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PubMed2026

Lactylation-related immune-metabolic dysregulation defines prognostic and therapeutic stratification in lung adenocarcinoma.

BACKGROUND: Lactylation links lactate metabolism with inflammatory signaling and immune regulation in tumors. However, its cellular distribution and translational value in lung adenocarcinoma (LUAD) remain unclear. METHODS: Single-cell RNA-sequencing datasets GSE189357 and GSE171145 were integrated to characterize lactylation-related activity, intercellular communication, and malignant epithelial cell states in LUAD. Single-cell-derived lactylation-related differentially expressed genes were mapped to TCGA-LUAD and multiple GEO cohorts. Univariate Cox regression and machine learning algorithms were used to construct a lactylation-related prognostic signature (LRPS). The associations of LRPS with prognosis, immunotherapy response, drug sensitivity, genomic alterations, immune infiltration, and inflammation- and metabolism-related pathways were evaluated. KRT7 was further validated using virtual knockout analysis, spatial transcriptomics, and in vitro and in vivo experiments. RESULTS: lactylation-related transcriptional activity showed heterogeneous distribution across LUAD cell populations and was associated with altered cell-cell communication. In malignant epithelial cells, LRTS-high and LRTS-low states exhibited distinct metabolic, inflammatory, and tumor-related pathway activities. LRPS showed stable prognostic performance in TCGA-LUAD and multiple GEO cohorts and remained an independent prognostic factor. Low LRPS was associated with greater potential benefit from immunotherapy, whereas different LRPS groups displayed distinct drug sensitivity, genomic alteration, and immune microenvironment patterns. KRT7 was highly expressed in LUAD and associated with poor prognosis. KRT7 knockdown suppressed LUAD cell proliferation, migration, invasion, colony formation, and tumor growth in vivo. CONCLUSIONS: This study identifies lactylation-related immune-metabolic dysregulation as a clinically relevant feature of LUAD and develops a single-cell-guided LRPS for prognosis and therapeutic stratification. KRT7 emerged as an LRPS-related functional candidate with experimentally supported roles in malignant LUAD phenotypes.

Immunotherapy↗

PubMed1996

Solution structure of the CpG containing d(CTTCGAAG)2 oligonucleotide: NMR data and energy calculations are compatible with a BI/BII equilibrium at CpG.

We report the analysis of the solution structure of the DNA duplex d(CTTCGAAG)2 compared to that of d(CATCGATG)2, the two oligonucleotides being related by the permutation of residues 2 and 7. An earlier study has demonstrated the malleability of CpG in the tetrad TCGA of d(CATCGATG)2 [Lefebvre et al. (1995) Biochemistry 34, 12019-12028]. Conformations of d(CTTCGAAG)2 were evaluated by (a) two-dimensional NMR, including proton and phosphorus experiments, (b) adiabatic mapping of the conformational space, (c) restrained molecular mechanics undertaken with sugar phase angle, epsilon-zeta difference angle, and NOE distances as input, and (d) back-calculation-refinement against NOE spectra at various mixing times. d(CTTCGAAG)2 like d(CATCGATG)2 exhibits a B-DNA conformation. However, significant differences are noted between the two oligonucleotides, extending up to the central CpG step, although this step resides in the same TCGA tetrad in both sequences. In structures obtained with refined NMR data, CpG adopts, for instance, a greater twist and a higher guanine phase within d(CTTCGAAG)2 compared to d(CATCGATG)2. In the former oligonucleotide, the structure of CpG resembles strikingly that found in the ACGT tetrad of the cAMP responsive element [Mauffret et al. (1992) J. Mol. Biol. 227, 852-875]. Moreover, two conformers with CpG either in the BII state (epsilon, zeta = g-, t) or in the BI state (epsilon, zeta = t, g-) are found equally stable for d(CTTCGAAG)2. The energy barrier from BI to BII comes to only 5.7 kcal/mol, and the path of the transition is very short. When calculations on d(CTTCGAAG)2 are performed taking the BI/BII equilibrium into account, the agreement with both the 1H and 31P data is found better than in the case with a single conformation taken alone. The BI/BII equilibrium may also occur in d(CATCGATG)2, but the amount of BII conformer is now found weaker compared to its analogue. The ability of the CpG phosphate groups to adopt the BII conformation could provide a satisfying explanation for the high mutation rates observed at these sites.

CpG Islands↗

PubMed1999

Binding kinetics and footprinting of TaqI endonuclease: effects of metal cofactors on sequence-specific interactions.

Restriction endonucleases achieve sequence-specific recognition and strand cleavage through the interplay of base, phosphate backbone, and metal cofactor interactions. In this study, we investigate the binding kinetics of TaqI endonuclease using the wild-type enzyme and a binding proficient, catalysis deficient mutant TaqI-D137A both in the absence of a metal cofactor and in the presence of Mg2+ or Ca2+. As demonstrated by gel mobility shift analyses, TaqI endonuclease requires a metal cofactor for achieving high-affinity specific binding to its cognate sequence, TCGA. In the absence of a metal cofactor, the enzyme binds all DNA sequences (TaqI cognate site, star site, and nonspecific site) with essentially equal affinity, thereby exhibiting little discrimination. The dissociation constant of the cognate sequence in the presence of Mg2+ at 60 degrees C is 0. 26 nM, a value comparable to our previously reported Km of 0.5 nM measured under steady-state conditions. The TaqI-TCGA-Mg2+ complex is stable, with a half-life of 21 min at 60 degrees C. The boundary of the protein-DNA interface is approximated to be about 18 bp as determined by DNase I footprinting. Data from this study support the notion that a metal cofactor plays a critical role for achieving sequence-specific discrimination in a subset of nucleases, including TaqI, EcoRV, and others.

Base Sequence↗

PubMed2026

Biallelic loss of RB1 in hepatocellular carcinoma as synthetic lethal target for artificial intelligence-guided therapy.

The retinoblastoma (RB1) gene is a critical tumor suppressor that regulates cell cycle progression and genomic stability. Although RB1 alterations have been reported in hepatocellular carcinoma (HCC), the biological and clinical consequences of biallelic RB1 inactivation (RB1-Bi) remain poorly defined. We performed a comprehensive allele-specific genomic analysis of HCC patients from the TCGA-LIHC (n = 355) and in-house AMC (n = 206) cohorts, collectively comprising the AMC-TCGA discovery cohort. In this combined cohort, RB1-Bi was identified in 14.6% of tumors, was enriched in poorly differentiated HCCs and was independently associated with significantly reduced overall survival (adjusted hazard ratio 3.32, 95% CI 1.93-5.72, p < 0.001). Additionally, a deep learning-based histopathology model using hematoxylin and eosin-stained slides (i.e., FR-MIL model) accurately predicted RB1-Bi status (F1 score 84.39% [95% CI, ±0.02]), making it readily identifiable in routine clinical practice. The prevalence and prognostic impact of RB1-Bi, as well as FR-MIL model performance, were consistent across independent validation cohorts, including advanced-stage tumors and external institutions. High-throughput drug screening in isogenic HCC models revealed that RB1-Bi HCC cells were particularly sensitive to inhibitors targeting mitotic regulators (e.g., AURKA, PLK1, KSP) and DNA damage response pathways (e.g., PARP inhibitors). Synthetic lethal interactions between RB1-Bi and these compounds were demonstrated in vitro and in vivo, and combination treatment with mitotic and PARP inhibitors had synergistic effects with acceptable tolerability. We conclude that RB1-Bi represents a clinically actionable biomarker that identifies a high-risk HCC subtype with specific therapeutic vulnerabilities, offering new opportunities for precision medicine.

Humans↗

PubMed2025

Transfer learning with multiomics integration and deep neural networks reveals drug resistance mechanisms in cancer.

Drug resistance remains one of the primary challenges in effective cancer therapy. In this study, we employed a deep neural network (DNN)-based transfer learning (TL) approach to predict drug response and uncover drug resistance mechanisms. We integrated gene expression, somatic mutation, and copy number aberration (CNA) data with drug response profiles using multi-omics integration (MI). We used the Genomics of Drug Sensitivity in Cancer (GDSC) data for training and incorporated drugs with same pathways into the training models. We then evaluated drug response predictions on independent in-vivo PDX Encyclopedia (PDX) and ex-vivo the Cancer Genome Atlas (TCGA) datasets. In addition, we conducted pathway enrichment analyses to elucidate the mechanisms underlying drug resistance for paclitaxel, 5-fluorouracil (5-FU), gemcitabine, and cetuximab. We also applied Fisher's exact test (FET) to assess potential associations between drug resistance and the presence of mutations or CNAs. Our pan-drug models outperformed other methods based on the area under the precision-recall curve (AUCPR). Our pathway enrichment analyses revealed LDHB-mediated pyruvate metabolism and FYN-mediated focal adhesion might have pivotal roles in paclitaxel resistance, while PINK1-mediated mitophagy might be critical in 5-FU resistance. In addition to transcriptional activation, FET suggested that CNAs in LDHB and PINK1 may also be associated with resistance to paclitaxel and 5-FU, respectively. Furthermore, enrichment results for paclitaxel and cetuximab indicated shared resistance mechanisms between the two drugs. Importantly, our findings are consistent with prior experimental studies, providing literature-based validation of our results. Overall, our DNN-based TL approach achieved strong predictive performance across PDX & TCGA datasets and enrichment analyses provided valuable biological insights into drug resistance mechanisms.

Humans↗

PubMed2025

The offonome reveals on and off states of gene expression near the detection limit of RNA-seq.

RNA-seq, widely used for gene expression profiling, provides nucleotide level genome coverage and summary gene expression values. Generally, low-expressed genes are ignored due to their unfavorable signal-to-noise ratio, however, these genes may offer crucial information, such as detecting rare cells in bulk tissues. In this study, we applied an approach that transforms the expression levels of low-expressed genes into a robust dichotomized on/off state by leveraging similarities in transcript coverage shape. Applied to three human cancer cohorts from the Cancer Genome Atlas (TCGA), chosen based on tissue morphology and anatomic site, we identified genes, the "offonome" near the detection limit, consistently or occasionally off across samples. Genes in the offonome spectrum proved useful for supervised and unsupervised applications, including characterizing oncogenic pathways, and identifying rare populations of cells in bulk tissue. Interrogating the offonome is relevant to bulk tumor analyses like TCGA, potentially expediting gene investigation in low-input situations like single cell RNA-seq.

Humans↗

PubMed1994

Localized chemical reactivity in DNA associated with the sequence-specific bisintercalation of echinomycin.

Four complementary footprinting and probing techniques utilizing DNAse I, methidiumpropyl EDTA (MPE).FeII, diethyl pyrocarbonate (DEPC) and KMnO4 as DNA-cleaving or DNA-modifying agents have been applied to investigate the sequence-specific binding to DNA of the antitumour antibiotic echinomycin. A 265 bp EcoRI-PvuII DNA restriction fragment excised from plasmid pBS was used as a substrate. Six regions of protection against DNAase I cleavage were located on the 265-mer: three sites encompass the sequences 5'-TCGA or 5'-GCGT and the three others contain 5'-GpG (CpC) dinucleotide sequences where the inhibition of DNAase I cutting by echinomycin is less pronounced. In contrast, MPE.FeII cleavage allows identification of only three echinomycin-binding sites on the 265-mer: two sites contain the sequence 5'-TCGA and one encompasses the sequence 5'-ACCA. Cleavage of DNA by MPE.FeII in the presence of echinomycin remains practically unaffected at the sequence 5'-GCGT, despite its identification by DNAase I as a strong site for binding the antibiotic, as well as at the two other sequences containing GpG steps. With both DNAase I and MPE.FeII, enhanced DNA cleavage is evident at AT-rich sequences in the presence of echinomycin. Enhanced reactivity towards KMnO4 and DEPC provides clear evidence for sequence-dependent conformational changes in DNA induced by the antibiotic. The experiments reveal that KMnO4 reacts most strongly with thymines located around, but not necessarily adjacent to, an echinomycin-binding site, whereas the carbethoxylation reactions caused by DEPC occur primarily at the adenine residues lying immediately 5' or 3' to the dinucleotide that denotes an echinomycin-binding site. The results reported here demonstrate that DEPC and KMnO4 serve as sensitive probes for different states of the DNA helix. It seems that the reaction with KMnO4 involves transient unstacking events, whereas the carbethoxylation reaction of DEPC requires larger-scale helix opening.

Base Sequence↗

PubMed2025

Identification of a prognostic signature consisting of three macrophage-related genes for glioblastoma based on bulk and single-cell transcriptomes analyses.

BACKGROUND: Tumor-associated macrophages have been implicated in the progression and treatment resistance of glioblastoma (GBM). This study aimed to identify macrophage-related genes associated with prognosis and therapeutic response in GBM. MATERIALS AND METHODS: Bulk RNA-seq data from 533 patients with GBM were downloaded from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Bioinformatic tools were used to detect the co-expression gene modules associated with the infiltration of immune cells, identify a prognostic macrophage-related gene signature, and explore their association with sensitivity to chemotherapeutic drugs and immune checkpoint blockade. Single-cell RNA-seq data and multiplexed immunofluorescence were used to validate ISG20 expression (a member of the identified gene signature) in macrophages. RESULTS: We detected gene modules associated with macrophages and identified a signature consisting of three macrophage-related genes (ISG20, PARP12 and IFIT5) in the discovery set (TCGA-GBM, n = 159), and validated its prognostic value in the validation set (CGGA-GBM, n = 374). This gene signature demonstrated favorable accuracy in predicting prognosis and resistance of immuno- and chemo-therapy. The co-expression of ISG20 and PD-1 in macrophages was verified by single-cell RNA-seq data and multiplex immunofluorescence. CONCLUSIONS: This study presents a macrophage-related gene signature to predict prognosis and therapeutic response in GBM. ISG20, PARP12 and IFIT5 are interferon-stimulated genes, and further investigations may provide new insights into the interplay between macrophages and interferon signaling in GBM.

Humans↗

PubMed2026

X-intNMF: a cross- and intra-omics regularized NMF framework for multi-omics integration.

MOTIVATION: The rapid accumulation of multi-omics data presents a valuable opportunity to advance our understanding of complex diseases and biological systems, driving the development of integrative computational methods. However, the complexity of biological processes, spanning multiple molecular layers and involving intricate regulatory interactions, requires models that can capture both intra- and cross-omics relationships. Most existing integration methods primarily focus on sample-level similarities or intra-omics feature interactions, often neglecting the interactions across different omics layers. This limitation can result in the loss of critical biological information and suboptimal performance. To address this gap, we propose X-intNMF, a network-regularized non-negative matrix factorization (NMF) framework that simultaneously integrates intra- and cross-omics feature interactions into a shared low-dimensional representation (see Fig. 1). By modeling these multi-layered relationships, X-intNMF enhances the representation of biological interactions and improves integration quality and prediction accuracy. RESULTS: For evaluation, we applied X-intNMF to predict breast cancer phenotypes and classify clinical outcomes in lung and ovarian cancers using mRNA expression, microRNA expression, and DNA methylation data from TCGA. The results show that X-intNMF consistently outperforms state-of-the-art methods. Ablation studies confirm that incorporating both cross-omics and intra-omics interactions contributes significantly to the model's improved performance. Additionally, survival analysis on 25 TCGA cancer datasets demonstrates that the integrated multi-omics representation offers strong prognostic value for both overall survival and disease-free status. These findings highlight X-intNMF's ability to effectively model multi-layered molecular interactions while maintaining interpretability, robustness, and scalability within the NMF framework. AVAILABILITY AND IMPLEMENTATION: The source code and datasets supporting this study are publicly available at GitHub (https://github.com/compbiolabucf/X-intNMF) and archived on Zenodo (https://doi.org/10.5281/zenodo.18238385).

Multiomics↗

PubMed2026

GRNContext: an interactive web platform for contextualized gene regulatory networks visualization across human cancers.

SUMMARY: While current Gene Regulatory Network (GRN) databases provide comprehensive reference maps of potential interactions between transcription factors and target genes, they do not specify which regulatory interactions are active within specific biological contexts. This limitation is particularly critical in cancer, where transcriptional programs are inherently tissue-specific. To address this gap, we developed GRNContext, an interactive web platform designed for the visualization, exploration, and comparative analysis of gene regulatory networks contextualized across 33 cancer types from The Cancer Genome Atlas (TCGA). Our approach uses the TFLink human reference GRN as a starting point and integrates TCGA transcriptomic profiles to infer cancer-specific regulatory activity. Regulatory relevance was assessed using complementary machine learning and statistical methods, which were unified into a consensus score to prioritize and filter the most relevant candidate regulators for each target gene. By providing both curated context-specific GRNs and a user-friendly platform, GRNContext constitutes a comprehensive and accessible resource that supports mechanistic investigations, hypothesis generation, and translational research focused on transcriptional regulation in cancer. AVAILABILITY AND IMPLEMENTATION: GRNContext is supported by all major browsers and freely available on the web at https://apps.cienciavida.org/grncontext. It is implemented as a client-server web application featuring a FastAPI backend and a React frontend utilizing Cytoscape.js for interactive network visualization, all containerized via Docker for cross-platform compatibility.

Humans↗

PubMed2026

POU2F3 expression in lung squamous cell carcinoma: transcriptomic and immunohistochemical profiling with prognosis.

BACKGROUND: Lung squamous cell carcinoma (LUSC) lacks well-defined molecular targets. This study investigated the clinical and biological relevance of POU class 2 homeobox 3 (POU2F3), a tuft cell-associated transcription factor, in LUSC. METHODS: RNA sequencing data of patients with LUSC from The Cancer Genome Atlas (TCGA cohort, n = 190) was analysed and compared to a cohort of surgically resected cases analyzed via immunohistochemistry (IHC cohort, n = 137). Prognostic impact was assessed via survival analyses. Transcriptomic features, pathway enrichment, and immune profiles were evaluated via differentially expressed gene analysis, Gene Set Enrichment Analysis, and CIBERSORTx. RESULTS: High POU2F3 expression independently predicted poor overall survival in the TCGA cohort (HR = 2.06, 95% CI: 1.04-4.08, P = 0.039). In contrast, POU2F3 expression was not prognostic in the IHC cohort (P = 0.995). Morphologically, POU2F3-positive tumours were enriched for non-keratinizing and poorly differentiated subtypes. Transcriptomic analysis showed suppression of proliferation and immune-related pathways (FDR < 0.001), with suggestive enrichment of the TGF-β (FDR = 0.143) and p53 (FDR = 0.229) signaling pathways. On immune deconvolution, POU2F3-high tumours showed a nominal increase in activated dendritic cells, which did not withstand multiple testing correction. POU2F3 protein was detected in 12.4% of tumours and was significantly associated with p53 or RB1 abnormalities (single or double) (P = 0.028). CONCLUSIONS: POU2F3 marks a transcriptionally distinct, early-stage subtype of LUSC with keratinization-related features. Its prognostic relevance appears context-dependent and requires prospective validation in uniformly treated cohorts.

Humans↗

PubMed2026

DNA copy number patterns reveal prognostic markers and elucidate mechanisms of evolution in IDH-mutant astrocytoma.

BACKGROUND: Current literature suggestsisocitrate dehydrogenase (IDH)-mutant astrocytoma contains several molecular subgroups. In this study, we are interested in determining the connection between different molecular subgroups with grade and/or survival. METHODS: A cohort of 470 Mayo Clinic adult patients (≥18 years, 56.2% male) with primary IDH-mutant astrocytoma diagnosed by World Health Organization (WHO) 2021 criteria were examined. Results were validated in an independent cohort of 614 Mayo Clinic Neuropathology consult patients and 235 The Cancer Genome Atlas (TCGA) patients. RESULTS: The Mayo Clinic Practice cohort confirmed the association of CDKN2A/B deletion with overall survival (OS, homozygous vs hemizygous vs intact, 2.7 vs 9.6 vs 17.2 years, P < .001). Phosphatase and tensin homolog (PTEN) deletion was also associated with poor OS (7.3 vs 17.4 years, P < .001). Increased number of copy number alterations was associated with OS (continuous variable, HR = 1.027, P < .001). Carrying one or more copies of the germline risk allele at rs55705857 was associated with earlier age of onset (median age 33 vs 35 years, P = .01), and a shorter OS after adjusting for age, grade, sex and treatment (HR = 1.81, P = .007). The Mayo Clinic Neuropathology Consult cohort and TCGA were utilized to validate age of onset and survival, respectively. Unsupervised clustering of the copy number alterations identified several clinically significant groups that may define pathways to disease progression. Losses of chromosomes 11p, 13q, 1p, and 10q were all associated with reduced overall survival in the Mayo Clinic cohort. CONCLUSIONS: Patients with hemizygous loss of CDKN2A/B, loss of PTEN, increased number of copy number alterations, specific chromosomal arm losses or rs55705857 germline risk allele have reduced overall survival.

Humans↗

PubMed1986

Nucleotide sequence and characteristics of the gene for L-lactate dehydrogenase of Thermus caldophilus GK24 and the deduced amino-acid sequence of the enzyme.

The gene for L-lactate dehydrogenase (LDH) (EC 1.1.1.27) of Thermus caldophilus GK24 was cloned in Escherichia coli using synthetic oligonucleotides as hybridization probes. The nucleotide sequence of the cloned DNA was determined. The primary structure of the LDH was deduced from the nucleotide sequence. The deduced amino acid sequence agreed with the NH2-terminal and COOH-terminal sequences previously reported and the determined amino acid sequences of the peptides obtained from trypsin-digested T. caldophilus LDH. The LDH comprised 310 amino acid residues and its molecular mass was determined to be 32,808. On alignment of the whole amino acid sequences, the T. caldophilus LDH showed about 40% identity with the Bacillus stearothermophilus, Lactobacillus casei and dogfish muscle LDHs. The T. caldophilus LDH gene was expressed with the E. coli lac promoter in E. coli, which resulted in the production of the thermophilic LDH. The gene for the T. caldophilus LDH showed more than 40% identity with those for the human and mouse muscle LDHs on alignment of the whole nucleotide sequences. The G + C content of the coding region for the T. caldophilus LDH was 74.1%, which was higher than that of the chromosomal DNA (67.2%). The G + C contents in the first, second and third positions of the codons used were 77.7%, 48.1% and 95.5% respectively. The high G + C content in the third base caused extremely non-random codon usage in the LDH gene. About half (48.7%) the codons in the LDH gene started with G, and hence there were relatively high contents of Val, Ala, Glu and Gly in the LDH. The contents of Pro, Arg, Ala and Gly, which have high G + C contents in their codons, were also high. Rare codons with U or A as the third base were sometimes used to avoid the TCGA sequence, the recognition site for the restriction endonuclease, TaqI. Two TCGA sequences were found only in the sequence of CTCGAG (XhoI site) in the sequenced region of the T. caldophilus DNA. There were three segments with similar sequences in the two 5' non-coding regions, probably the promoter and ribosome-binding regions, of the genes for the T. caldophilus LDH and the Thermus thermophilus 3-isopropylmalate dehydrogenase.

Amino Acid Sequence↗

PubMed2026

Deep learning techniques in predicting BRAF mutation status in cutaneous melanoma from histopathologic images.

AIMS: To develop and validate a deep learning framework for discriminating BRAF mutation status in cutaneous melanoma from routine H&E whole-slide images (WSIs) as a proof-of-concept complementary approach alongside molecular testing. METHODS: We built a two-stage pipeline comprising U-Net-based tumour segmentation followed by an Inception v3 classifier. In total, 272 institutional melanoma cases with confirmed BRAF status were used for model development (training and internal validation). Generalisability was assessed in an external test set of 76 cutaneous melanoma cases from the Cancer Genome Atlas (TCGA). Dermatopathologist-defined tumour-rich regions of interest were used to train and evaluate segmentation. WSIs were processed at 20×magnification using 512×512 tiles; slide-level mutation probabilities were obtained by averaging the predicted probabilities across all tumour-enriched tiles. RESULTS: Inception v3 achieved area under the receiver operating characteristic curve values of 0.973 (training), 0.954 (validation) and 0.915 (TCGA testing) and outperformed a ResNet50 baseline, showing stable external generalisation. Performance remained robust in advanced pathological T-category primary tumours (pT3-T4). Tumour probability heatmaps supported spatial interpretability by localising regions contributing most strongly to predicted mutation status. CONCLUSIONS: Deep learning applied to routine H&E WSIs can infer BRAF mutation status in cutaneous melanoma with consistent performance across institutional and external cohorts. Given the observed external sensitivity and negative predictive value, the model is not suitable for rule-out use or for deferring/omitting molecular testing. Any workflow integration is future work and would require prospective validation and calibration of probability outputs in real-world clinical series.

Artificial Intelligence↗

PubMed2026

The Genomic Landscape of MYC-, MYCL-, and MYCN-Amplified Solid Tumors.

PURPOSE: MYC, MYCN, and MYCL amplifications are recurrent oncogenic events across solid tumors. Currently, no standardized selection biomarker is available to identify patients with MYC-dependent tumors. EXPERIMENTAL DESIGN: We analyzed copy-number alterations of MYC family genes and their features in more than 68,000 tumor-normal paired samples from pediatric and adult patients sequenced with MSK-IMPACT (Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets) and annotated with FACETS (Fraction and Allele-Specific Copy Number Estimates from Tumor Sequencing). The relationship between amplification features and MYC mRNA expression levels were evaluated in more than 10,000 samples from The Cancer Genome Atlas (TCGA). RESULTS: Across MSK Cancer Center samples, MYC amplifications were most common, found in 2,949 samples compared with 310 in MYCL and 217 in MYCN. Although MYCN and MYCL amplifications were predominantly focal (<10 Mb, 79% and 93%, respectively), MYC amplifications were frequently broader (>10 Mb, 62%). Although most tumor types showed similar features between broad and focal amplifications of MYC, in select cancer types, we identified differing co-occurrence and mutual exclusivity patterns with other disease-specific drivers. Furthermore, although MYC-amplified TCGA samples showed higher mRNA expression than wild-type ones, the focality of MYC amplification was seen to have limited influence on expression levels. CONCLUSIONS: Our results suggest that MYC dependency likely depends on many factors, including, but not limited to, total copy number of the detected amplification, lineage-specific factors, concomitant presence or absence of additional oncogenic alterations, and in some cases amplification focality.

Humans↗

PubMed2025

COL5A1 in the tumor microenvironment predicts the prognosis of head and neck cancer.

ObjectivesThis study aims to investigate the significance of tumor microenvironment (TME)-related genes and signal transduction pathways in head and neck cancer (HNC).MethodsGene expression and clinical data of HNC patients were obtained from the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened through a multi-step filtration approach to obtain candidate predictors. The biological role of COL5A1 in HNC was verified through rigorous bioinformatic analysis, experimental validation using quantitative real-time PCR (qRT-PCR), immunohistochemical (IHC) analysis from HNC samples, and IHC data from the Human Protein Atlas (HPA) database.ResultsCOL5A1 was significantly upregulated in HNC tissues and cell lines. High COL5A1 expression was significantly associated with advanced tumor grade (P < .05) and shorter survival (TCGA: P < .001; GSE42743: P = .004). COL5A1 was an independent prognostic indicator (univariate analysis: HR = 1.324, P = .001; Multivariate analysis: HR = 1.326, P = .005). It was enriched in pathways related to tumor invasion and immune responses, and its expression was associated with decreased levels of CD8+ T cells and increased levels of macrophages and neutrophils. Spatial distribution analysis revealed higher expression at the tumor's leading edge (vs. tumor core: P < .001). COL5A1 expression is associated with tumor stage, with more pronounced expression in advanced-stage tumors.ConclusionCOL5A1 represented a novel potential prognostic indicator and therapeutic target in an HNC database sample, as its expression is closely linked to tumor progression, immune cell infiltration, and adverse clinical outcomes. These findings, primarily derived from squamous cell carcinoma-dominated cohorts, warrant further functional validation.

Humans↗

PubMed2025

Multi‑omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study.

BACKGROUND: Predictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell death (PCD) is increasingly recognized as a potential target in cancer therapy and prognosis. However, a PCD-based predictive model for serous ovarian carcinoma (SOC) is lacking. In the present study, we aimed to establish a cell death index (CDI)-based model using PCD-related genes. METHODS: We included 1254 genes from 12 PCD patterns in our analysis. Differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were screened. Subsequently, 14 PCD-related genes were included in the PCD-gene-based CDI model. Genomics, single-cell transcriptomes, bulk transcriptomes, spatial transcriptomes, and clinical information from TCGA-OV, GSE26193, GSE63885, and GSE140082 were collected and analyzed to verify the prediction model. RESULTS: The CDI was recognized as an independent prognostic risk factor for patients with SOC. Patients with SOC and a high CDI had lower survival rates and poorer prognoses than those with a low CDI. Specific clinical parameters and the CDI were combined to establish a nomogram that accurately assessed patient survival. We used the PCD-genes model to observe differences between high and low CDI groups. The results showed that patients with SOC and a high CDI showed immunosuppression and hardly benefited from immunotherapy; therefore, trametinib_1372 and BMS-754807 may be potential therapeutic agents for these patients. CONCLUSIONS: The CDI-based model, which was established using 14 PCD-related genes, accurately predicted the tumor microenvironment, immunotherapy response, and drug sensitivity of patients with SOC. Thus this model may help improve the diagnostic and therapeutic efficacy of PPPM.

Humans↗

PubMed2025

Transcriptome-wide analysis reveals potential roles of CFD and ANGPTL4 in fibroblasts regulating B cell lineage for extracellular matrix-driven clustering and novel avenues for immunotherapy in breast cancer.

BACKGROUND: The remodeling of the extracellular matrix (ECM) plays a pivotal role in tumor progression and drug resistance. However, the compositional patterns of ECM in breast cancer and their underlying biological functions remain elusive. METHODS: Transcriptome and genome data of breast cancer patients from TCGA database was downloaded. Patients were classified into different clusters by using non-negative matrix factorization (NMF) based on signatures of ECM components and regulators. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify core genes related to ECM clusters. Additional 10 independent public cohorts including Metabric, SCAN_B, GSE12276, GSE16446, GSE19615, GSE20685, GSE21653, GSE58644, GSE58812, and GSE88770 were collected to construct Training or Testing cohort, following machine learning calculating ECM correlated index (ECI) for survival analysis. Pathway enrichment and correlation analysis were used to explore the relationship among ECM clusters, ECI and TME. Single-cell transcriptome data from GSE161529 was processed for uncovering the differences among ECM clusters. RESULTS: Using NMF, we identified three ECM clusters in the TCGA database: C1 (Neuron), C2 (ECM), and C3 (Immune). Subsequently, WGCNA was employed to pinpoint cluster-specific genes and develop a prognostic model. This model demonstrated robust predictive power for breast cancer patient survival in both the Training cohort (n = 5,392, AUC = 0.861) and the Testing cohort (n = 1,344, AUC = 0.711). Upon analyzing the tumor microenvironment (TME), we discovered that fibroblasts and B cell lineage were the core cell types associated with the ECM cluster phenotypes. Single-cell RNA sequencing data further revealed that angiopoietin like 4 (ANGPTL4)+ fibroblasts were specifically linked to the C2 phenotype, while complement factor D (CFD)+ fibroblasts characterized the other ECM clusters. CellChat analysis indicated that ANGPTL4+ and CFD+ fibroblasts regulate B cell lineage via distinct signaling pathways. Additionally, analysis using the Kaplan-Meier Plotter website showed that CFD was favorable for immunotherapy response, whereas ANGPTL4 negatively impacted the outcomes of cancer patients receiving immunotherapy. CONCLUSION: We identified distinct ECM clusters in breast cancer patients, irrespective of molecular subtypes. Additionally, we constructed an effective prognostic model based on these ECM clusters and recognized ANGPTL4+ and CFD+ fibroblasts as potential biomarkers for immunotherapy in breast cancer.

Humans↗