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PubMed2026

Hepatitis B virus genome mutations in precore and basal core promoter regions among HBeAg-negative chronic hepatitis B patients with high viral load in Indonesia.

Hepatitis B e antigen (HBeAg) is widely used as a marker for active HBV replication and serves as a surrogate for HBV DNA > 200,000 IU/mL to determine eligibility for tenofovir disoproxil fumarate (TDF) prophylaxis to prevent vertical transmission, according to WHO guidelines. However, some HBeAg-negative patients still harbor high viral loads. Mutations in the precore (PC) and basal core promoter (BCP) regions may reduce or abolish HBeAg expression without necessarily suppressing viral replication. Next-generation sequencing (NGS)-based characterization of these mutations remains limited in Indonesia. This study aimed to analyze the mutation prevalence in the BCP and PC regions associated with HBeAg negativity in Indonesian patients. We conducted a cross-sectional study of 32 chronic HBV treatment-naïve, unvaccinated patients with HBV DNA > 200,000 IU/mL (16 HBeAg-negative, 16 HBeAg-positive) at Cipto Mangunkusumo General Hospital. BCP and PC mutations were analyzed using NGS, classifying mutations as major (mutation frequency index [MFI] ≥20%) or minor (MFI 1- < 20%). Associations were analyzed using the Chi-square or Fisher's exact test and p-values were adjusted using the Benjamini-Hochberg procedure. Among 29 major mutation sites, PC mutations A1846T/C and G1896A were more frequent in HBeAg-negative than HBeAg-positive patients (81.3% vs 6.3% and 75.0% vs 12.5%, respectively; all adjusted p = 0.019). Combined analysis showed higher mutation frequencies in HBeAg-negative patients (93.8%, 81.3%, and 62.5% for A1846T/C, G1896A, and G1899A, respectively; all adjusted p = 0.015). In conclusion, HBeAg-negative patients with high viral loads are strongly associated with PC mutations, particularly G1896A, A1846T/C, and G1899A. These exploratory findings provide regional NGS-based molecular evidence that established PC mutations may contribute to the coexistence of HBeAg negativity and continued high-level HBV replication in Indonesian patients. Larger studies incorporating broader virological and clinical comparison groups are required to determine the clinical significance of these findings.

Humans↗

PubMed2026

Impact of Genomic Mutations on the Transcriptional Pathways and Tumor Microenvironment Landscape of Localized Early Prostate Cancer.

BACKGROUND: The management of intermediate-risk early prostate cancer (PCa) is challenging due to the difficulty in distinguishing indolent from aggressive tumors. This study explores the association between genomic alterations and the tumor and its microenvironment (TME) and implications for disease progression. METHODS: We performed multi-omic profiling in a cohort of 53 localized PCa using targeted sequencing, transcriptional, and proteomic spatial profiling. RESULTS: Somatic mutations and copy number alterations in RB1 (21%), PTEN (18%), and TP53 (9%) were identified. Kaplan-Meier analysis revealed that alterations in the RB and Cell Cycle pathways, particularly aberrations in PTEN, TP53, or RB1, were associated with shorter biochemical recurrence-free survival (p < 0.001). Spatial proteomic analysis demonstrated a complex immune landscape in patients with mutations. The tumor compartment demonstrated higher expression of immune checkpoint markers, T-cell activation proteins, and proliferation markers; and a TME that is enriched with CD8 + T cells and antigen-presenting cells, but also with immunosuppressive M2 macrophages, suggesting adaptive immune resistance. CONCLUSIONS: Our analysis demonstrates that genomic alterations in PTEN, TP53, or RB1 are not only prognostic for poor outcomes but are also associated with a unique, immunologically complex TME in this Brazilian cohort.

Humans↗

PubMed2026

Integration of single-cell transcriptomics and genomic mutation analysis identifies an immunotherapy-resistant tumor subcluster and validates ARNTL2 as a malignant driver in lung adenocarcinoma.

BACKGROUND: Immunotherapy resistance in lung adenocarcinoma (LUAD) remains a critical clinical challenge, and the mechanisms underlying resistance-associated intratumoral heterogeneity are poorly characterized. METHODS: We performed single-cell RNA sequencing of LUAD patients receiving neoadjuvant immunotherapy (responders vs. non-responders), integrating inferCNV, GSVA, and differential expression analyses. Cluster-specific genes were validated across seven independent cohorts (TCGA-LUAD, GSE13213, GSE26939, GSE29016, GSE30219, GSE31210, GSE42127). A multi-algorithm machine learning framework was used to construct a prognostic model, and the immune microenvironment was characterized using TCIA scoring, seven infiltration algorithms, and ESTIMATE. ARNTL2 function was assessed by CCK-8 and Transwell assays in A549 and H1299 cells. RESULTS: Non-responders showed significant enrichment of epithelial cells, depletion of cytotoxic T/NK cells, and elevated copy number variation burden versus responders (p < 0.0001). A resistance-enriched malignant subcluster (Cluster 2) exhibited hyperproliferative and metabolic reprogramming signatures with upregulated KRT17, S100A2, and CST6, which showed tumor-specific overexpression, adverse prognostic value, and genomic amplification across cohorts. CoxBoost combined with survivalSVM achieved optimal predictive performance (C-index = 0.686), yielding robust risk stratification (HR: 2.54-10.51, all p < 0.05). Low-risk patients showed greater immune infiltration and higher TCIA immunophenoscores. ARNTL2 was an independent prognostic factor (HR: 2.07-4.64) strongly correlated with risk score (r = 0.69), and its knockdown suppressed proliferation and invasion in both LUAD cell lines (all p < 0.05). CONCLUSION: This study identifies a resistance-associated malignant subcluster in LUAD, constructs a validated CoxBoost + survivalSVM prognostic model with robust immune stratification, and establishes ARNTL2 as a core oncogenic driver and therapeutic target.

ARNTL2↗

PubMed2026

Somatic mutations and genome mosaicism in aging and disease.

Age-related genome mosaicism is an inherent feature of multicellularity and genomic instability. It occurs because of DNA mutations, the accumulation of which leads to diverse genomic landscapes across different tissues. DNA mutations in the genome are consequences of DNA damage, changes in the chemical structure of DNA, such as strand breaks or loss of bases. DNA damage is very frequent and normally repaired quickly. However, errors intrinsic to DNA repair or replication can give rise to permanent changes in genome sequence information. Such DNA mutations are diverse and include single-nucleotide variants, small insertions and deletions, and larger genome structural variants. Since the 1950s, somatic mutations have been proposed to be a major cause of aging. Indeed, somatic mutations are the cause of cancer, the risk of which increases exponentially with age, and possibly other age-related diseases, such as neurodegenerative diseases and cardiomyopathies. Somatic mutations vary from cell to cell owing to the innate stochasticity of their occurrence, from error-prone processing of randomly inflicted DNA damage. With the emergence of single-cell and single-molecule sequencing, it has become possible to quantitatively analyze somatic mutations in human cells and tissues. Here, we discuss a possible causal relationship between mutation-driven mosaicism of the somatic genome and aging-related functional decline and disease by exploring several predictions of the somatic mutation theory of aging.

Humans↗

PubMed2024

ERCC2 mutations alter the genomic distribution pattern of somatic mutations and are independently prognostic in bladder cancer.

Excision repair cross-complementation group 2 (ERCC2) encodes the DNA helicase xeroderma pigmentosum group D, which functions in transcription and nucleotide excision repair. Point mutations in ERCC2 are putative drivers in around 10% of bladder cancers (BLCAs) and a potential positive biomarker for cisplatin therapy response. Nevertheless, the prognostic significance directly attributed to ERCC2 mutations and its pathogenic role in genome instability remain poorly understood. We first demonstrated that mutant ERCC2 is an independent predictor of prognosis in BLCA. We then examined its impact on the somatic mutational landscape using a cohort of ERCC2 wild-type (n = 343) and mutant (n = 39) BLCA whole genomes. The genome-wide distribution of somatic mutations is significantly altered in ERCC2 mutants, including T[C>T]N enrichment, altered replication time correlations, and CTCF-cohesin binding site mutation hotspots. We leverage these alterations to develop a machine learning model for predicting pathogenic ERCC2 mutations, which may be useful to inform treatment of patients with BLCA.

Humans↗

PubMed2025

Long-Read Sequencing of the MUC1 VNTR: Genomic Variation, Mutational Landscape, and Its Impact on ADTKD Diagnosis and Progression.

BACKGROUND: ADTKD-MUC1 is caused by frameshift mutations in MUC1 gene that produce a frameshifted protein (MUC1fs) toxic to kidney cells. The gene's variable number of tandem repeats (VNTR), with high GC content, makes it largely inaccessible to standard sequencing. As a result, both the reference sequence and natural variation in this region remain poorly defined, complicating mutation detection and data interpretation. Standard methods also fail to pinpoint the exact VNTR unit affected, limiting insight into mutation mechanisms and genotype-phenotype correlations. METHODS: We employed Single Molecule, Real-Time (SMRT) sequencing and characterized the genomic sequence of MUC1 in 300 individuals including 279 individuals from 143 families suspected of having ADTKD-MUC1. We compared these results to those obtained using the CLIA-approved mass spectrometry-based probe extension (PE) assay, which specifically detect the most prevalent 59dupC mutation. We correlated the structural features of the MUC1 VNTR with the rate of kidney function decline in affected individuals. RESULTS: We identified MUC1 consensus sequences for 205 unique VNTR alleles, with 9 distinct types of frameshift mutations present on 52 distinct mutated VNTR alleles. MUC1 frameshift mutations were identified in 71 of 143 families (50%) with suspected ADTKD, comprising 135 genetically affected individuals (48%). The SMRT assay exhibited complete concordance and revealed that the PE assay is capable of detecting frameshift mutations in approximately 85% of affected families. The constellation of VNTR structures supports a genotype-progression model, in which fast progressors exhibit a significantly lower number of repeat units on the wild-type allele and a higher number of repeats on the mutation-bearing allele, including an increased number of frameshifted repeat units. CONCLUSIONS: SMRT sequencing outperforms current diagnostic methods for ADTKD-MUC1 and reveals the prognostic value of VNTR structures. Although their contribution to disease progression is modest (~6% variance explained), it remains biologically and clinically meaningful.

Autosomal Dominant Tubulointerstitial Kidney Disea↗

PubMed2026

Patterns of Drug Resistance, Drug Resistance Conferring Mutations and Genomic DNA Methylation Revealed in Mycobacterium tuberculosis From South Africa.

Tuberculosis remains a major public health threat globally, with drug-resistant strains undermining treatment efficacy. We analyzed 126 Mycobacterium tuberculosis (M. tuberculosis) isolates with diverse drug resistance spectra and selected 35 for whole genome sequencing (WGS) using Illumina NextSeq, SMRT PacBio Onso and SMRT PacBio Revio sequencing platforms. The study aimed to characterize drug resistance profiles, compare short- and long-read sequencing performance, identify lineages among South African isolates, detect known drug resistance mutations and their lineage-specific patterns, and utilize long-read SMRT platforms for epigenetic profiling. Multiple drug resistance mutations were identified, some lineage-specific, and notably, East-African-Indian (EAI) Lineage 1 isolates often considered less pathogenic, showed significant potential for multidrug-resistance development, including higher fluoroquinolone resistance as compared to other lineages. Three DNA motifs with methylated adenines, namely CACGCaG, CtCCaG and GaTNNNNRtAC, were detected, with methylation patterns varying by lineage and strain due to mutations in the corresponding methyltransferases (MTases). A particularly notable finding was the stable maintenance of a genetic heterogeneity in the mamB MTase, performing methylation at CACGCaG motifs. These results highlight the combined role of genetic and epigenetic variation in M. tuberculosis adaptive evolution and underscore the value of integrating long-read sequencing into TB surveillance and research.

Mycobacterium tuberculosis↗

PubMed2026

Identification of ultrasound-associated gene candidates in myeloid cells and construction of a prognostic risk model for acute myeloid leukemia.

BACKGROUND: Incorporating ultrasound (US) treatment sensitivity analysis may improve the treatment of acute myeloid leukemia (AML). METHODS: This study integrated single-cell and bulk datasets for analysis. Differential expression analysis between US-treated and control samples was performed using limma package. The AUCell package was used to calculate US-associated scores in the single-cell dataset. Differentially expressed genes (DEGs) between the specific groups were identified, followed by intersection analysis with previously identified DEGs. Univariate regression, Least Absolute Shrinkage and Selection Operator (LASSO) analysis (using the glmnet package), and stepwise multivariate regression (using the MASS package) were used to refine the candidate genes and to construct a risk model. The model genes were validated using in vitro experiments. Enrichment analysis was conducted using gene set enrichment analysis (GSEA), and immune infiltration was evaluate by single-sample GSEA (ssGSEA) and ESTIMATE algorithms. The correlations between RiskScores and drug sensitivity were analyzed by oncoPredict package. Finally, tumor mutational burden (TMB) and genomic mutations were compared between the risk groups. RESULTS: Nine prognostic signatures (SPINK2, HNRNPAB, SH3BGRL3, CLEC11A, ITGA4, RPL39L, MX1, HEXIM1, and MAP4K4) were identified. Particularly, low expression of SPINK2 attenuated the activity and invasion of AML cells. High-risk group had higher immune cell infiltration. Eight drugs were predicted to be correlated with the RiskScore model. DNMT3A and RUNX1 showed higher mutation frequencies in the high-risk group, whereas KIT and MUC16 showed higher mutation frequencies in the low-risk group. CONCLUSION: The RiskScore model established in this study provides a theoretical basis for clinically screening responsive populations and optimizing treatment strategies.

Humans↗

PubMed2025

ProgModule: A novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy response.

BACKGROUND: Cancer originates from dysregulated cell proliferation driven by driver gene mutations. Despite numerous algorithms developed to identify genomic mutational signatures, they often suffer from high computational complexity and limited clinical applicability. METHODS: Here, we presented ProgModule, an advanced computational framework designed to identify mutation driver modules for cancer prognosis and immunotherapy response prediction. In ProgModule, we introduced the Prognosis-Related Mutually Exclusive Mutation (PRMEM) score, which optimizes the balance between exclusive mutation coverage and the incorporation of mutation combination mechanisms critical for cancer prognosis. RESULTS: Applying to BLCA and HNSC cohorts, ProgModule successfully identified driver modules that stratify patients into distinct prognostic subgroups, and the combination of these modules could serve as an effective prognostic biomarker. Extending our method to diverse cancers, ProgModule presented robust prognostic performance and stability across model parameters, including stopping criteria and network topology. Moreover, our analysis suggested that driver modules can predict immunotherapeutic benefit more effectively than existing signatures. Further analyses based on published CRISPR data indicated that genes within these modules may serve as potential therapeutic targets. CONCLUSIONS: Altogether, ProgModule emerges as a powerful tool for identifying mutation driver modules as prognostic and immunotherapy response biomarkers, and genes within these modules may be used as potential therapeutic targets for cancer, offering new insights into precision oncology.

Humans↗

PubMed2025

MitoScribe single-cell molecular recorder logs graded signaling dynamics into mitochondrial DNA.

Genetically encoded DNA recorders convert transient biological events into stable genomic mutations, offering a means to reconstruct past cellular states. However, current approaches to log historical events by modifying genomic DNA have limited capacity to record the magnitude of biological signals within individual cells. Here, we introduce MitoScribe, a mitochondrial DNA (mtDNA)-based recording platform that uses mtDNA base editors (DdCBEs) to write graded biological signals into mtDNA as neutral, single-nucleotide substitutions at a defined site. Taking advantage of the hundreds to thousands of mitochondrial genome copies per cell, we demonstrate MitoScribe enables reproducible, highly sensitive, non-destructive, durable, and high-throughput measurements of molecular signals, including hypoxia, NF-κB activity, BMP and Wnt signaling. We show multiple modes of operation, including multiplexed recordings of two independent signals, and coincidence detection of temporally overlapping signals. Coupling MitoScribe with single-cell RNA sequencing and mitochondrial transcript enrichment, we further reconstruct signaling dynamics at the single-cell transcriptome level. Applying this approach during the directed differentiation of human induced pluripotent stem cells (iPSCs) toward mesoderm, we show that early heterogeneity in response to a differentiation cue predicts the later cell state. Together, MitoScribe provides a scalable platform for high-resolution molecular recording in complex cellular contexts.

Journal Article↗

PubMed2026

Fault Lines in the Genome: Somatic DNA Mutations in Aging and Neurodegeneration.

The human genome is both fragile and resilient: prone to alteration yet protected by extensive repair mechanisms. With age, individuals accumulate genetic damage from environmental factors and cell-intrinsic processes, with effects ranging from benign nucleotide shifts to disease-driving mutations. Such alterations to the genetic code outside the germline are described as somatic mutations and display striking heterogeneity across cell types. Recently, somatic mutations have emerged as a hallmark feature of aging in the body's longest-lived tissue: the central nervous system (CNS). The distinctively long lifespan, high metabolism, electrochemical activity, and unique epigenome of CNS cells may render them especially vulnerable to mutational accumulation. The CNS therefore provides a model for understanding how somatic mutations drive cellular dysfunction beyond an established role in cancer. Here, we review the somatic mutations that arise in the brain across lifespan, the mechanisms that lead to their formation, and their potential contributions to aging and age-related disease.

Journal Article↗

PubMed2026

Caloric restriction modulates genome-wide somatic mutation in mice.

Somatic mutations accumulate throughout life in every cell, and this process constitutes one of the hallmarks of aging-genomic instability. Caloric restriction (CR) has been shown to extend lifespan across diverse species. Using high-fidelity duplex DNA sequencing of bulk liver, bulk kidney, hepatocytes, and cerebellar neurons, we found that CR in mice reduces genome-wide somatic mutation burdens across multiple tissues and cell types. CR reduced both substitution and insertion/deletion burdens, with the magnitude of these effects varying across sample types. CR also decreased the activity of the enigmatic single-base substitution (SBS) mutational process SBS5 that gives rise to most mutations in mammals. Surprisingly, the mutation burden reduction from CR was greatest in transcriptionally inactive regions. This work illuminates links between diet, aging, and genomic integrity and establishes genomic integrity as a modifiable axis of aging.

DNA↗

PubMed2025

Multimodal deep learning for immunotherapy response prediction and biomarker discovery in non-small cell lung cancer.

OBJECTIVE: Immunotherapy has emerged as a promising treatment for advanced non-small cell lung cancer (NSCLC), but accurately predicting which patients will benefit from it remains a major clinical challenge. To address this, we aim to develop a novel multimodal method, DeepAFM, that integrates histopathology, genomic features, and clinical information to predict patient responses to anti-PD-(L)1 immunotherapy. MATERIALS AND METHODS: A total of 93 patients with advanced NSCLC were included in this study. Histopathological whole-slide images were processed using a self-supervised VQVAE2 for representation learning. PCA and K-means clustering were then applied for dimensionality reduction and feature grouping. Key regions of interest were visualized through permutation importance evaluation and color-coding techniques. The extracted histopathological features, along with genomic alterations and clinical variables, were integrated into the DeepAFM multimodal prediction model. RESULTS: The DeepAFM achieved a high predictive performance with an area under the curve (AUC) of 0.77 (95% confidence interval: 0.69-1.00). Attention-based heatmaps revealed that the model could identify critical pathological patterns, genomic mutations, and clinical indicators associated with patient responses to immunotherapy. DISCUSSION: The integration of multimodal data enabled the model to capture complex interactions among pathology, genomics, and clinical characteristics, enhancing the interpretability and predictive power of immunotherapy response prediction. The visualization techniques facilitated the identification of biologically meaningful features and potential biomarkers. CONCLUSION: This study demonstrates the effectiveness of the DeepAFM in predicting responses to immunotherapy in advanced NSCLC. The approach not only improves prediction accuracy but also provides valuable insights for personalized treatment strategies and biomarker discovery.

Humans↗

PubMed2026

Homology-directed CRISPR-Cas9 correction of the KRT5 p.E475G mutation in human iPSC line from a patient with severe epidermolysis bullosa simplex.

Severe epidermolysis bullosa simplex is a skin fragility disorder characterized by blistering caused by cytolysis within basal keratinocytes, resulting in compromised epidermal integrity. Here we report the generation of the human induced pluripotent stem cell (hiPSC) line MLi002-A-1, an isogenic control derived from patient-specific MLi002-A line carrying the KRT5 c.1424A > G (p.E475G) mutation. Genome editing restored the wild-type sequence without detectable changes at top-predicted off-target sites. The edited line exhibits a normal karyotype, typical pluripotent morphology, robust pluripotency marker expression, and trilineage differentiation potential. This genetically matched control enables mutation-specific studies and in vitro modeling of epidermolysis bullosa simplex.

CRISPR-Cas9↗

PubMed1975

RNA processing and RNA tumor virus origin and evolution.

The results of molecular hybridization experiments with high-molecular-weight RNA isolated from RNA tumor viruses and DNA from normal cells suggest that RNA tumor virus genomes originate from cell genes. Some RNA tumor viruses (here called class 1) appear to have been generated in recent times in that their RNA is closely related in nucleotide sequence to certain cell genes (class 1 genes). A second class of RNA tumor viruses (here called class 2) is more distantly related to genomic information of normal cells. Structural properties of the RNA of RNA tumor viruses lead us to propose that the tumor virus RNA is originated when RNA transcripts of class 1 genes are processed by a mechanism we call "paraprocessing." We postulate that RNA paraprocessing is normally used only at particular times during differentiation and is characterized by the cytoplasmic appearance of high-molecular-weight RNA chains containing terminal polyadenylic acid (200 residues). Paraprocessing of class 1 gene transcripts in committed or differentiated cells is considered to be aberrant in transcription that can lead to the generation of an RNA tumor virus genome. If the paraprocessed class 1 gene transcript codes for a reverse transcriptase, replication of the RNA becomes possible. Transfer of the replicating RNA to a new cell can result in genetic change such that the virus genome mutates, differing from the original progenitor genes. We propose that this genetic change causes class 1 viruses to become class 2. These ideas are applied to evidence concerning the biology of infection of RNA tumor viruses and concerning the involvement of RNA tumor viruses in human cancer. Genetic change can also occur during the origination of an RNA tumor virus genome by repeated reverse transcription and recombination (45) or by genetic alteration of particularly changeable cell genes ("hot spots") (43).

Animals↗

PubMed2026

Molecular profiling of pancreatic acinar cell carcinoma and amphicrine-like carcinoma: high frequency of homologous recombination deficiency and molecular heterogeneity.

BACKGROUND: The 6th edition of the WHO Classification of Digestive System Tumours distinguishes amphicrine-like carcinomas (ALCs) from mixed neuroendocrine-non-neuroendocrine neoplasms (MiNENs). Acinar cell carcinomas (ACCs) with an intimately admixed and not separated neuroendocrine component comprising >30% of the tumour are classified as amphicrine-like ACCs (AL-ACCs). We characterised the genomic landscape of pancreatic ACCs and AL-ACCs to validate current classification and identify therapeutic targets. METHODS: Among 2,151 pancreatic biopsy and resection cases that underwent targeted next-generation sequencing using the OncoPanel AMC v4.3 or v4.5 (DNA-based hybrid capture, targeting 323 genes (v4.3) or 343 genes (v4.5)), eight ACCs, seven AL-ACCs originally diagnosed as MiNENs under the 5th edition of the WHO classification scheme, and four neuroendocrine tumours (NETs) were identified, diagnosed between 2020 and 2026. RESULTS: Homologous recombination deficiency (HRD)-associated alterations, involving BRCA1/2, ATM and FANCD2, were identified in 87.5% (7/8) of ACCs and 29% of AL-ACCs. One ACC had an ATRX nonsense mutation. Genomic heterogeneity was observed in molecular profiling of AL-ACCs; two demonstrated a 'true hybrid' signature with co-occurrence of lineage-specific drivers: MEN1 deletion and splice site mutation (neuroendocrine-associated), APC, SMAD4 and CTNNB1 alterations (exocrine-associated). Two others exhibited 'ACC-like' signatures, including missense BRCA1 and nonsense TP53 mutations and MDM4 and AKT3 amplifications, located on chromosome 1q, despite their neuroendocrine differentiation. CONCLUSIONS: Pancreatic ACCs frequently harbour HRD-related alterations, suggesting potential for PARP-inhibitor therapy. AL-ACCs comprise molecularly heterogeneous groups, including true hybrid and ACC-like patterns. Larger studies are required to elucidate the molecular distinction between true hybrid AL-ACCs and those with single-lineage alterations to refine their classification.

acinar↗

PubMed2025

SURROGATE SELECTION OVERSAMPLES EXPANDED T CELL CLONOTYPES.

Surrogate selection is an experimental design that without sequencing any DNA can restrict a sample of cells to those carrying certain genomic mutations. In immunological disease studies, this design may provide a relatively easy approach to enrich a lymphocyte sample with cells relevant to the disease response because the emergence of neutral mutations associates with the proliferation history of clonal subpopulations. A statistical analysis of clonotype sizes provides a structured, quantitative perspective on this useful property of surrogate selection. Our model specification couples within-clonotype birth-death processes with an exchangeable model across clonotypes. Beyond enrichment questions about the surrogate selection design, our framework enables a study of sampling properties of elementary sample diversity statistics; it also points to new statistics that may usefully measure the burden of somatic genomic alterations associated with clonal expansion. We examine statistical properties of immunological samples governed by the coupled model specification, and we illustrate calculations in surrogate selection studies of melanoma and in single-cell genomic studies of T cell repertoires.

Bayes’s rule↗

PubMed2026

Prognostic model based on calcium-related genes predicts prognosis and reveals the immune landscape of acute myeloid leukemia.

Acute myeloid leukemia (AML) exhibits heterogeneous outcomes and lacks reliable prognostic markers. As a critical regulator of cell fate, the prognostic value of calcium signaling in AML requires investigation. This study aimed to construct a calcium-related gene (CRG)-based prognostic model for AML. Differential analysis on RNA-seq data was conducted for AML from The Cancer Genome Atlas and Gene Expression Omnibus (GEO). Intersecting differentially expressed genes and CRGs yielded AML-associated differentially expressed CRGs (DECRGs). A prognostic model was developed using univariate/multivariate Cox regression and least absolute shrinkage and selection operator (LASSO) and validated in a GEO dataset. Bioinformatics analyses explored the links between risk groups and immune characteristics, genomic mutations, and drug sensitivity. Key genes' effects on cell proliferation, apoptosis, and differentiation were verified in vitro using CCK-8 assay, colony formation assay, and flow cytometry. The 13-DECRG-based model distinguished high- and low-risk patients in both training and validation cohorts, with high-risk patients showing a worse prognosis. The risk score was an independent prognostic factor. Immune analysis revealed a unique immune microenvironment for the high-risk group. CAMK2A overexpression inhibited cell proliferation and colony-forming ability, promoted cell apoptosis, and induced an increased proportion of CD11b- and CD14-positive cells. In vitro experiments indicated CAMK2A-induced suppression of AML cells' malignant phenotype by activating the P53 signaling pathway. An AML CRG-based model with favorable risk stratification performance was constructed. In vitro experiments revealed CAMK2A-induced inhibition of the malignant phenotype via suppressing proliferation, promoting apoptosis, and facilitating myeloid differentiation in AML cells. This study provides novel evidence for understanding CRGs in AML as well as the potential functions of CAMK2A.

Journal Article↗