cases of pronounced insulin resistance, even with a healthy lifestyle.ConclusionsThis study reveals the significant associations between the identified TyG and lifestyle trajectories and the stratification of ischemic stroke risk among non-diabetics. The TyG index is a valuable indicator for assessing insulin resistance. However, the potential benefits of lifestyle changes for those with significantly high TyG levels need to be clarified by more research to develop more effective stroke prevention strategies.

Clinical subtypes of older adults starting long-term care in Japan and their association with prognoses: a data-driven cluster analysis

We aimed to identify the clinical subtypes in individuals starting long-term care in Japan and examined their association with prognoses. Using linked medical insurance claims data and survey data for care-need certification in a large city, we identified participants who started long-term care. Grouping them based on 22 diseases recorded in the past 6 months using fuzzy c-means clustering, we examined the longitudinal association between clusters and death or care-need level deterioration within 2 years. We analyzed 4,648 participants (median age 83 [interquartile range 78–88] years, female 60.4%) between October 2014 and March 2019 and categorized them into (i) musculoskeletal and sensory, (ii) cardiac, (iii) neurological, (iv) respiratory and cancer, (v) insulin-dependent diabetes, and (vi) unspecified subtypes. The results of clustering were replicated in another city. Compared with the musculoskeletal and sensory subtype, the adjusted hazard ratio (95% confidence interval) for death was 1.22 (1.05–1.42), 1.81 (1.54–2.13), and 1.21 (1.00–1.46) for the cardiac, respiratory and cancer, and insulin-dependent diabetes subtypes, respectively. The care-need levels more likely worsened in the cardiac, respiratory and cancer, and unspecified subtypes than in the musculoskeletal and sensory subtype. In conclusion, distinct clinical subtypes exist among individuals initiating long-term care.

Cell State Transition Models Stratify Breast Cancer Cell Phenotypes and Reveal New Therapeutic Targets.

Understanding signaling patterns of transformation and controlling cell phenotypes is a challenge of current biology. Here we applied a cell State Transition Assessment and Regulation (cSTAR) approach to a perturbation dataset of single cell phosphoproteomic patterns of multiple breast cancer (BC) and normal breast tissue-derived cell lines. Following a separation of luminal, basal, and normal cell states, we identified signaling nodes within core control networks, delineated causal connections, and determined the primary drivers underlying oncogenic transformation and transitions across distinct BC subtypes. Whereas cell lines within the same BC subtype have different mutational and expression profiles, the architecture of the core network was similar for all luminal BC cells, and mTOR was a main oncogenic driver. In contrast, core networks of basal BC were heterogeneous and segregated into roughly four major subclasses with distinct oncogenic and BC subtype drivers. Likewise, normal breast tissue cells were separated into two different subclasses. Based on the data and quantified network topologies, we derived mechanistic cSTAR models that serve as digital cell twins and allow the deliberate control of cell movements within a Waddington landscape across different cell states. These cSTAR models suggested strategies of normalizing phosphorylation networks of BC cell lines using small molecule inhibitors.

Assessing the functional roles of coevolving PHD finger residues.

Although in silico folding based on coevolving residue constraints in the deep-learning era has transformed protein structure prediction, the contributions of coevolving residues to protein folding, stability, and other functions in physical contexts remain to be clarified and experimentally validated. Herein, the PHD finger module, a well-known histone reader with distinct subtypes containing subtype-specific coevolving residues, was used as a model to experimentally assess the contributions of coevolving residues and to clarify their specific roles. The results of the assessment, including proteolysis and thermal unfolding of wildtype and mutant proteins, suggested that coevolving residues have varying contributions, despite their large in silico constraints. Residue positions with large constraints were found to contribute to stability in one subtype but not others. Computational sequence design and generative model-based energy estimates of individual structures were also implemented to complement the experimental assessment. Sequence design and energy estimates distinguish coevolving residues that contribute to folding from those that do not. The results of proteolytic analysis of mutations at positions contributing to folding were consistent with those suggested by sequence design and energy estimation. Thus, we report a comprehensive assessment of the contributions of coevolving residues, as well as a strategy based on a combination of approaches that should enable detailed understanding of the residue contributions in other large protein families.

Astrocytes Modulate a Specific Paraventricular Thalamus→Prefrontal Cortex Projection to Enhance Consciousness Recovery from Anesthesia.

Current anesthetic theory is mostly based on neurons and/or neuronal circuits. A role for astrocytes also has been shown in promoting recovery from volatile anesthesia, while the exact modulatory mechanism and/or the molecular target in astrocytes is still unknown. In this study by animal models in male mice and electrophysiological recordings in vivo and in vitro, we found that activating astrocytes of the paraventricular thalamus (PVT) and/or knocking down PVT astrocytic Kir4.1 promoted the consciousness recovery from sevoflurane anesthesia. Single-cell RNA sequencing of the PVT reveals two distinct cellular subtypes of glutamatergic neurons: PVT GRM and PVT ChAT neurons. Patch-clamp recording results proved astrocytic Kir4.1-mediated modulation of sevoflurane on the PVT mainly worked on PVT ChAT neurons, which projected mainly to the mPFC. In summary, our findings support the novel conception that there is a specific PVT→prefrontal cortex projection involved in consciousness recovery from sevoflurane anesthesia, which is mediated by the inhibition of sevoflurane on PVT astrocytic Kir4.1 conductance.

Identification of immunotherapy-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognostic signature in gastric carcinoma.

Recent advances in immunotherapy have elicited a considerable amount of attention as viable therapeutic options for several cancer types, the present study aimed to explore the immunotherapy-related genes (IRGs) and develop a prognostic risk signature in gastric carcinoma (GC) based on these genes. IRGs were identified by comparing immunotherapy responders and non-responders in GC. Then, GC patients were divided into distinct subtypes by unsupervised clustering method based on IRGs, and the differences in immune characteristics and prognostic stratification between these subtypes were analyzed. An immunotherapy-related risk score (IRRS) signature was developed and validated for risk classification and prognosis prediction based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts. Besides, the predictive ability of the IRRS in immunotherapy response was also determined. A total of 63 IRGs were identified, and 371 GC patients were stratified into two molecular subgroups with significantly different prognosis and immune characteristics. Then, an IRRS signature comprised of three IRGs (CENP8, NRP1, and SERPINE1) was constructed to predict the prognosis of GC patients in TCGA cohort. Importantly, external validation in multiple GEO cohorts further confirmed the universal applicability of the IRRS in distinct populations. Furthermore, we found that the IRRS was significantly correlated with patient's responsiveness to immunotherapy, GC patients with low IRRS are more likely to benefit from existing immunotherapy. The risk score could serve as a robust prognostic biomarker, provide therapeutic benefits for immunotherapy and may be helpful for clinical decision making in GC patients.

Deciphering the molecular landscape of rheumatoid arthritis offers new insights into the stratified treatment for the condition.

For Rheumatoid Arthritis (RA), a long-term chronic illness, it is essential to identify and describe patient subtypes with comparable goal status and molecular biomarkers. This study aims to develop and validate a new subtyping scheme that integrates genome-scale transcriptomic profiles of RA peripheral blood genes, providing a fresh perspective for stratified treatments. We utilized independent microarray datasets of RA peripheral blood mononuclear cells (PBMCs). Up-regulated differentially expressed genes (DEGs) were subjected to functional enrichment analysis. Unsupervised cluster analysis was then employed to identify RA peripheral blood gene expression-driven subtypes. We defined three distinct clustering subtypes based on the identified 404 up-regulated DEGs. Subtype A, named NE-driving, was enriched in pathways related to neutrophil activation and responses to bacteria. Subtype B, termed interferon-driving (IFN-driving), exhibited abundant B cells and showed increased expression of transcripts involved in IFN signaling and defense responses to viruses. In Subtype C, an enrichment of CD8+ T-cells was found, ultimately defining it as CD8+ T-cells-driving. The RA subtyping scheme was validated using the XGBoost machine learning algorithm. We also evaluated the therapeutic outcomes of biological disease-modifying anti-rheumatic drugs. The findings provide valuable insights for deep stratification, enabling the design of molecular diagnosis and serving as a reference for stratified therapy in RA patients in the future.

The Role and Clinical Relevance of Glycolysis-Associated Genes on Immune Infiltration in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) poses a significant challenge with dismal survival rates, necessitating a deeper understanding of its molecular mechanisms and the development of improved therapies. Metabolic reprogramming, particularly heightened glycolysis, plays a crucial role in HCC progression. Glycolysis-associated genes (GAGs) emerge as key players in HCC pathogenesis, influencing the tumor microenvironment and immune responses. This study aims to investigate the intricate interplay between GAGs and the immune landscape within HCC, offering valuable insights into potential prognostic markers and therapeutic targets to enhance treatment strategies and patient outcomes. Through the exploration of GAGs, we have identified two distinct molecular glycolytic subtypes in HCC patients, each exhibiting significant differences in both the immune microenvironment and prognosis. A risk model comprising five key GAGs was formulated and subsequently evaluated for their predictive accuracy. Our findings underscore the diverse tumor microenvironment and immune responses associated with the varying glycolytic subtypes observed in HCC. The identified key GAGs hold promise as prognostic indicators for evaluating HCC risk levels, predicting patient outcomes, and guiding clinical treatment decisions, particularly in the context of anticipating responses to immunotherapy drugs.

Prediction of immunotherapy response of bladder cancer with a pyroptosis-related signature indicating tumor immune microenvironment.

Although prognostic models based on pyroptosis-related genes (PRGs) have been constructed in bladder cancer (BLCA), the comprehensive impact of these genes on tumor microenvironment (TME) and immunotherapeutic response has yet to be investigated. Based on expression profiles of 52 PRGs, we utilized the unsupervised clustering algorithm to identify PRGs subtypes and ssGSEA to quantify immune cells and hallmark pathways. Moreover, we screened feature genes of distinct PRGs subtypes and validated the associations with immune infiltrations in tissue using the multiplex immunofluorescence. Univariate, LASSO, and multivariate Cox regression analyses were employed to construct the scoring scheme. Four PRGs clusters were identified, samples in cluster C1 were infiltrated with more immune cells than those in others, implying a favorable response to immunotherapy. While the cluster C2, which shows an extremely low level of most immune cells, do not respond to immunotherapy. CXCL9/CXCL10 and SPINK1/DHSR2 were identified as feature genes of cluster C1 and C2, and the specimen with high CXCL9/CXCL10 was characterized by more CD8 + T cells, macrophages and less Tregs. Based on differentially expressed genes (DEGs) among PRGs subtypes, a predictive model (termed as PRGs score) including five genes (CACNA1D, PTK2B, APOL6, CDK6, ANXA2) was built. Survival probability of patients with low-PRGs score was significantly higher than those with high-PRGs score. Moreover, patients with low-PRGs score were more likely to benefit from anti-PD1/PD-L1 regimens. PRGs are closely associated with TME and oncogenic pathways. PRGs score is a promising indicator for predicting clinical outcome and immunotherapy response.

Deciphering Distinct Genetic Risk Factors for FTLD-TDP Pathological Subtypes via Whole-Genome Sequencing.

Frontotemporal lobar degeneration with neuronal inclusions of the TAR DNA-binding protein 43 (FTLD-TDP) is a fatal neurodegenerative disorder with only a limited number of risk loci identified. We report our comprehensive genome-wide association study as part of the International FTLD-TDP Whole-Genome Sequencing Consortium, including 985 cases and 3,153 controls, and meta-analysis with the Dementia-seq cohort, compiled from 26 institutions/brain banks in the United States, Europe and Australia. We confirm UNC13A as the strongest overall FTLD-TDP risk factor and identify TNIP1 as a novel FTLD-TDP risk factor. In subgroup analyses, we further identify for the first time genome-wide significant loci specific to each of the three main FTLD-TDP pathological subtypes (A, B and C), as well as enrichment of risk loci in distinct tissues, brain regions, and neuronal subtypes, suggesting distinct disease aetiologies in each of the subtypes. Rare variant analysis confirmed TBK1 and identified VIPR1 , RBPJL , and L3MBTL1 as novel subtype specific FTLD-TDP risk genes, further highlighting the role of innate and adaptive immunity and notch signalling pathway in FTLD-TDP, with potential diagnostic and novel therapeutic implications.

SEDphone: Spatial encoding of centrifugal microfluidic disc integrated smartphone-controlled platform via RT/LAMP-CRISPR/Cas12a system for influenza virus subtypes detection

Accurate and rapid identification of multiple pathogens simultaneously is paramount for global public health, including the detection of different influenza virus subtypes. Recently, the development of CRISPR/Cas technology and its combination with isothermal amplification technology has presented promising opportunities for exploiting point-of-care, highly sensitive and specific detection methods. However, there have been several challenges in terms of multiplex detection and compatibility with isothermal amplification technology, especially loop-mediated isothermal amplification (LAMP). In this study, we introduced a multiplex detection platform, named spatial encoding of centrifugal microfluidic disc integrated smartphone-controlled platform (SEDphone), which was based on CRISPR/Cas12a and RT/LAMP technology. Five distinct influenza virus subtypes could be simultaneously identified: influenza A virus H1N1, H3N2, H5N1, H7N9, and influenza B virus. The assay limit of detection reached 10 copies/μL within a 45-minute timeframe. Furthermore, the clinical efficacy was validated by detecting 22 positive and 16 negative clinical samples. We anticipate that this simple yet efficient platform holds significant potential as a versatile multiplex pathogen detection in both research and diagnostic settings.

Three autism subtypes based on single-subject gray matter network revealed by semi-supervised machine learning.

Autism spectrum disorder (ASD) is a heterogeneous, early-onset neurodevelopmental condition characterized by persistent impairments in social interaction and communication. This study aims to delineate ASD subtypes based on individual gray matter brain networks and provide new insights from a graph theory perspective. In this study, we extracted and normalized single-subject gray matter networks and calculated each network's topological properties. The heterogeneity through discriminative analysis (HYDRA) method was utilized to subtype all patients based on network properties. Next, we explored the differences among ASD subtypes in terms of network properties and clinical measures. Our investigation identified three distinct ASD subtypes. In the case-control study, these subtypes exhibited significant differences, particularly in the precentral gyrus, lingual gyrus, and middle frontal gyrus. In the case analysis, significant differences in global and nodal properties were observed between any two subtypes. Clinically, subtype 1 showed lower VIQ and PIQ compared to subtype 3, but exhibited higher scores in ADOS-Communication and ADOS-Total compared to subtype 2. The results highlight the distinct brain network properties and behaviors among different subtypes of male patients with ASD, providing valuable insights into the neural mechanisms underlying ASD heterogeneity.

Krause corpuscles are genital vibrotactile sensors for sexual behaviours

Krause corpuscles, which were discovered in the 1850s, are specialized sensory structures found within the genitalia and other mucocutaneous tissues1–4. The physiological properties and functions of Krause corpuscles have remained unclear since their discovery. Here we report the anatomical and physiological properties of Krause corpuscles of the mouse clitoris and penis and their roles in sexual behaviour. We observed a high density of Krause corpuscles in the clitoris compared with the penis. Using mouse genetic tools, we identified two distinct somatosensory neuron subtypes that innervate Krause corpuscles of both the clitoris and penis and project to a unique sensory terminal region of the spinal cord. In vivo electrophysiology and calcium imaging experiments showed that both Krause corpuscle afferent types are A-fibre rapid-adapting low-threshold mechanoreceptors, optimally tuned to dynamic, light-touch and mechanical vibrations (40–80 Hz) applied to the clitoris or penis. Functionally, selective optogenetic activation of Krause corpuscle afferent terminals evoked penile erection in male mice and vaginal contraction in female mice, while genetic ablation of Krause corpuscles impaired intromission and ejaculation of males and reduced sexual receptivity of females. Thus, Krause corpuscles of the clitoris and penis are highly sensitive mechanical vibration detectors that mediate sexually dimorphic mating behaviours.

High-resolution HLA sequencing and hypocretin receptor 2 autoantibodies in narcolepsy type 1 and type 2.

Narcolepsy is a sleep disorder caused by an apparent degeneration of orexin/hypocretin neurons in the lateral hypothalamic area and a subsequent decrease in orexin/hypocretin levels in the cerebrospinal fluid. Narcolepsy is classified into type 1 (NT1) and type 2 (NT2). While genetic associations in the human leukocyte antigen (HLA) region and candidate autoantibodies have been investigated in NT1 to imply an autoimmune origin, less is known about the pathogenesis in NT2. Twenty-six NT1 and 15 NT2 patients were included, together with control groups of 24 idiopathic hypersomnia (IH) patients and 778 general population participants. High-resolution sequencing was used to determine the alleles, the extended haplotypes, and the genotypes of HLA-DRB3, -DRB4, -DRB5, -DRB1, -DQA1, -DQB1, -DPA1, and -DPB1. Radiobinding assay was used to determine autoantibodies against hypocretin receptor 2 (anti-HCRTR2 autoantibodies). NT1 was associated with HLA-DRB5*01:01:01, -DRB1*15:01:01, -DQA1*01:02:01, -DQB1*06:02:01, -DRB5*01:01:01, -DRB1*15:01:01, -DQA1*01:02:01, -DQB1*06:02:01 (odds ratio [OR]: 9.15; p=8.31×10-4) and HLA-DRB5*01:01:01, -DRB1*15:01:01, -DQA1*01:02:01, -DQB1*06:02:01, -DRB4*01:03:01, -DRB1*04:01:01, -DQA1*03:02//03:03:01, -DQB1*03:01:01 (OR: 23.61; p=1.58×10-4) genotypes. Lower orexin/hypocretin levels were reported in the NT2 subgroup (n=5) that was associated with the extended HLA-DQB1*06:02:01 haplotype (p=.001). Anti-HCRTR2 autoantibody levels were not different between study groups (p=.8524). We confirmed the previous association of NT1 with HLA-DQB1*06:02:01 extended genotypes. A subgroup of NT2 patients with intermediate orexin/hypocretin levels and association with HLA-DQB1*06:02:01 was identified, indicating a possible overlap between the two distinct narcolepsy subtypes, NT1 and NT2. Low anti-HCRTR2 autoantibody levels suggest that these receptors might not function as autoimmune targets in either NT1 or NT2.

Elucidating the role of angiogenesis-related genes in colorectal cancer: a multi-omics analysis.

Angiogenesis plays a pivotal role in colorectal cancer (CRC), yet its underlying mechanisms demand further exploration. This study aimed to elucidate the significance of angiogenesis-related genes (ARGs) in CRC through comprehensive multi-omics analysis. CRC patients were categorized according to ARGs expression to form angiogenesis-related clusters (ARCs). We investigated the correlation between ARCs and patient survival, clinical features, consensus molecular subtypes (CMS), cancer stem cell (CSC) index, tumor microenvironment (TME), gene mutations, and response to immunotherapy. Utilizing three machine learning algorithms (LASSO, Xgboost, and Decision Tree), we screen key ARGs associated with ARCs, further validated in independent cohorts. A prognostic signature based on key ARGs was developed and analyzed at the scRNA-seq level. Validation of gene expression in external cohorts, clinical tissues, and blood samples was conducted via RT-PCR assay. Two distinct ARC subtypes were identified and were significantly associated with patient survival, clinical features, CMS, CSC index, and TME, but not with gene mutations. Four genes (S100A4, COL3A1, TIMP1, and APP) were identified as key ARCs, capable of distinguishing ARC subtypes. The prognostic signature based on these genes effectively stratified patients into high- or low-risk categories. scRNA-seq analysis showed that these genes were predominantly expressed in immune cells rather than in cancer cells. Validation in two external cohorts and through clinical samples confirmed significant expression differences between CRC and controls. This study identified two ARG subtypes in CRC and highlighted four key genes associated with these subtypes, offering new insights into personalized CRC treatment strategies.

Spatial Transcriptome-Wide Profiling of Small Cell Lung Cancer Reveals Intra-Tumoral Molecular and Subtype Heterogeneity.

Small cell lung cancer (SCLC) is a highly aggressive malignancy characterized by rapid growth and early metastasis and is susceptible to treatment resistance and recurrence. Understanding the intra-tumoral spatial heterogeneity in SCLC is crucial for improving patient outcomes and clinically relevant subtyping. In this study, a spatial whole transcriptome-wide analysis of 25 SCLC patients at sub-histological resolution using GeoMx Digital Spatial Profiling technology is performed. This analysis deciphered intra-tumoral multi-regional heterogeneity, characterized by distinct molecular profiles, biological functions, immune features, and molecular subtypes within spatially localized histological regions. Connections between different transcript-defined intra-tumoral phenotypes and their impact on patient survival and therapeutic response are also established. Finally, a gene signature, termed ITHtyper, based on the prevalence of intra-tumoral heterogeneity levels, which enables patient risk stratification from bulk RNA-seq profiles is identified. The prognostic value of ITHtyper is rigorously validated in independent multicenter patient cohorts. This study introduces a preliminary tumor-centric, regionally targeted spatial transcriptome resource that sheds light on previously unexplored intra-tumoral spatial heterogeneity in SCLC. These findings hold promise to improve tumor reclassification and facilitate the development of personalized treatments for SCLC patients.