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Dual RNA isolation from blood: an optimized protocol for host and bacterial RNA purification for dual RNA-sequencing analysis in whole blood sepsis samples.

Dual RNA-sequencing (dual RNA-seq) holds significant promise for deciphering bacterial virulence mechanisms during systemic infections. However, its application in sepsis research is hindered by technical challenges, including a low bacterial burden in blood and limited sample volumes and RNA yield from vulnerable populations, such as neonates. We developed an optimized protocol [dual RNA isolation from blood (DRIB)] for simultaneous stabilization, isolation and purification of high-quality host leukocyte and bacterial RNA from low-volume whole blood samples (0.5 ml). This protocol is compatible with clinical sample collection workflows and high-throughput RNA sequencing. The feasibility of DRIB for dual RNA-seq was validated using a pilot cohort of clinical adult sepsis samples, enabling the investigation of host-bacterial gene expression during sepsis. The DRIB protocol yielded 2.10-6.91 µg of total RNA per clinical sample in our pilot cohort. Dual-species ribosomal RNA (rRNA) depletion and RNA-seq generated 16.6-24.8 million filtered reads per sample, with 63±7% of reads uniquely mapped to host or bacterial sequences. Host genes accounted for 51-68% (8.4-10.9 million) reads, while 0.5-6.7% (79,496-789,808 reads) mapped to bacterial genomes. Bioinformatic analysis revealed that both shared and individual transcriptional patterns were identified in host and bacterial responses, including pathways related to immune metabolism and metal-ion binding. Our optimized DRIB protocol and RNA-seq pipeline effectively captured both host and bacterial RNA transcription in clinical sepsis samples. Expanding this approach to larger cohorts and varying disease timepoints will provide crucial new insights into host-bacterial gene co-expression dynamics in sepsis progression and outcomes.

Humans↗

Ovarian development is driven by early spatiotemporal priming of the coelomic epithelium.

Ovarian organogenesis requires the coordinated specification of supporting and steroidogenic cell lineages from multipotent coelomic epithelium (CE) progenitors. A longstanding question is whether the CE contains transcriptionally distinct, spatially organized progenitor subpopulations with predetermined lineage biases, or whether specification into supporting and steroidogenic lineages occurs only after delamination and integration into the bipotential gonad. The developmental origins of granulosa cells and the emergence of ovarian steroidogenic/stromal progenitors (SPs) also remain poorly defined. Here, we show that CE cells covering the fetal mouse ovary are transcriptionally heterogeneous and spatially organized into subdomains already primed toward supporting or steroidogenic fates. CE priming is dynamic, with transient coexistence of supporting- and steroidogenic-biased CE progenitors before resolving into a predominantly supporting-biased CE. Local delamination of these primed cells seeds intragonadal niches where pre-granulosa cells and SPs mirror the spatio-temporal arrangements of CE-primed progenitors. We further demonstrate a dual origin for the supporting lineage, with granulosa cells deriving from both the CE and supporting-like cells (SLCs). In parallel, we show that SPs arise from steroidogenic-primed CE cells, expand to represent 52% of ovarian somatic cells at birth, persist into adulthood and contribute to both theca and steroidogenic stromal cells. Together, these findings reveal transcriptionally and spatially distinct CE subpopulations that shape somatic lineage emergence with important implications for ovarian pathophysiology.

Ovarian development↗

Probiotic Lacticaseibacillus casei 2S-1 Attenuates Escherichia coli-Induced Enteritis via Gut Microbiota Modulation and Host Gene Regulation.

Maintaining gut microbial homeostasis is crucial for host health, whereas infection with Escherichia coli (E. coli) is a major contributor to intestinal inflammation and microbial dysbiosis. Recent research has focused on probiotic strategies for managing enteric inflammatory disorders. Previous studies have shown that beneficial microorganisms show protection through modulating host immune responses, enhancing intestinal epithelial barrier integrity, and inhibiting pathogenic bacteria. To evaluate the prophylactic effectiveness of a recently isolated strain, Lacticaseibacillus casei 2S-1, in a murine model of E. coli-induced enteritis, this study focuses on interactions within the microbiota-intestinal-immune axis, together with host transcriptional responses and pathway enrichment associated with oxidative stress and mitochondrial function. In vitro analysis of probiotic features, including growth dynamics, acidogenic capacity, and tolerance to acidic and bile salt environments, as well as genetic safety profiling, followed the methodical isolation and taxonomic identification of L. casei 2S-1. A preventive intervention protocol was established, and a murine model of enteritis was induced by exposure to E. coli. Histopathological analyses were performed to observe in vivo safety and protective efficacy. Changes in gut microbial structure were characterized by 16S rRNA gene sequencing, while host responses were identified by intestinal immunohistochemistry and transcriptome profiling. L. casei 2S-1 showed probiotic properties. In vitro analyses showed that the strain exhibited tolerance to acidic and bile salt conditions, and its untreated culture supernatant showed antimicrobial activity against pathogenic bacteria. Its safety profile was supported by genomic analysis, which verified the lack of virulence-associated genes and antibiotic resistance factors. In vivo, L. casei 2S-1 pretreatment reduced mortality and intestinal inflammation, modulated gut microbial composition, and preserved intestinal barrier-associated protein expression in infected mice. This study provides experimental evidence supporting the prophylactic effects of L. casei 2S-1 and its associations with gut microbiota modulation and host transcriptional responses, providing a foundation for further investigation of probiotic-based preventive strategies against intestinal infections.

Animals↗

CCT2 defines a highly cisplatin-resistant and poor-prognosis subtype of lung adenocarcinoma.

Cisplatin-based chemotherapy is a standard treatment for lung adenocarcinoma (LUAD), yet acquired cisplatin resistance remains a marked cause of treatment failure. The molecular mechanisms driving cisplatin resistance in LUAD have not been fully elucidated. The present study integrated bulk transcriptomic data, genomic mutation profiles and single-cell RNA sequencing data to systematically investigate cisplatin resistance in LUAD. Resistance-associated genes were identified through differential expression, survival analysis and database integration. Unsupervised clustering was used to define cisplatin resistance-associated subtypes. Functional characteristics were explored using pathway enrichment, immune infiltration, tumor mutation burden and weighted gene co-expression network analysis. A machine learning framework incorporating 101 algorithms was applied to identify key genes and construct a prognostic model. Single-cell analyses and in vitro experiments were performed to validate the biological role of the core gene. Molecular docking and molecular dynamics simulations were conducted to identify potential therapeutic compounds. A total of two molecular subtypes with distinct cisplatin resistance levels and prognostic outcomes were identified. The high-resistance subtype exhibited enhanced cell cycle activity, DNA repair signaling and immune heterogeneity. Machine learning analysis revealed a five-gene signature, with chaperonin-containing TCP1 subunit 2 (CCT2) emerging as a key regulator of cisplatin resistance. Single-cell analyses showed that CCT2 was predominantly enriched in resistant epithelial cell subpopulations. Functional experiments demonstrated that CCT2 knockdown significantly inhibited cell proliferation and enhanced cisplatin sensitivity in LUAD cell lines. A number of candidate compounds targeting CCT2 exhibited stable binding in silico. The present findings identified CCT2 as a key mediator of cisplatin resistance in LUAD and provided potential therapeutic strategies to overcome chemotherapy resistance.

chaperonin-containing TCP-1 subunit 2↗

[Changes in the gene expression profile of the left heart ventricle during growth in the rat].

Wistar rats of 8, 10 and 12-week-old were chosen for study of the relationship between cardiac growth and its gene expression profile changes during maturation. The ultrasonic parameters of rat hearts were recorded before sacrifice, then total RNA of left ventricle were extracted and gene expression profiles were analyzed by cDNA microarray. During growth from 8 weeks to 12 weeks, the body weight increased by 45.5% (287+/-13 g vs 197+/-10 g), and the increment in the first two-week period was equal to that of the second two-week period. The mass of left ventricle and the posterior wall thickness increased by 27.7% (0.60+/-0.03 g vs 0.47+/-0.02 g) and 23.6% (2.04+/-0.04 mm vs 1.65+/-0.13 mm), respectively, and their increment in the first two-week period was much more than that in the second one. Meanwhile, the gene expression profile of the left ventricle changed significantly, which involved cellular structure, metabolism, oxidative stress, signal transduction, etc. Compared with the 8-week-old rats, these genes were mostly up-regulated in 10-week-old rats, while for 12-week-old rats, the gene expression profile of the left ventricle recovered to the pattern of 8-week-old rats again on the whole. These results suggest that the relationship between the changes in cardiac function and gene expression profile can be analyzed comprehensively with the technique of microarray, and that the changes in gene expression profile of the left ventricle during rat maturation adapt to the physiological growth of heart, which is of benefit for keeping the metabolism balance between materials and energy.

Animals↗

Single-cell transcriptomic landscape of the southern green stink bug (Nezara viridula) midgut.

BACKGROUND: The southern green stink bug (SGSB), Nezara viridula, is a globally distributed hemipteran pest that damages many economically important crops. Its midgut supports digestion, defense, symbiosis, and interactions with orally delivered control agents, yet the cellular composition of this tissue remains poorly characterized. We therefore developed a single-cell transcriptomic atlas of the N. viridula midgut. RESULTS: Single-cell RNA sequencing of two biological replicates yielded a quality-filtered data set of 13,763 cells. Unsupervised clustering identified 12 transcriptionally distinct populations with putative annotations, including a stem cell/enteroblast (SC/EB)-like population, seven enterocyte-related populations, goblet-like cells, enteroendocrine cells, visceral muscle cells, and an extracellular-matrix-associated epithelial population. Enterocyte-related populations accounted for more than 77% of recovered cells. Putative annotations were assigned primarily from marker gene enrichment and homology to markers reported in other insects. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses identified population-associated functional enrichment patterns, and pseudotime analysis suggested transcriptional relationships between the SC/EB-like population and several enterocyte- and secretory-associated populations without establishing developmental lineages. Immune- and defense-associated transcripts were preferentially enriched in the pEC2 population, and genes associated with symbiont recognition, insecticide action, xenobiotic transport, and orally delivered double-stranded RNA showed population-biased expression. Descriptive comparisons with published insect midgut data sets identified shared and data-set-specific patterns among annotated populations. CONCLUSION: This atlas provides the first single-cell transcriptomic resource for a stink bug midgut and establishes a descriptive cellular framework for SGSB midgut biology. The dataset prioritizes candidate genes and cell populations for future spatial validation, functional testing, and studies of hemipteran midgut physiology, symbiosis, immunity, and pest-management-relevant traits. © 2026 Society of Chemical Industry.

Nezara viridula↗

Bioinformatics in the post-sequence era.

In the past decade, bioinformatics has become an integral part of research and development in the biomedical sciences. Bioinformatics now has an essential role both in deciphering genomic, transcriptomic and proteomic data generated by high-throughput experimental technologies and in organizing information gathered from traditional biology. Sequence-based methods of analyzing individual genes or proteins have been elaborated and expanded, and methods have been developed for analyzing large numbers of genes or proteins simultaneously, such as in the identification of clusters of related genes and networks of interacting proteins. With the complete genome sequences for an increasing number of organisms at hand, bioinformatics is beginning to provide both conceptual bases and practical methods for detecting systemic functional behaviors of the cell and the organism.

Computational Biology↗

Gene discovery using the serial analysis of gene expression technique: implications for cancer research.

Cancer is a genetic disease. As such, our understanding of the pathobiology of tumors derives from analyses of the genes whose mutations are responsible for those tumors. The cancer phenotype, however, likely reflects the changes in the expression patterns of hundreds or even thousands of genes that occur as a consequence of the primary mutation of an oncogene or a tumor suppressor gene. Recently developed functional genomic approaches, such as DNA microarrays and serial analysis of gene expression (SAGE), have enabled researchers to determine the expression level of every gene in a given cell population, which represents that cell population's entire transcriptome. The most attractive feature of SAGE is its ability to evaluate the expression pattern of thousands of genes in a quantitative manner without prior sequence information. This feature has been exploited in three extremely powerful applications of the technology: the definition of transcriptomes, the analysis of differences between the gene expression patterns of cancer cells and their normal counterparts, and the identification of downstream targets of oncogenes and tumor suppressor genes. Comprehensive analyses of gene expression not only will further understanding of growth regulatory pathways and the processes of tumorigenesis but also may identify new diagnostic and prognostic markers as well as potential targets for therapeutic intervention.

Gene Expression Profiling↗

Exploring the Effect of Whole-Genome Duplication on Salmonid LincRNA Repertoire.

Long intergenic non-coding RNAs (lincRNAs) are key epigenetic regulators of genome function, yet their evolutionary dynamics following whole-genome duplication (WGD) events remain poorly understood. Salmonids, which underwent a lineage-specific autotetraploidization (salmonid-specific WGD, ~88-100 million years ago), provide an excellent model to investigate the retention, divergence, and functional potential of recently duplicated non-coding elements. LincRNA repertoires were compared across five genome-annotated salmonids (Oncorhynchus tshawytscha, O. kisutch, O. mykiss, Salmo salar, and S. trutta) and their closest non-duplicated relative, northern pike (Esox lucius). LincRNAs represented ~5-7% of annotated genes in all salmonids except S. salar (18%). Sequence conservation was low relative to coding genes, with only 11-68 highly similar (e-value < 1 &#xd7; 10-30; similarity > 70% and alignments > 100 nucleotides) putative orthologues shared between salmonids and northern pike, and 161-338 among salmonids alone. Synteny conservation was modest in lincRNAs, with lower conservation in putative orthologues (8-16%) compared to putative ohnologues (8-33%). Secondary structure conservation was associated with sequence similarity (&#x3c1; = -0.45; p = 2.2 &#xd7; 10-16), and the association was stronger among WGD ohnologues than orthologues. In S. salar and O. mykiss, lincRNA putative ohnologues showed weaker expression correlations than coding genes, suggesting widespread regulatory divergence, possibly through neo- and subfunctionalisation. Conserved salmonid lincRNAs showed enriched predicted interactions with miRNAs involved in tumour suppression, brain, bone, and muscle development (e.g., miR-455, miR-365, miR124, miR-133a, miR-140, and miR-9), a finding supported by limited transcriptomic data. Although salmonid WGD expanded lincRNA repertoires, lincRNAs have undergone rapid sequence and transcriptional divergence, with limited conservation across species based on sequence similarity, chromosomal position, synteny, and secondary structure. A subset of conserved lincRNAs retains structural features and regulatory signatures consistent with roles as miRNA sponges in brain, skeletal, and muscle development and tumour suppression, potentially acting within conserved regulatory networks. These findings provide new insights into lincRNA evolution following genome duplication and highlight the need for experimental validation of their regulatory functions.

Animals↗

A Comprehensive Bioinformatics Approach to Analysis of Variants: Variant Calling, Annotation, and Prioritization.

Next-Generation Sequencing (NGS), also known as high-throughput sequencing technologies, has enabled rapid and efficient sequencing of large amounts of DNA and RNA. These technologies have revolutionized the field of genomics, transcriptomics, and proteomics and have been widely used in cancer research, leading to advances in clinical diagnosis and treatment. Improvements in the NGS technologies enabled millions of fragments to be sequenced simultaneously in a time- and cost-effective manner and resulted in large amount of genomic data which require efficient analysis methods. Analysis of the genomic data requires both efficient computer resources and bioinformatics approaches. This chapter details a comprehensive computational approach and analysis steps for genomic data analysis.

Computational Biology↗

The transcriptome and its translation during recovery from cell cycle arrest in Saccharomyces cerevisiae.

Complete genome sequences together with high throughput technologies have made comprehensive characterizations of gene expression patterns possible. While genome-wide measurement of mRNA levels was one of the first applications of these advances, other important aspects of gene expression are also amenable to a genomic approach, for example, the translation of message into protein. Earlier we reported a high throughput technology for simultaneously studying mRNA level and translation, which we termed translation state array analysis, or TSAA. The current studies test the proposition that TSAA can identify novel instances of translation regulation at the genome-wide level. As a biological model, cultures of Saccharomyces cerevisiae were cell cycle-arrested using either alpha-factor or the temperature-sensitive cdc15-2 allele. Forty-eight mRNAs were found to change significantly in translation state following release from alpha-factor arrest, including genes involved in pheromone response and cell cycle arrest such as BAR1, SST2, and FAR1. After the shift of the cdc15-2 strain from 37 degrees C to 25 degrees C, 54 mRNAs were altered in translation state, including the products of the stress genes HSP82, HSC82, and SSA2. Thus, regulation at the translational level seems to play a significant role in the response of yeast cells to external physical or biological cues. In contrast, surprisingly few genes were found to be translationally controlled as cells progressed through the cell cycle. Additional refinements of TSAA should allow characterization of both transcriptional and translational regulatory networks on a genomic scale, providing an additional layer of information that can be integrated into models of system biology and function.

Cell Cycle↗

Renal transcriptomes: segmental analysis of differential expression.

BACKGROUND/AIMS: Progress accomplished by complete genomes and cDNA-sequencing projects calls for methods that fully use these resources to study gene expression patterns in characterized cell populations. However, since the number of functional genes cannot be readily inferred from the genomic sequence, it is highly desirable to make use of methods enabling to study both known and unknown genes. METHODS: The method of serial analysis of gene expression provides short diagnostic cDNA tags without bias towards known genes. In addition, the frequency of each tag in the library conveys quantitative information on gene expression. A microassay was set-up to perform serial analysis of gene expression in minute samples such as those obtained by microdissecting nephron segments. RESULTS: Studies carried out in the thick ascending limb of Henle's loop and the collecting duct of the mouse kidney provided expression data for several thousand genes. Known markers were found appropriately enriched, and several of the thick ascending limb or collecting duct specific transcripts had no database match. CONCLUSIONS: The microassay for serial analysis of gene expression makes possible large-scale quantitative measurements of mRNA levels in nephron segments. The comprehensive picture generated by analyzing both known and unknown transcripts in defined cell populations should help to discover genes with dedicated functions.

Animals↗

The genexpress IMAGE knowledge base of the human muscle transcriptome: a resource of structural, functional, and positional candidate genes for muscle physiology and pathologies.

Sequence, gene mapping, and expression data corresponding to 910 genes transcribed in human skeletal muscle have been integrated to form the muscle module of the Genexpress IMAGE Knowledge Base. Based on cDNA array hybridization, a set of 14 transcripts preferentially or specifically expressed in muscle have been selected and characterized in more detail: Their pattern of expression was confirmed by Northern blot analysis; their structure was further characterized by full-insert cDNA sequencing and cDNA extension; the map location of the corresponding genes was refined by radiation hybrid mapping. Five of the 14 selected genes appear as interesting positional and functional candidate genes to study in relation with muscle physiology and/or specific orphan muscular pathologies. One example is discussed in more detail. The expression profiling data and the associated Genexpress Index2 entries for the 910 genes and the detailed characterization of the 14 selected transcripts are available from a dedicated Web server at. The database has been organized to provide the users with a working space where they can find curated, annotated, integrated data for their genes of interest. Different navigation routes to exploit the resource are discussed.

Base Sequence↗

Genomic and Immune Landscape of Pancreatic Ductal Adenocarcinoma Associated with Germline Pathogenic Variants in ATM.

PURPOSE: Germline pathogenic variants (PV) in ATM increase the risk of pancreatic ductal adenocarcinoma (PDAC), but the underlying tumor biology of PDAC associated with germline PV in ATM has not been adequately explored. EXPERIMENTAL DESIGN: Whole-genome, whole-exome, and RNA sequencing were performed on PDAC tumors from 25 germline ATM PV carriers diagnosed at Mayo Clinic between 2007 and 2017. Somatic and copy-number alterations, mutational signatures, transcriptomic subtypes, and the immune landscape were evaluated. RESULTS: High-quality whole-exome and whole-genome sequencing were obtained from 21 and 15 tumors, respectively. Biallelic inactivation of ATM was observed in 87%, KRAS PV in 90%, CDKN2A homozygous loss in 60%, and TP53 alterations in <10% of these tumors. A predominant clock-like mutational signature was present in all samples. Whole-transcriptome analysis identified that the aberrantly differentiated endocrine exocrine subtype accounted for 18% of PDAC and was consistently associated with >5-year overall survival. In addition, a 28-gene expression-based signature associated with overall survival was identified and further validated in The Cancer Genome Atlas cohort. Immune landscape analysis through CODEX identified enriched CD4 T-helper cell/tumor interactions and reduced B7H3-high cell/tumor interactions in ATM PV carriers compared with noncarriers. CONCLUSIONS: The observed absence of TP53 PV and enrichment for CDKN2A alterations in ATM tumors, along with differences in the mutational signatures, transcriptomic subtypes and immune landscape, improve our understanding of the mechanistic pathways involved in PDAC development in germline ATM PV carriers and help identify potential targeted therapeutic strategies.

Humans↗

Expression regulation network in papillae of sea cucumbers: Whole-transcriptome and DNA methylation datasets.

To elucidate the expression regulation network of papilla size of sea cucumbers (Apostichopus japonicus), the whole-transcriptome and DNA methylome datasets of different sizes of papillae in sea cucumbers were generated. Average clean bases of whole-transcriptome (16.35&#x2009;G) and DNA methylome (28.92&#x2009;G) were obtained using RNA sequencing and whole-genome bisulfite sequencing techniques. A total of 3,188 ceRNA networks were also identified including 3,081 long non-coding RNAs (lncRNA)/microRNAs (miRNA)/mRNA networks and 107 circular RNA (circRNA)/miRNA/mRNA networks. Methylome data indicate that there were 3,307 and 3,776 differentially methylated regions (DMRs) with high-level methylation as well as 3,125 and 3,016 DMRs with low-level methylation in big papillae compared to small papillae. The identified DMRs were mainly distributed in introns, promotors, or exons. The whole-transcriptome and DNA methylome datasets generated from this study not only established a robust theoretical foundation (especially from the epigenetic aspect) for elucidating expression regulation network determining papilla size in sea cucumbers but also can be a valuable resource of biomarker mining for papilla appearance-based selective breeding in sea cucumbers.

DNA Methylation↗

Comparative transcriptomic analysis of the gills and hepatopancreas of freshwater-cultured Litopenaeus vannamei under chronic nitrite stress.

To investigate the differences in molecular responses between the gills and hepatopancreas of freshwater-cultured Litopenaeus vannamei under chronic nitrite stress, a 30-day chronic stress experiment was conducted with a control group and a stress group. Transcriptomic analysis of the gills and hepatopancreas was performed using Illumina sequencing; differentially expressed genes (DEGs) were identified, and GO, KEGG, GSEA, PPI, and RT-qPCR validation were carried out. The results showed that 196 DEGs (161 up-regulated and 35 down-regulated) were identified in the gills, and 287 DEGs (199 up-regulated and 88 down-regulated) in the hepatopancreas, with only 18 DEGs shared between the two tissues. DEGs in the gills were enriched in oxidoreductase activity, glycerophospholipid metabolism, and tyrosine metabolism; DEGs in the hepatopancreas were enriched in lipid transporter activity, phagosome, ECM-receptor interaction, and riboflavin metabolism. GSEA revealed significant suppression of the mTOR pathway in the gills and the Polycomb complex pathway in the hepatopancreas. PPI network analysis identified hub genes P5CS and eEF2 in the gills, and PER, TUBB1, SHMT, and TUBB4B in the hepatopancreas. RT-qPCR validation was consistent with the RNA-seq results (R2&#xa0;=&#xa0;0.764). This study indicates that, under chronic nitrite stress, the gill response is centered on redox regulation and inhibition of growth metabolism, whereas the hepatopancreas response primarily involves lipid transport, cytoskeletal remodeling, and phagosome activation. The two tissues synergistically adapt through fundamental biosynthetic and motor protein pathways. This research provides molecular evidence for deciphering the nitrite tolerance mechanisms in freshwater-cultured shrimp.

Animals↗

A multi-modal survival prediction framework with group-based batch training and structural consistency alignment.

OBJECTIVE: Integrating whole-slide images (WSIs) with transcriptomic profiles is pivotal for enhancing cancer survival prediction. However, the intrinsic gigapixel resolution and variable sequence lengths of WSIs create a fundamental trade-off between training efficiency and the preservation of data heterogeneity in existing frameworks. Furthermore, substantial statistical and structural discrepancies between histological and genomic modalities often impede effective cross-modal alignment and fusion, thereby limiting prognostic accuracy. METHODS: We propose PRISM, an efficient multi-modal learning framework for integrating WSIs with transcriptomic profiles. To reconcile training efficiency with full data heterogeneity, PRISM first stochastically partitions variable-length WSI sequences into a main subset and a complementary residual subset, both of which are packed into fixed-length groups for batch training. The main subset is processed in the main branch, utilizing isolation masking to maintain intra-group sequence independence. Simultaneously, the residual subset is consolidated into "hyperslides" within a residual branch that leverages tailored supervision, effectively capturing inter-slide correlations. Furthermore, PRISM integrates an Informative Token Aggregation (ITA) module to reduce redundancy in WSIs and employs Cross-batch Structural Consistency Alignment (CBSCA) mechanism to enhance inter-modal structural connectivity. Finally, efficient cross-modal feature interaction is achieved through a Low-rank Bilinear Gated Fusion (LBGF) module. Code is available at https://github.com/Alisa2080/PRISM. RESULTS: Compared with existing methods, PRISM achieves the best overall C-index across five TCGA cohorts. On the larger TCGA-BRCA dataset, PRISM requires only 6&#xa0;hours of training time, substantially reducing computational cost relative to strong multimodal baselines. Furthermore, comprehensive evaluations demonstrate that PRISM achieves the best overall IBS ranking and favorable time-dependent AUC performance at 1, 3, and 5&#xa0;years, thereby delivering a more favorable trade-off between prognostic performance and computational efficiency. CONCLUSION: PRISM provides a favorable balance between predictive performance, calibration quality, and computational efficiency, highlighting its potential for practical deployment in multimodal survival modeling for computational pathology.

Humans↗

High MGMT expression identifies aggressive colorectal cancer with distinct genomic features and immune evasion properties.

INTRODUCTION: The epigenetic silencing of O6-methylguanine DNA methyltransferase (MGMT) is associated with reduced DNA repair capacity, carcinogenesis and increased sensitivity to alkylating chemotherapy. However, the biological role and clinical significance of MGMT overexpression in cancer remains poorly understood. METHODS: Using multiplexed quantitative immunofluorescence we measured the localized levels of MGMT protein, &#x3b3;H2AX and CD8+ T&#x2009;cells in multiple retrospective colorectal cancer (CRC) cohorts. Genomic and transcriptomic features of selected cases were also studied with whole exome DNA sequencing and genome-wide methylation analysis. MGMT-methylated human CRC cells SW620 were transfected with an MGMT-containing plasmid and co-cultured with allogeneic peripheral blood mononuclear cells. RESULTS: A subset of CRCs showed MGMT protein upregulation associated with lower &#x3b3;H2AX, reduced CD8+ tumor infiltrating lymphocytes (TILs), mismatch repair proficient (pMMR) status and shorter survival. CD8+ TILs were more distant from MGMT-expressing cells than MGMT-negative cells and the MGMT promoter methylation status did not highly correlate with MGMT protein levels in CRC. In genomic/transcriptomic analysis, high MGMT expression was associated with a lower nonsynonymous somatic mutational burden, higher transition-to-transversion mutation ratio, increased deleterious TP53 variants and distinct transcriptomic profiles. The exogenous expression of MGMT in SW620 CRC cells reduced the number of spontaneous nonsynonymous mutations, reproduced mutational features of MGMT-high CRC and limited the in vitro T-cell-mediated killing of malignant cells induced by proinflammatory cytokines in tumor/immune cell co-cultures. CONCLUSIONS: MGMT overexpression identifies a previously undescribed subset of CRCs with distinct biological and clinical properties including reduced mutagenesis, adaptive immune evasion, predominantly pMMR phenotype and aggressive clinical course. Direct, quantitative assessment of MGMT protein expression using spatially resolved analysis is more reliable than inference of MGMT expression by promoter methylation status in CRC.

Humans↗