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PubMed2026

A novel deep learning-driven framework for improving lncRNA comprehensive annotation with LncADeep 2.0.

MOTIVATION: Long non-coding RNAs (lncRNAs) have emerged as crucial players in diverse physiological and pathological processes, yet the biological mechanisms of the vast majority of lncRNAs remain elusive. To fill this gap, it is necessary to improve the accuracy of lncRNA identification and functional annotation. RESULTS: Here, we introduce LncADeep 2.0, an integrated deep learning framework designed to meet these needs. In the identification module, LncADeep 2.0 incorporated novel peptide features along with sequence and structural information, demonstrating superior performance over our previous LncADeep and other existing tools on both annotated transcripts from GENCODE and RNA-seq data. For functional annotation, LncADeep 2.0 leveraged lncRNA-centric interaction networks and gene ontology terms through the transfer learning strategy to achieve robust annotation performance with limited functional data. Compared to LncADeep, LncADeep 2.0 could accurately elucidate the general functions of given lncRNA sequences, predict tissue- or cell-type-specific functions from bulk and single-cell RNA-seq data, and establish connections between tumor-associated lncRNAs and genomic markers. Overall, LncADeep 2.0 stands out as an efficient and reliable tool for lncRNA identification and functional annotation across a wide spectrum of biological processes. AVAILABILITY AND IMPLEMENTATION: LncADeep 2.0 is available for use at https://github.com/Jefferson-Chou/LncADeep2 and https://doi.org/10.5281/zenodo.17164767.

RNA, Long Noncoding↗

PubMed2026

Loss of tumor-infiltrating lymphocytes and poor response to immunotherapy in IDH GOF mutant melanoma.

Recent innovations in melanoma treatment with immune checkpoint blockade (ICB) have improved overall outcomes for patients; however, over 50% of patients still develop resistance to treatment. These patients either have intrinsic resistance and never respond to therapy or develop acquired resistance months or years into treatment. The mechanisms underlying ICB resistance remain poorly understood. Our data show that patients with isocitrate dehydrogenase gain-of-function (IDH GOF) mutant melanoma have a worse response to anti-PD1 immunotherapy. IDH mutations have been found to be oncogenic and associated with differential methylation in multiple cancers but are not yet characterized in human melanoma. Here, we investigate the clinical, immune, and transcriptional phenotypes of IDH GOF melanomas through analyses of clinical response, single-cell RNA-seq, bulk RNA-seq, and DNA methylation data. Single-cell data analysis showed decreased immune infiltrate and activity in the IDH GOF tumors. Bulk sequencing data demonstrated the association among IDH mutation, immune exclusion, and disruptions in global DNA methylation. The melanoma-derived genomic data presented support previously described resistance mechanisms of IDH mutation in other cancer types and is the first demonstration to our knowledge of the role of IDH GOF in the human melanoma tumor microenvironment.

Humans↗

PubMed2025

Twisted Sister1: an agravitropic mutant of bread wheat (Triticum aestivum) with altered root and shoot architectures.

We identified a mutant of hexaploid wheat (Triticum aestivum) with impaired responses to gravity. The mutant, named Twisted Sister1 (TS1), had agravitropic roots that were often twisted along with altered shoot phenotypes. Roots of TS1 were insensitive to externally applied auxin, with the genetics and physiology suggestive of a mutated AUX/IAA transcription factor gene. Hexaploid wheat possesses over 80 AUX/IAA genes, and sequence information did not identify an obvious candidate. Bulked segregant analysis of an F2 population mapped the mutation to chromosome 5A, and subsequent mapping located the mutation to a 41 Mbp region. RNA-seq identified the TraesCS5A03G0149800 gene encoding a TaAUX/IAA protein to be mutated in the highly conserved domain II motif. We confirmed TraesCS5A03G0149800 as underlying the mutant phenotype by generating transgenic Arabidopsis thaliana. Analysis of RNA-seq data suggested broad similarities between Arabidopsis and wheat for the role of AUX/IAA genes in gravity responses, although there were marked differences. Here we show that the sequenced wheat genome, along with previous knowledge of the physiology of gravity responses from other plant species, gene mapping, RNA-seq, and expression in Arabidopsis have enabled the cloning of a key wheat gene that defines plant architecture.

Triticum↗

PubMed2026

Malignant epithelial states drive immune dysfunction in ampulla of Vater carcinoma.

BACKGROUND: Ampulla of Vater (AoV) carcinoma is a rare malignancy arising at the junction of intestinal and pancreatobiliary epithelium. Its heterogeneous clinical behavior and histological diversity have hindered therapeutic advances, and the cellular basis of this heterogeneity remains unclear. We aimed to construct a single-cell transcriptomic atlas of AoV carcinoma, with a focus on identifying epithelial subtypes and their interactions with the tumor microenvironment (TME). METHODS: We performed single-cell RNA sequencing on eight primary AoV tumors and four matched normal tissues. Comprehensive clustering and transcriptomic analyses identified cell-type composition, epithelial heterogeneity, and tumor-immune interactions. Findings were validated using deconvolution of bulk RNA-seq data from 62 AoV carcinoma patients. Results Malignant epithelial cells were categorized into four distinct subtypes: Int-Wnt, PB-KRAS, Int-Hypoxia, and Cycling stage. PB-KRAS cells exhibited stem-like transcriptional programs and high genomic instability. Deconvolution analysis of bulk RNA-seq data from the independent AoV cohort revealed that enrichment of the PB-KRAS subtype correlated with tumor recurrence and poor survival. Our immune profiling analysis discovered a significant association between PB-KRAS subtype and GZMK+ CD8+ T cells, which are in a pre-dysfunctional state, alongside SPP1+ macrophages exhibiting immunosuppressive traits. Spatial transcriptome data further supports the immunosuppressive natures of TME around PB-KRAS subtype malignant epithelial cells in AoV carcinoma. CONCLUSIONS: Our study presents a single-cell atlas of AoV carcinoma, highlighting the molecular diversity of malignant epithelium and its association with the immune microenvironment. The PB-KRAS subtype emerges as a stem-like, immunosuppressive tumor state associated with poor prognosis, providing insights for future therapeutic targeting.

Ampulla of Vater carcinoma↗

PubMed2026

Synovial short-lived plasma cells mediate adalimumab resistance in rheumatoid arthritis via MIF-CD74 axis-driven, partially TNF-α-independent inflammation.

OBJECTIVE: Synovial plasma cell infiltration predicts inadequate response to adalimumab in patients with rheumatoid arthritis (RA), yet the cellular and molecular mechanisms underlying this association remain unclear. This study aimed to dissect the functional heterogeneity of synovial plasma cells between adalimumab responders and non-responders at single-cell resolution, and to identify the molecular pathways driving treatment resistance. METHODS: This study was based on a prospective clinical cohort of 101 RA patients receiving adalimumab, from which synovial tissues of 8 patients (4 ACR20 responders and 4 non-responders) were profiled by 10x Genomics single-cell RNA sequencing (66,539 high-quality cells). A systematic ligand-receptor screening was performed to identify candidate signaling axes. Core findings were validated at four levels: an independent single-cell validation cohort (n = 4), external bulk RNA-seq cohorts (GSE15602, GSE47726), multiplex immunofluorescence on synovial tissues (n = 9 per group), and in vitro functional experiments using patient-derived peripheral blood monocyte-derived macrophages stimulated with recombinant human MIF under pharmacological intervention with adalimumab, the MIF inhibitor ISO-1, and an anti-CD74 neutralizing antibody. RESULTS: Plasma cells were significantly enriched in non-responder synovium, with a heterogeneous pattern characterized by quantitative accumulation of long-lived plasma cells (LLPCs) and functional dominance of short-lived plasma cells (SLPCs): SLPCs contributed 58.15% of total ribosomal module activity and preferentially overexpressed MIF. Systematic screening of 145 candidate ligand-receptor pairs identified MIF-CD74 as the only axis satisfying all four independent evidence layers. Tissue-level immunofluorescence confirmed that approximately 95% of synovial CD138+ plasma cells in non-responders co-expressed MIF, compared with approximately 45% in responders. In vitro, rh-MIF upregulated macrophage activation markers (CD74, CD80, CD86, HLA-DR) and induced IL-6 and TNF-α secretion. Adalimumab neutralized supernatant TNF-α but failed to suppress MIF-driven IL-6 and IL-1β activation, whereas ISO-1 and anti-CD74 effectively blocked MIF-induced effects at all levels examined. These findings were replicated in patient-derived PBMC macrophages. CONCLUSION: In adalimumab-resistant RA, a functionally active SLPC subset drives partially TNF-α-independent macrophage inflammation through the MIF-CD74 axis, representing a resistance pathway not fully addressed by anti-TNF therapy. Targeting MIF or CD74 blocked this axis in vitro, supporting MIF-CD74-directed precision intervention.

Adalimumab↗

PubMed2026

Developmental analysis of the cone photoreceptor-less little skate retina reveals distinct Onecut1 isoforms.

The retinal development of elasmobranchs, the subclass comprising sharks, skates, and rays, remains poorly understood. This group is diverse in retinal phenotype, with many sharks and rays possessing rods together with one or more cone types. In contrast, the little skate (Leucoraja erinacea) has only a single rod photoreceptor type, which has been reported to exhibit some physiological and anatomical properties associated with cones. To investigate how this unusual photoreceptor system develops, we first identified an embryonic stage of early photoreceptor formation based on otx2 expression. We then developed a retinal electroporation approach to test whether a onecut1-dependent cone-associated reporter could be activated in the embryonic skate retina. Activation of this reporter was not detected, indicating that the corresponding enhancer is not robustly active under the conditions tested. To assess developmental changes in gene expression, we generated bulk RNA-seq datasets from embryonic, hatchling, and adult retinas. These analyses showed strong embryonic expression of onecut1, increasing expression of rod-associated genes through development, and pseudogenization or loss of multiple cone-enriched genes. We further identified a developmentally regulated onecut1 splice isoform containing an additional 48 amino acid sequence between the CUT and homeodomain DNA-binding domains. This spacer-containing isoform, termed LSOC1X2, was most abundant in the embryonic retina. To test whether LSOC1X2 retained regulatory activity, we assayed it in a mouse retinal reporter system. Both skate Onecut1 isoforms activated the ThrbCRM1 reporter in this heterologous context. Together, these findings identify a novel, developmentally regulated retinal onecut1 isoform in the little skate and establish it as a candidate regulator for future studies of photoreceptor development in this species and its elasmobranch relatives.

Animals↗

PubMed2026

FANCI promotes esophageal squamous cell carcinoma progression and cell cycle regulation and interacts with FANCD2.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is an aggressive malignancy with poor clinical outcomes, and reliable molecular biomarkers and therapeutic targets remain limited. Fanconi anemia group I protein (FANCI) is a core component of the Fanconi anemia (FA) pathway, but its expression pattern, clinical significance, and functional role in ESCC have not been comprehensively defined. This study aimed to investigate FANCI expression and prognostic value in ESCC, assess its effects on malignant cellular phenotypes and tumor growth, and explore its potential mechanistic relationship with Fanconi anemia group D2 protein (FANCD2) and cell-cycle regulation. METHODS: Multi-cohort analyses were performed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, together with ESCC single-cell RNA sequencing (RNA-seq) data. FANCI functions were assessed by bidirectional gain- and loss-of-function experiments in vitro (proliferation, colony formation, migration, invasion, apoptosis, and cell-cycle assays) and by xenograft models in vivo. Mechanistic studies included protein-protein interaction (PPI) analyses, co-immunoprecipitation (Co-IP), and immunofluorescence (IF) colocalization. RESULTS: FANCI was consistently upregulated in ESCC across bulk transcriptomic datasets and was further supported by quantitative polymerase chain reaction (qPCR), Western blotting, and immunohistochemistry (IHC). FANCI discriminated ESCC from normal tissues in TCGA-ESCC and was independently validated in GSE53624 [area under the curve (AUC) =0.940 and 0.975, respectively]. FANCI was associated with poorer overall survival (OS) and shorter disease-free interval (DFI), and these findings were validated in an independent GEO cohort. Functionally, FANCI promoted ESCC cell proliferation, migration, and invasion, while inhibiting apoptosis; FANCI knockdown suppressed tumor growth in vivo and induced G2/M cell-cycle arrest. Mechanistically, FANCI physically interacted with FANCD2, colocalized with FANCD2 in the nucleus, and was associated with altered FANCD2 protein abundance, consistent with cell-cycle and DNA repair-related programs. Single-cell analysis indicated that FANCI was enriched in epithelial cells and associated with higher activity of malignant functional programs. In TCGA-ESCC, FANCI-high tumors showed distinct mutation profiles, a trend toward increased tumor mutation burden (TMB), and altered immune-associated signatures. CONCLUSIONS: FANCI is upregulated in ESCC and is associated with diagnostic and prognostic value. It promotes malignant phenotypes and tumor growth, potentially through a FANCI-FANCD2-linked cell-cycle/DNA repair program, supporting FANCI as a candidate biomarker and therapeutic target in ESCC.

Esophageal squamous cell carcinoma (ESCC)↗

PubMed2024

Meningioma transcriptomic landscape demonstrates novel subtypes with regional associated biology and patient outcome.

Meningiomas, although mostly benign, can be recurrent and fatal. World Health Organization (WHO) grading of the tumor does not always identify high-risk meningioma, and better characterizations of their aggressive biology are needed. To approach this problem, we combined 13 bulk RNA sequencing (RNA-seq) datasets to create a dimension-reduced reference landscape of 1,298 meningiomas. The clinical and genomic metadata effectively correlated with landscape regions, which led to the identification of meningioma subtypes with specific biological signatures. The time to recurrence also correlated with the map location. Further, we developed an algorithm that maps new patients onto this landscape, where the nearest neighbors predict outcome. This study highlights the utility of combining bulk transcriptomic datasets to visualize the complexity of tumor populations. Further, we provide an interactive tool for understanding the disease and predicting patient outcomes. This resource is accessible via the online tool Oncoscape, where the scientific community can explore the meningioma landscape.

Meningioma↗

PubMed2026

Deciphering novel targets in salivary gland pleomorphic adenoma by integrating plasma proteomics and parotid transcriptomics analyses.

BACKGROUND/PURPOSE: Pleomorphic adenoma (PA) is the most common salivary gland benign tumor, with its molecular drivers elusive due to a lack of experimental models. This study aimed to decipher novel targets in PA by systematically integrating plasma protein quantitative trait loci (pQTL)-based Mendelian randomization (MR) with multi-omics profiling of parotid gland tissues. MATERIALS AND METHODS: We performed two-sample MR using 5450 plasma pQTLs and genome-wide association study summary for benign or broader salivary gland diseases from FinnGen consortium. Bulk RNA-sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) comparing PA to normal tissue were used for transcriptomic validation. Immunohistochemistry (IHC) was applied for protein-level validation in human PA, adenoid cystic carcinoma (ACC), and murine inflammatory lesions. RESULTS: MR identified 12 plasma proteins associated with benign salivary gland tumor risk. Transmembrane serine protease 6 (TMPRSS6) was the only protein significantly risk-increasing for both benign and broader salivary gland diseases. Strikingly, mitogen-activated protein kinase kinase 4 (MAP2K4) showed opposite MR effects between benign and all-lesion outcomes. Bulk RNA-seq showed limited concordance with MR findings, while scRNA-seq revealed a unique plastic epithelium and partially validated candidates at cellular resolution. Critically, IHC confirmed MAP2K4 protein overexpression specifically in human PA, but not in ACC or inflammatory lesions, while TMPRSS6 was downregulated in established pathologies despite its genetic risk association. CONCLUSION: By integrating plasma proteome-based causal inference with parotid tissue multi-omics, this study unveils MAP2K4 as a potential PA-specific driver. This integrative framework provides novel, context-specific targets for further functional investigation in salivary gland tumorigenesis.

Gene expression profiling↗

PubMed2026

A contextual activity score (CAS) for inferring ADAR-associated transcriptional activity across RNA-seq, single-cell, and spatial transcriptomics.

BACKGROUND AND OBJECTIVE: Adenosine-to-inosine RNA editing, catalyzed by Adenosine Deaminases Acting on RNA (ADARs), is a widespread modification involved in neural function, immune regulation, and cancer. The Alu Editing Index (AEI) is the standard metric to estimate ADAR activity but requires raw sequencing reads and is poorly suited for single-cell and spatial transcriptomic data. This study aimed to develop an alternative framework for inferring ADAR-associated transcriptional activity from gene expression data across diverse transcriptomic technologies. METHODS: We developed the Contextual Activity Score (CAS), a framework based on transcriptional signatures from ADAR perturbation experiments. Context-specific signatures were generated for human neurons, mouse neurons, and cancer models to infer ADAR1 and ADAR2 activity. CAS was computed from normalized gene expression matrices using regulon-based enrichment analysis. Performance was evaluated by comparing with the Alu Editing Index across bulk RNA sequencing datasets, simulated sequencing depths, and library preparation protocols. RESULTS: CAS showed strong concordance with the Alu Editing Index across multiple datasets, while remaining robust to reduced sequencing depth and different library protocols. Unlike the Alu Editing Index, CAS can be applied to single-cell and spatial transcriptomic data and enables the independent assessment of ADAR2 activity. In cancer and neuronal contexts, CAS captured biologically meaningful variations in ADAR-associated transcriptional activity at sample, cell-type, and spatial levels. CONCLUSION: CAS provides a scalable approach applicable across multiple RNA-seq protocols for estimating ADAR-associated transcriptional activity using gene expression data. This method, implemented in an open-source R package for broad adoption, expands the ability to study ADAR-associated transcriptional activity across transcriptomic modalities where direct editing quantification is challenging, such as single-cell and spatial transcriptomics.

Adenosine Deaminase↗

PubMed2026

Dysregulation of U12-Type Splicing in Lupus Neutrophils.

OBJECTIVE: Neutrophil dysfunction is a hallmark of systemic lupus erythematosus (SLE), but its molecular basis remains unclear. This study explores transcriptional and posttranscriptional changes in low-density granulocytes (LDGs), a proinflammatory neutrophil subset expanded in SLE, focusing on NADPH oxidase (Nox) function and minor intron splicing. METHODS: LDGs and normal-density granulocytes (NDGs) were isolated from patients with SLE and healthy controls (HCs). CYBA (p22phox) expression was evaluated at transcript and protein levels. Nox activity was measured using luminol assays. Bulk RNA sequencing (RNA-seq) and rMATS software were used to assess alternative splicing, particularly of U12-type intron-containing genes. RESULTS: CYBA expression was reduced in SLE LDGs (n = 11) compared to SLE and HC NDGs (n = 6), with levels resembling those in chronic granulomatous disease neutrophils. SLE LDGs exhibited impaired Nox activity (n = 7 SLE, n = 12 HC). CYBA is a U12 intron-containing gene, and transcriptomic analysis revealed broad down-regulation of this gene class in SLE LDGs, suggesting minor spliceosome dysfunction. rMATS analysis showed increased U12-type intron retention and widespread splicing defects-including exon skipping and mutually exclusive exon use-in genes such as GBP5, MAEA, and STX10. These abnormalities were validated in an independent long-read RNA-seq data set from SLE peripheral blood mononuclear cells. Importantly, splicing disruptions correlated with disease activity and autoantibody profiles. CONCLUSION: Impaired U12-dependent splicing may contribute to neutrophil dysfunction in SLE, potentially via defective oxidative burst and altered immune regulation. These findings highlight the minor spliceosome as a novel player in lupus pathogenesis.

Humans↗

PubMed2026

Integrated Genomic and Proteomic Analysis Reveals T-B Lymphocyte Signatures in the MYCN Driven "Immune Desert" of Specific Neuroblastoma Subtypes.

AIMS: This study aims to systematically dissect how MYCN amplification shapes the immunosuppressive tumor microenvironment (TME) in high-risk neuroblastoma, elucidating key mechanisms underlying immune evasion. METHODS: We performed an integrated multi-omics analysis of bulk RNA-seq (n = 721), single-cell RNA-seq (n = 9), proteomic data (n = 49) and spatial transcriptomics (Visium, with external validation in melanoma). Analyses included unsupervised clustering, cell-cell communication inference, transcriptional regulatory network reconstruction, and spatial proximity assessment to map the immune landscape. RESULTS: A distinct molecular subtype (Class C), defined by MYCN amplification and poor prognosis, exhibited a comprehensive "immune desert" phenotype characterized by low immune scores and minimal leukocyte infiltration. Single-cell analysis confirmed significant depletion of T and B lymphocytes within the Class C TME. Dysregulated transcriptional networks were identified, including upregulation of REL and EOMES in T cells-with EOMES potentially driving exhaustion via regulation of Transient Receptor Potential (TRP) genes, and REL inhibition enhancing cytotoxic function in vitro. A unique immunosuppressive B-cell subset (B7) engaged in enhanced crosstalk with exhausted T cells and harbored a MYC-centered network linked to cell cycle dysregulation and poor survival. Spatial transcriptomics revealed significant proximity between B7-active regions and Treg/exhaustion-enriched areas, externally validated in melanoma. Proteomic data validated elevated REL expression in MYCN-amplified tumors. CONCLUSION: This work delineates the immunosuppressive architecture of MYCN-driven neuroblastoma, revealing novel regulatory nodes within specific lymphocyte compartments. Integrating single-cell, spatial, and proteomic evidence, we propose REL inhibition as a therapeutic candidate, the EOMES/TRP axis as a bioinformatically supported hypothesis, and the B7/MYC hub as a hypothesis supported by transcriptomic and spatial evidence.

Humans↗

PubMed2026

Integrated single-cell and bulk transcriptomic analysis identifies a novel senescent fibroblast subtype associated with poor prognosis in acral melanoma.

BACKGROUND: Acral melanoma (AM) exhibits significant intratumoral heterogeneity, but its tumor microenvironment (TME) and immune regulation remain unclear. This study aims to dissect TME heterogeneity and establish a prognostic model based on key cell subpopulations. METHODS: We collected AM single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA). Unsupervised clustering, CellChat, and Scissor analysis were performed to characterize cellular heterogeneity, cell-cell communication, and prognosis-related cell subpopulations. Kaplan-Meier analysis was used to assess the prognostic value of key genes, which were further validated by multiplex immunohistochemistry (mIHC). RESULTS: In AM, Mel_C2, C7, and C9 with high SEMA6A and KIT expression were strongly linked to poor prognosis. We further identified a senescent fibroblast subpopulation (sCAF_CDKN2A) characterized by high fibroblast senescence signature (FSS) scores. Integrating Scissor analysis of fibroblast subtypes with bulk prognostic data, we identified COL3A1, VCAN, and KIT as prognosis-associated genes upregulated in poor-outcome-related fibroblast subsets. Cell-cell communication analysis revealed that sCAF_CDKN2A engages in an immunosuppressive network, interacting with regulatory T cells (Tregs) via MIF signaling and receiving signals from exhausted CD8+ T cells through PPIA-BSG interactions. Using transcription factor expression patterns from these fibroblast subtypes, we constructed a prognostic model that effectively stratified patients into distinct risk groups with significant differences in overall survival (OS). mIHC confirmed significantly higher protein levels of SEMA6A and COL3A1 in tumor tissues compared to matched normal tissues. CONCLUSIONS: We established a novel prognostic model for AM and identified sCAF_CDKN2A as an immunosuppressive senescent fibroblast subpopulation driving poor prognosis.

Acral melanoma↗

PubMed2026

Single-cell multiomics reveals exosome-mediated reprogramming and clonotypic remodeling of T cells in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is an aggressive and immunogenic subtype lacking targeted therapies. While tumor-derived exosomes are known to modulate immune function, their direct impact on human T cell plasticity and antigen specificity remains poorly defined. Here, we conducted a comprehensive single-cell multiomic analysis of primary human T cells exposed to exosomes derived from 17 genomically diverse TNBC cell lines and 35 patient samples. Integrating single-cell RNA-seq, V(D)J sequencing, non-coding RNA profiling, bulk and single-cell cytokine analyses, we uncovered conserved and subtype-specific immunomodulatory programs induced by TNBC exosomes. Exosome-treated T cells displayed skewing toward regulatory and dysfunctional phenotypes, including Th17-like, Treg, and PD-1⁺/PD-L1⁺ Tfh cells. Functional profiling revealed suppression of early activation markers and cytokine responses, alongside selective preservation of cytotoxic features in γδ T and NKT subsets. Transcriptomic and miRNA network analyses demonstrated widespread downregulation of immune effector genes (e.g., HBEGF and TNFSF9) mediated by exosome-delivered regulatory miRNAs (has-miR-98-5p). Notably, exosome-stimulated T cells displayed distinct clonotypic expansions, characterized by the emergence of five tumor-specific γδ TCR clonotypes and 30 unique αβ TCR CDR3 sequences that were absent in mock-treated controls, underscoring the role of exosomes in shaping TCR repertoire dynamics.

Humans↗

PubMed2026

Nested co-expression network analysis identifies compact gene clusters in a black box.

MOTIVATION: Digital analysis of biological systems requires methods capable of identifying both broad and nested gene modules reflecting complex biological processes. Existing transcriptomic methods often miss compact gene sets corresponding to subprocesses in specialized cell types, limiting insights into functional heterogeneity. RESULTS: We present Nested-WGCNA, a two-stage unsupervised network analysis algorithm designed to identify coarse-grained and fine-grained gene modules. Applied to bulk RNA-Seq data, Nested-WGCNA reveals stable modules reproducible across datasets. When validated against scRNA-Seq data, these modules correspond to both major and minor immune cell subtypes. Application to immunotherapy response datasets uncovers predictive and prognostic biomarkers, highlighting its utility in treatment stratification and biomarker discovery. AVAILABILITY: The NestedWGCNA source code and analysis pipeline are available on GitHub (https://github.com/ilyada/NestedWGCNA) and archived on Zenodo (https://doi.org/10.5281/zenodo.18959244).

Algorithms↗

PubMed2026

SCMO: a deep learning model integrating the single-cell resolution TME ecosystem and multi-omics for survival prediction in CRC patients.

BACKGROUND: Colorectal cancer (CRC) remains a leading cause of global cancer mortality, highlighting the need for precise survival prediction to guide clinical decisions. Although tissue-level multi-omics is widely utilized for survival prediction, its limited resolution cannot capture tumor heterogeneity. Single-cell RNA sequencing (scRNA-seq) enables dissection of the tumor microenvironment (TME) at cellular resolution, supporting personalized prognostic assessment. METHODS: We collected 213 CRC scRNA-seq samples and established a CRC-specific TME atlas comprising 339,060 cells. Using this atlas as a reference, we deconvolved bulk RNA-seq data from TCGA-CRC cohort with the EcoTyper algorithm to reconstruct TME features. Clinical, genomic, and transcriptomic data were obtained from the Xena platform; microbial data were sourced from the BIC database. We integrated TME and multi-omics features through a self-normalizing neural network to construct a deep learning model (single-cell resolution TME ecosystem with multi-omics data [SCMO]) for survival prediction. To enhance interpretability, we utilized the Integrated Gradients algorithm and spatial transcriptomic data to analyze multi-omics and TME features. We performed anticancer drug screening with tumor necrosis factor receptor-associated protein 1 (TRAP1), a critical feature according to the Integrated Gradients algorithm, as a potential target. RESULTS: We identified 13 survival-related TME features from the CRC-specific atlas: 12 cell states and one multi-cellular ecosystem. SCMO, which combined TME and multi-omics features, improved survival prediction and outperformed existing methods, achieving a concordance index of 0.762. The SCMO demonstrated robust performance for long-term predictions, achieving areas under the curve (AUCs) of 0.752, 0.772, and 0.869 for 1-, 3-, and 5-year predictions in the training set, with corresponding test set AUCs of 0.639, 0.756, and 0.772. TME features from the SCMO model revealed that ecosystem density increased with CRC malignancy. Multi-omics features included TRAP1 as a potential drug target. Drug screening identified saikosaponin A as a novel TRAP1 inhibitor, and its anticancer activity was validated in vitro. We developed SCMO-Lite, a simplified model incorporating 12 high-attribution-weight multi-omics features, which demonstrated robust risk stratification. CONCLUSIONS: SCMO combines analytical precision with biological interpretability, offering novel insights for oncology survival prediction.

Humans↗

PubMed2026

Radiogenomics predicts immune microenvironment heterogeneity and response to combination immunotherapy in hepatocellular carcinoma.

BACKGROUND: The combination of immune checkpoint inhibitors (ICIs) with anti-angiogenic agents is the preferred first-line therapy option for patients with advanced hepatocellular carcinoma (HCC), yet only a subset of patients responds, urging the quest for prediction biomarkers. We aimed to integrate genomics with radiology to propose an immune-derived radiogenomics biomarker of response to such combination immunotherapy and evaluate its added value in clinical context. METHODS: We integrated bulk RNA sequencing (RNA-seq) and proteomics data of 994 HCC patients with single-cell RNA-seq data of 11 samples across multiple datasets to identify an immune-related signature (IRS) that may influence sensitivity or resistance to such combined immunotherapy strategy, followed by verification of selected marker genes using immunohistochemistry and cytological experiments. We then trained/validated a cross-modality radiogenomics biomarker using machine learning based on TCIA database that was further tested in multi-scale independent cohorts covering 754 HCC patients. RESULTS: Integrative multi-omics analysis identifed a parsimonious 2-gene prognostic signature including KPNA2 and SMG5 that was significantly associated with immune heterogeneity and response to combination immunotherapy. Machine-learning pipeline exported the optimal 4-feature radiogenomics biomarker using support vector machine that significantly discriminated prognosis (hazard ratio 1.415–1.890; p < 0.05 for all) and modestly predicted response to ICI plus anti-angiogenic therapy (area under the curve 0.720–0.829) in independent retrospective series across major imaging modalities (computed tomography/magnetic resonance imaging). In a prospective neoadjuvant cohort, this biomarker also showed favorable performance for predicting pathological response and tumor recurrence, accompanied by biological validation through single-cell RNA-seq analysis of pre-treatment biopsies. CONCLUSIONS: Our study provides a cross-device-cross-modal radiogenomics biomarker that can improve patient selection for emerging ICI plus anti-angiogenic therapy with novel potential therapeutic targets in HCC.

Humans↗

PubMed2026

Transcriptome changes in circulating immune cells of critical COVID-19 patients predict a specific metabolic and epigenetic imprint.

BACKGROUND: The progression to critical COVID-19 arises predominantly from a dysregulated host immune response although the underlying regulatory mechanisms still remain partially elusive. This limits a prompt prediction of the disease progression, reduces the therapeutic options and restrains our understanding of “long COVID”. METHODS: Here, we analyzed the transcriptome of peripheral blood mononuclear cells (PBMCs) collected from COVID-19 patients experiencing different degrees of the disease (mild and critical), and control patients enrolled in the clinical trial COntAGIouS as well as independent bulk RNA-seq, single-cell RNA-seq and proteomic datasets. RESULTS: In critical COVID-19 patients, the integrative analysis of transcriptomic data revealed an altered regulatory network involving microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and coding genes that control mRNA translation-related genes, epigenetics, and metabolism. In parallel, we observed an upregulation of tRNA aminoacylation genes in critical COVID-19 patients by the analysis of either bulk or single-cell RNA-seq data from publicly available independent cohorts. Additionally, we found increased expression of coding genes enriched for the cognate amino acids (glycine, alanine, isoleucine and tyrosine), all related to protein localization, post-translational modifications, and cell metabolism in our cohort. Similar alterations in amino acid frequency were found in an independent proteomic dataset. CONCLUSIONS: Collectively, our findings indicate a broad perturbation of the gene expression landscape that characterizes the aberrant host immune response in critical COVID-19 patients and is potentially coordinated by miRNA and tRNA metabolism alterations. TRIAL REGISTRATION: COntAGIouS, NCT04327570. Registered 26 March 2020, https://clinicaltrials.gov/ct2/show/NCT04327570 .

Female↗