SEARCH · Search PubMed

Results for “unsupervised clustering”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8Linked to original sources

PubMed2006

Identification of primary tumors of brain metastases by infrared spectroscopic imaging and linear discriminant analysis.

This study applies infrared (IR) spectroscopy to distinguish normal brain tissue from brain metastases and to determine the primary tumor of four frequent brain metastases such as lung cancer, colorectal cancer, breast cancer, and renal cell carcinoma. Standard methods sometimes fail to identify the origin of brain metastases. As metastatic cells contain the molecular information of the primary tissue cells and IR spectroscopy probes the molecular fingerprint of cells, IR spectroscopy based methods constitute a new approach to determine the primary tumor of a brain metastasis. IR spectroscopic images were recorded by a FTIR spectrometer equipped with a macro sample chamber and coupled to a focal plane array detector. Unsupervised cluster analysis of IR images revealed variances within each sample and between samples of the same tissue type. Cluster averaged IR spectra of tissue classes with known diagnoses were selected to develop a metric with eight variables. These data trained a supervised classification model based on linear discriminant analysis that was used to identify the origin of 20 cryosections including one brain metastasis with an unknown primary tumor.

Brain Neoplasms↗

PubMed2005

Acute myeloid leukemia bearing cytoplasmic nucleophosmin (NPMc+ AML) shows a distinct gene expression profile characterized by up-regulation of genes involved in stem-cell maintenance.

Approximately one third of acute myeloid leukemias (AMLs) are characterized by aberrant cytoplasmic localization of nucleophosmin (NPMc+ AML), consequent to mutations in the NPM putative nucleolar localization signal. These events are mutually exclusive with the major AML-associated chromosomal rearrangements, and are frequently associated with normal karyotype, FLT3 mutations, and multilineage involvement. We report the gene expression profiles of 78 de novo AMLs (72 with normal karyotype; 6 without major chromosomal abnormalities) that were characterized for the subcellular localization and mutation status of NPM. Unsupervised clustering clearly separated NPMc+ from NPMc- AMLs, regardless of the presence of FLT3 mutations or non-major chromosomal rearrangements, supporting the concept that NPMc+ AML represents a distinct entity. The molecular signature of NPMc+ AML includes up-regulation of several genes putatively involved in the maintenance of a stem-cell phenotype, suggesting that NPMc+ AML may derive from a multipotent hematopoietic progenitor.

Acute Disease↗

PubMed2002

Sources of variability and effect of experimental approach on expression profiling data interpretation.

BACKGROUND: We provide a systematic study of the sources of variability in expression profiling data using 56 RNAs isolated from human muscle biopsies (34 Affymetrix MuscleChip arrays), and 36 murine cell culture and tissue RNAs (42 Affymetrix U74Av2 arrays). RESULTS: We studied muscle biopsies from 28 human subjects as well as murine myogenic cell cultures, muscle, and spleens. Human MuscleChip arrays (4,601 probe sets) and murine U74Av2 Affymetrix microarrays were used for expression profiling. RNAs were profiled both singly, and as mixed groups. Variables studied included tissue heterogeneity, cRNA probe production, patient diagnosis, and GeneChip hybridizations. We found that the greatest source of variability was often different regions of the same patient muscle biopsy, reflecting variation in cell type content even in a relatively homogeneous tissue such as muscle. Inter-patient variation was also very high (SNP noise). Experimental variation (RNA, cDNA, cRNA, or GeneChip) was minor. Pre-profile mixing of patient cRNA samples effectively normalized both intra- and inter-patient sources of variation, while retaining a high degree of specificity of the individual profiles (86% of statistically significant differences detected by absolute analysis; and 85% by a 4-pairwise comparison survival method). CONCLUSIONS: Using unsupervised cluster analysis and correlation coefficients of 92 RNA samples on 76 oligonucleotide microarrays, we found that experimental error was not a significant source of unwanted variability in expression profiling experiments. Major sources of variability were from use of small tissue biopsies, particularly in humans where there is substantial inter-patient variability (SNP noise).

Animals↗

PubMed2006

Improved alignment quality by combining evolutionary information, predicted secondary structure and self-organizing maps.

BACKGROUND: Protein sequence alignment is one of the basic tools in bioinformatics. Correct alignments are required for a range of tasks including the derivation of phylogenetic trees and protein structure prediction. Numerous studies have shown that the incorporation of predicted secondary structure information into alignment algorithms improves their performance. Secondary structure predictors have to be trained on a set of somewhat arbitrarily defined states (e.g. helix, strand, coil), and it has been shown that the choice of these states has some effect on alignment quality. However, it is not unlikely that prediction of other structural features also could provide an improvement. In this study we use an unsupervised clustering method, the self-organizing map, to assign sequence profile windows to "structural states" and assess their use in sequence alignment. RESULTS: The addition of self-organizing map locations as inputs to a profile-profile scoring function improves the alignment quality of distantly related proteins slightly. The improvement is slightly smaller than that gained from the inclusion of predicted secondary structure. However, the information seems to be complementary as the two prediction schemes can be combined to improve the alignment quality by a further small but significant amount. CONCLUSION: It has been observed in many studies that predicted secondary structure significantly improves the alignments. Here we have shown that the addition of self-organizing map locations can further improve the alignments as the self-organizing map locations seem to contain some information that is not captured by the predicted secondary structure.

Computational Biology↗

PubMed2006

Cell-type specific gene expression profiles of leukocytes in human peripheral blood.

BACKGROUND: Blood is a complex tissue comprising numerous cell types with distinct functions and corresponding gene expression profiles. We attempted to define the cell type specific gene expression patterns for the major constituent cells of blood, including B-cells, CD4+ T-cells, CD8+ T-cells, lymphocytes and granulocytes. We did this by comparing the global gene expression profiles of purified B-cells, CD4+ T-cells, CD8+ T-cells, granulocytes, and lymphocytes using cDNA microarrays. RESULTS: Unsupervised clustering analysis showed that similar cell populations from different donors share common gene expression profiles. Supervised analyses identified gene expression signatures for B-cells (427 genes), T-cells (222 genes), CD8+ T-cells (23 genes), granulocytes (411 genes), and lymphocytes (67 genes). No statistically significant gene expression signature was identified for CD4+ cells. Genes encoding cell surface proteins were disproportionately represented among the genes that distinguished among the lymphocyte subpopulations. Lymphocytes were distinguishable from granulocytes based on their higher levels of expression of genes encoding ribosomal proteins, while granulocytes exhibited characteristic expression of various cell surface and inflammatory proteins. CONCLUSION: The genes comprising the cell-type specific signatures encompassed many of the genes already known to be involved in cell-type specific processes, and provided clues that may prove useful in discovering the functions of many still unannotated genes. The most prominent feature of the cell type signature genes was the enrichment of genes encoding cell surface proteins, perhaps reflecting the importance of specialized systems for sensing the environment to the physiology of resting leukocytes.

Adult↗

PubMed2006

Genomic alterations identified by array comparative genomic hybridization as prognostic markers in tamoxifen-treated estrogen receptor-positive breast cancer.

BACKGROUND: A considerable proportion of estrogen receptor (ER)-positive breast cancer recurs despite tamoxifen treatment, which is a serious problem commonly encountered in clinical practice. We tried to find novel prognostic markers in this subtype of breast cancer. METHODS: We performed array comparative genomic hybridization (CGH) with 1,440 human bacterial artificial chromosome (BAC) clones to assess copy number changes in 28 fresh-frozen ER-positive breast cancer tissues. All of the patients included had received at least 1 year of tamoxifen treatment. Nine patients had distant recurrence within 5 years (Recurrence group) of diagnosis and 19 patients were alive without disease at least 5 years after diagnosis (Non-recurrence group). RESULTS: Potential prognostic variables were comparable between the two groups. In an unsupervised clustering analysis, samples from each group were well separated. The most common regions of gain in all samples were 1q32.1, 17q23.3, 8q24.11, 17q12-q21.1, and 8p11.21, and the most common regions of loss were 6q14.1-q16.3, 11q21-q24.3, and 13q13.2-q14.3, as called by CGH-Explorer software. The average frequency of copy number changes was similar between the two groups. The most significant chromosomal alterations found more often in the Recurrence group using two different statistical methods were loss of 11p15.5-p15.4, 1p36.33, 11q13.1, and 11p11.2 (adjusted p values < 0.001). In subgroup analysis according to lymph node status, loss of 11p15 and 1p36 were found more often in Recurrence group with borderline significance within the lymph node positive patients (adjusted p = 0.052). CONCLUSION: Our array CGH analysis with BAC clones could detect various genomic alterations in ER-positive breast cancers, and Recurrence group samples showed a significantly different pattern of DNA copy number changes than did Non-recurrence group samples.

Adult↗

PubMed2004

Can gene expression profiling predict survival for patients with squamous cell carcinoma of the lung?

BACKGROUND: Lung cancer remains to be the leading cause of cancer death worldwide. Patients with similar lung cancer may experience quite different clinical outcomes. Reliable molecular prognostic markers are needed to characterize the disparity. In order to identify the genes responsible for the aggressiveness of squamous cell carcinoma of the lung, we applied DNA microarray technology to a case control study. Fifteen patients with surgically treated stage I squamous cell lung cancer were selected. Ten were one-to-one matched on tumour size and grade, age, gender, and smoking status; five died of lung cancer recurrence within 24 months (high-aggressive group), and five survived more than 54 months after surgery (low-aggressive group). Five additional tissues were included as test samples. Unsupervised and supervised approaches were used to explore the relationship among samples and identify differentially expressed genes. We also evaluated the gene markers' accuracy in segregating samples to their respective group. Functional gene networks for the significant genes were retrieved, and their association with survival was tested. RESULTS: Unsupervised clustering did not group tumours based on survival experience. At p < 0.05, 294 and 246 differentially expressed genes for matched and unmatched analysis respectively were identified between the low and high aggressive groups. Linear discriminant analysis was performed on all samples using the 27 top unique genes, and the results showed an overall accuracy rate of 80%. Tests on the association of 24 gene networks with study outcome showed that 7 were highly correlated with the survival time of the lung cancer patients. CONCLUSION: The overall gene expression pattern between the high and low aggressive squamous cell carcinomas of the lung did not differ significantly with the control of confounding factors. A small subset of genes or genes in specific pathways may be responsible for the aggressive nature of a tumour and could potentially serve as panels of prognostic markers for stage I squamous cell lung cancer.

Carcinoma, Squamous Cell↗

PubMed2005

Infiltration of the synovial membrane with macrophage subsets and polymorphonuclear cells reflects global disease activity in spondyloarthropathy.

Considering the relation between synovial inflammation and global disease activity in rheumatoid arthritis (RA) and the distinct but heterogeneous histology of spondyloarthropathy (SpA) synovitis, the present study analyzed whether histopathological features of synovium reflect specific phenotypes and/or global disease activity in SpA. Synovial biopsies obtained from 99 SpA and 86 RA patients with active knee synovitis were analyzed for 15 histological and immunohistochemical markers. Correlations with swollen joint count, serum C-reactive protein concentrations, and erythrocyte sedimentation rate were analyzed using classical and multiparameter statistics. SpA synovitis was characterized by higher vascularity and infiltration with CD163+ macrophages and polymorphonuclear leukocytes (PMNs) and by lower values for lining-layer hyperplasia, lymphoid aggregates, CD1a+ cells, intracellular citrullinated proteins, and MHC-HC gp39 complexes than RA synovitis. Unsupervised clustering of the SpA samples based on synovial features identified two separate clusters that both contained different SpA subtypes but were significantly differentiated by concentration of C-reactive protein and erythrocyte sedimentation rate. Global disease activity in SpA correlated significantly with lining-layer hyperplasia as well as with inflammatory infiltration with macrophages, especially the CD163+ subset, and with PMNs. Accordingly, supervised clustering using these synovial parameters identified a cluster of 20 SpA patients with significantly higher disease activity, and this finding was confirmed in an independent SpA cohort. However, multiparameter models based on synovial histopathology were relatively poor predictors of disease activity in individual patients. In conclusion, these data indicate that inflammatory infiltration of the synovium with CD163+ macrophages and PMNs as well as lining-layer hyperplasia reflect global disease activity in SpA, independently of the SpA subtype. These data support a prominent role for innate immune cells in SpA synovitis and warrant further evaluation of synovial histopathology as a surrogate marker in early-phase therapeutic trials in SpA.

Adult↗

PubMed2026

Prognostic value of circulating tumor DNA and copy-number alterations in patients receiving tandem [225Ac]Ac-/[177Lu]Lu-PSMA-617 therapy for metastatic castration-resistant prostate cancer: a prospective observational study.

BACKGROUND: Prostate-specific membrane antigen-targeted radioligand therapy (PSMA-RLT) demonstrates clinical efficacy in metastatic castration-resistant prostate cancer (mCRPC), yet robust biomarkers for dynamic treatment monitoring and resistance remain lacking. We investigated circulating tumor DNA (ctDNA)-derived tumor fraction (TFx) and genome-wide copy-number alterations (CNAs) as non-invasive biomarkers of treatment response and resistance biology. METHODS: Seventy-eight patients with advanced mCRPC receiving tandem [225Ac]Ac-/[177Lu]Lu-PSMA-617 were prospectively enrolled. Plasma samples collected longitudinally (n = 172) underwent ultra-low-pass whole-genome sequencing. TFx was estimated using ichorCNA, and recurrent CNAs were identified using GISTIC2.0. Associations with progression and overall survival (OS) were assessed using Cox proportional hazards models, including time-dependent analyses. RESULTS: Baseline TFx differed across metastatic disease stages (p = 0.027) and dynamic TFx changes paralleled PSA kinetics during early treatment. Modelled as a time-dependent variable, TFx was associated with a significantly increased risk of progression (HR 4.9, 95% CI 1.2-20.1, p = 0.026). Unsupervised clustering identified distinct high- and low-CNA burden groups strongly correlated with TFx (p = 8.09 × 10⁻8). High CNA burden was associated with shorter median OS (8.3 vs 13.8 months). Multivariable analysis identified baseline logPSA and logALP as independent predictors of OS. Recurrent CNAs affected key tumor suppressors (PTEN, RB1, BRCA2, ATM) and were enriched in pathways related to TP53 signalling, homologous recombination repair, and oncogenic signaling. Longitudinal analyses demonstrated persistence and expansion of specific amplifications at progression. CONCLUSIONS: ctDNA-derived TFx represents a dynamic biomarker of treatment response and progression risk, while CNA profiling provides insight into resistance mechanisms in mCRPC treated with PSMA-RLT. These findings support the integration of ctDNA-based biomarkers into clinical stratification and real-time monitoring strategies.

Humans↗

PubMed2024

Construction of a molecular diagnostic system for neurogenic rosacea by combining transcriptome sequencing and machine learning.

Patients with neurogenic rosacea (NR) frequently demonstrate pronounced neurological manifestations, often unresponsive to conventional therapeutic approaches. A molecular-level understanding and diagnosis of this patient cohort could significantly guide clinical interventions. In this study, we amalgamated our sequencing data (n = 46) with a publicly accessible database (n = 38) to perform an unsupervised cluster analysis of the integrated dataset. The eighty-four rosacea patients were partitioned into two distinct clusters. Neurovascular biomarkers were found to be elevated in cluster 1 compared to cluster 2. Pathways in cluster 1 were predominantly involved in neurotransmitter synthesis, transmission, and functionality, whereas cluster 2 pathways were centered on inflammation-related processes. Differential gene expression analysis and WGCNA were employed to delineate the characteristic gene sets of the two clusters. Subsequently, a diagnostic model was constructed from the identified gene sets using linear regression methodologies. The model's C index, comprising genes PNPLA3, CUX2, PLIN2, and HMGCR, achieved a remarkable value of 0.9683, with an area under the curve (AUC) for the training cohort's nomogram of 0.9376. Clinical characteristics from our dataset (n = 46) were assessed by three seasoned dermatologists, forming the NR validation cohort (NR, n = 18; non-neurogenic rosacea, n = 28). Upon application of our model to NR diagnosis, the model's AUC value reached 0.9023. Finally, potential therapeutic candidates for both patient groups were predicted via the Connectivity Map. In summation, this study unveiled two clusters with unique molecular phenotypes within rosacea, leading to the development of a precise diagnostic model instrumental in NR diagnosis.

Humans↗

PubMed2025

Genetic targets related to aging for the treatment of coronary artery disease.

BACKGROUND: Coronary Artery Disease (CAD) is the most common cardiovascular disease worldwide, threatening human health, quality of life and longevity. Aging is a dominant risk factor for CAD. This study aims to investigate the potential mechanisms of aging-related genes and CAD, and to make molecular drug predictions that will contribute to the diagnosis and treatment. METHODS: We downloaded the gene expression profile of circulating leukocytes in CAD patients (GSE12288) from Gene Expression Omnibus database, obtained differentially expressed aging genes through "limma" package and GenaCards database, and tested their biological functions. Further screening of aging related characteristic genes (ARCGs) using least absolute shrinkage and selection operator and random forest, generating nomogram charts and ROC curves for evaluating diagnostic efficacy. Immune cells were estimated by ssGSEA, and then combine ARCGs with immune cells and clinical indicators based on Pearson correlation analysis. Unsupervised cluster analysis was used to construct molecular clusters based on ARCGs and to assess functional characteristics between clusters. The DSigDB database was employed to explore the potential targeted drugs of ARCGs, and the molecular docking was carried out through Autodock Vina. Finally, single-cell data (GSE159677) of arterial intima was used to further explore the expression of aging signature genes in different cell subpopulations. RESULTS: We identified 8 ARCGs associated with CAD, in which HIF1A and FGFR3 were up while NOX4, TCF7L2, HK3, CDK18, TFAP4, and ITPK1 were down in CAD patients. Based on this, CAD patients can be divided into two molecular clusters, among which cluster A mainly involves functional pathways such as ECM receptor interaction and focal adhesion; cluster B mainly involves functional pathways such as amimo sugar and nucleotide sugar metabolism and pyrimidine metabolism. In addition, the molecular docking results showed that retinoic acid and resveratrol had good binding affinity with targets genes. Further single-cell analysis results showed that NOX4, TCF7L2, ITPK1, and HIF1A were specifically expressed in different types of cells in atherosclerotic tissues. CONCLUSION: Our study identified several ARCGs that may be involved in the pathogenesis and progression of CAD. Further, retinoic acid and resveratrol were potential candidate molecule drugs for inhibiting these targets.

Humans↗

PubMed2004

Papillary and follicular thyroid carcinomas show distinctly different microarray expression profiles and can be distinguished by a minimum of five genes.

PURPOSE: We have previously conducted independent microarray expression analyses of the two most common types of nonmedullary thyroid carcinoma, namely papillary thyroid carcinoma (PTC) and follicular thyroid carcinoma (FTC). In this study, we sought to combine our data sets to shed light on the similarities and differences between these tumor types. MATERIALS AND METHODS: Microarray data from six PTCs, nine FTCs, and 13 normal thyroid samples were normalized to remove interlaboratory variability and then analyzed by unsupervised clustering, t test, and by comparison of absolute and change calls. Expression changes in four genes not previously implicated in thyroid carcinogenesis were verified by reverse transcriptase polymerase chain reaction on these same samples, together with eight additional FTC tumors. RESULTS: PTCs showed two distinct groups of genes that were either over- or underexpressed compared with normal thyroid, whereas the predominant changes in FTCs were of decreased expression. Five genes could collectively distinguish the two tumor types. PTCs showed overexpression of CITED1, claudin-10 (CLDN10), and insulin-like growth factor binding protein 6 (IGFBP6) but showed no change in expression of caveolin-1 (CAV1) or -2 (CAV2); conversely, FTCs did not express CLDN10 and had decreased expression of IGFBP6 and/or CAV1 and CAV2. CONCLUSION: PTC and FTC show distinctive microarray expression profiles, suggesting that either they have different molecular origins or they diverge distinctly from a common origin. Furthermore, if verified in a larger series of tumors, these genes could, in combination with known tumor-specific chromosome translocations, form the basis of a valuable diagnostic tool.

Adenocarcinoma, Follicular↗

PubMed2003

Expression profiling of endometrium from women with endometriosis reveals candidate genes for disease-based implantation failure and infertility.

Endometriosis is clinically associated with pelvic pain and infertility, with implantation failure strongly suggested as an underlying cause for the observed infertility. Eutopic endometrium of women with endometriosis provides a unique experimental paradigm for investigation into molecular mechanisms of reproductive dysfunction and an opportunity to identify specific markers for this disease. We applied paralleled gene expression profiling using high-density oligonucleotide microarrays to investigate differentially regulated genes in endometrium from women with vs. without endometriosis. Fifteen endometrial biopsy samples (obtained during the window of implantation from eight subjects with and seven subjects without endometriosis) were processed for expression profiling on Affymetrix Hu95A microarrays. Data analysis was conducted with GeneChip Analysis Suite, version 4.01, and GeneSpring version 4.0.4. Nonparametric testing was applied, using a P value of 0.05, to assess statistical significance. Of the 12,686 genes analyzed, 91 genes were significantly increased more than 2-fold in their expression, and 115 genes were decreased more than 2-fold. Unsupervised clustering demonstrated down-regulation of several known cell adhesion molecules, endometrial epithelial secreted proteins, and proteins not previously known to be involved in the pathogenesis of endometriosis, as well as up-regulated genes. Selected dysregulated genes were randomly chosen and validated with RT-PCR and/or Northern/dot-blot analyses, and confirmed up-regulation of collagen alpha2 type I, 2.6-fold; bile salt export pump, 2.0-fold; and down-regulation of N-acetylglucosamine-6-O-sulfotransferase (important in synthesis of L-selectin ligands), 1.7-fold; glycodelin, 51.5-fold; integrin alpha2, 1.8-fold; and B61 (Ephrin A1), 4.5-fold. Two-way overlapping layer analysis used to compare endometrial genes in the window of implantation from women with and without endometriosis further identified three unique groups of target genes, which differ with respect to the implantation window and the presence of disease. Group 1 target genes are up-regulated during the normal window of implantation but significantly decreased in women with endometriosis: IL-15, proline-rich protein, B61, Dickkopf-1, glycodelin, N-acetylglucosamine-6-O-sulfotransferase, G0S2 protein, and purine nucleoside phosphorylase. Group 2 genes are normally down-regulated during the window of implantation but are significantly increased with endometriosis: semaphorin E, neuronal olfactomedin-related endoplasmic reticulum localized protein mRNA and Sam68-like phosphotyrosine protein alpha. Group 3 consists of a single gene, neuronal pentraxin II, normally down-regulated during the window of implantation and further decreased in endometrium from women with endometriosis. The data support dysregulation of select genes leading to an inhospitable environment for implantation, including genes involved in embryonic attachment, embryo toxicity, immune dysfunction, and apoptotic responses, as well as genes likely contributing to the pathogenesis of endometriosis, including aromatase, progesterone receptor, angiogenic factors, and others. Identification and validation of selected genes and their functions will contribute to uncovering previously unknown mechanism(s) underlying implantation failure in women with endometriosis and infertility, mechanisms underlying the pathogenesis of endometriosis and providing potential new targets for diagnostic screening and intervention.

Blotting, Northern↗

PubMed2026

Uncovering the diagnostic potential of seminal fluid beyond fertility: cfDNA methylation analysis for the detection of clinically significant prostate cancer.

Research on the potential use of seminal fluid as a liquid biopsy for prostate cancer detection has been limited due to challenges associated with acquisition of this bodily fluid in clinical studies. Here we sought to expand on our previous analysis, which demonstrated high levels of prostate-derived cell free DNA (cfDNA) in seminal fluid in presumed healthy individuals, to a much larger cohort that included participants with prostate cancer. A total of 279 men scheduled for prostate biopsy were enrolled over 4 months across 12 sites. Prior to their biopsy, participants mailed a seminal fluid sample collected at home to the laboratory, from which cfDNA was extracted and underwent methylation analysis. Consistent with our earlier study in healthy individuals, we observed an abundance of high molecular weight (HMW) cfDNA in all samples. Tissue-of-origin deconvolution revealed that granulocytes and sperm were the principal contributors to seminal fluid cfDNA, while prostate-derived cfDNA was present at abundances readily detectable with current technologies. The nucleosomal fraction was very pronounced in some but not all samples and was determined to be correlated with the relative sperm signal. The sperm signal was also observed to be associated with an increase in small insert sizes (< 125 bp) in the sequenced libraries. Unsupervised clustering revealed two distinct populations driven by the abundance of sperm and granulocytes. Since summarizing at the genomic region level confounded tissues of different origins, fragment-level DNA methylation features were used to characterize and quantify the prostate cancer related signal, and features associated with clinically significant prostate cancer were identified. This study expands on our previous work to further characterize seminal fluid and highlights its potential as a promising liquid biopsy medium for the detection and monitoring of clinically significant prostate cancer.

Humans↗

PubMed2005

Emerging patterns of neuronal responses in supplementary and primary motor areas during sensorimotor adaptation.

Acquisition and retention of sensorimotor skills have been extensively investigated psychophysically, but little is known about the underlying neuronal mechanisms. Here we examine the evolution of neural activity associated with adaptation to new kinematic tasks in two cortical areas: the caudal supplementary motor area (SMA proper), and the primary motor cortex (MI). We investigate the hypothesis that adaptation starts at premotor areas, i.e., higher in the hierarchy of computation, until a stable representation is formed in primary areas. In accordance with previous studies, we found that adaptation can be characterized by two phases: an early phase that is accompanied by fast and substantial reduction of errors, followed by a late phase with slower and more moderate improvements in behavior. We used unsupervised clustering to separate the activity of the single cells into groups of cells with similar response patterns, under the assumption that each such subpopulation forms a functional unit. We specifically observed the number of clusters in each cortical area during early and late phases of the adaptation and found that the number of clusters is higher in the SMA during early phases of adaptation. In contrast, a higher number of clusters was observed in MI only during late phases. Our results suggest a new approach to analyze responses of large populations of neurons and use it to show a hierarchy of dynamic reorganization of functional groups during adaptation.

Adaptation, Physiological↗

PubMed2026

Molecular subtyping of adrenocortical carcinoma reveals distinct subtypes with prognostic and therapeutic implications.

Adrenocortical carcinoma (ACC) is a rare but aggressive malignancy with poor survival and limited treatment options. To comprehensively characterize its molecular landscape and identify clinically relevant subtypes, we performed an integrated genomic analysis - including whole-exome sequencing, RNA sequencing, and copy number variation profiling - on 61 Chinese patients with ACC. We identified recurrent mutations in TP53 (25%), CTNNB1 (15%), ZNRF3 (10%), and MEN1 (8%). Unsupervised clustering of transcriptomic data revealed four distinct molecular subtypes: cortisol-driven (CD, 14%), immune-suppressed (IS, 40%), cell cycle-altered (CCA, 22%), and immunomodulatory (IM, 24%). The CD subtype exhibited steroidogenic pathway activation; the IS subtype showed T cell receptor downregulation and the worst disease-free survival; the CCA subtype was marked by chromosomal instability and cell cycle gene overexpression; and the IM subtype displayed enriched immune signaling and favorable outcomes. Copy number analysis further uncovered focal amplifications (e.g. TERT, CDK4) and HLA-II deletions. This study establishes a novel molecular classification of ACC, providing a framework for subtype-specific therapeutic strategies, such as CDK4/6 inhibition for CCA and immunotherapy for IM tumors, while highlighting the clinical challenges of immune-cold IS tumors.

Humans↗

PubMed2005

Tamoxifen treatment for breast cancer enforces a distinct gene-expression profile on the human endometrium: an exploratory study.

Tamoxifen treatment for breast cancer increases proliferation of the endometrium, resulting in an enhanced prevalence of endometrial pathologies, including endometrial cancer. An exploratory study was performed to begin to understand the molecular mechanism of tamoxifen action in the endometrium. Gene-expression profiles were generated of endometrial samples of tamoxifen users and compared with matched controls. The pathological classification of samples from both groups included atrophic/inactive endometrium and endometrial polyps. Unsupervised clustering revealed that samples of tamoxifen users were, irrespective of pathological classification, fairly similar and consequently form a subgroup distinct from the matched controls. Using SAM analysis (a statistical method to select genes differentially expressed between groups), 256 differentially expressed genes were selected between the tamoxifen and control groups. Upon comparing these genes with oestrogen-regulated genes, identified under similar circumstances, 95% of the differentially expressed genes turned out to be tamoxifen-specific. Finally, construction of a gene-expression network of the differentially expressed genes revealed that 69 genes centred around five well-known genes: TP53, RELA, MYC, epidermal growth factor receptor and beta-catenin. This could indicate that these well-known genes, and the pathways in which they function, are important for tamoxifen-controlled proliferation of the endometrium.

Antineoplastic Agents, Hormonal↗

PubMed2026

Deconvolution of evolutionary architecture unmasks a high-risk, subclonal-rich subtype in treatment-naive small cell lung cancer.

BACKGROUND: Intratumoral heterogeneity (ITH) drives therapeutic resistance in small cell lung cancer (SCLC). However, conventional single-sample analysis has limited horizontal, cross-patient comparisons, leaving the overarching evolutionary architecture in treatment-naive tumors poorly understood. This study aims to deconvolve these architectures to identify clinically relevant evolutionary subtypes. METHODS: We analyzed whole-exome sequencing data from 41 treatment-naive SCLC patients. To overcome the cross-patient comparability bottleneck, we developed a novel probabilistic framework using a refined Gaussian Mixture Model (GMM). This standardized subclonal structures into four hierarchical strata, enabling the identification of evolutionary subtypes via unsupervised clustering. To address the scarcity of SCLC public data, prognostic concordance was robustly explored in The Cancer Genome Atlas (TCGA) lung squamous cell carcinoma (LUSC) based on shared smoking etiology, with lung adenocarcinoma (LUAD) serving as a negative control. RESULTS: The cohort robustly segregated into "Clonal-dominant" (Group 1, n=28) and "Subclonal-rich" (Group 2, n=13) subtypes. Group 1 evolution was primarily driven by tobacco signatures (SBS4). Conversely, Group 2 exhibited late-stage acquisition of a DNA mismatch repair deficiency (MMRd) signature (SBS15), fueling trace subclonal diversification. Clinically, Group 2 demonstrated a significantly lower objective response rate (ORR) to platinum-based regimens (25.0% vs. 81.3%, P=0.02). Furthermore, the Subclonal-rich architecture independently predicted inferior overall survival (OS) [adjusted hazard ratio (adj. HR) =2.93, P=0.02], driven predominantly by limited-stage disease. Cross-cancer analysis validated this histology-dependent, high-heterogeneity adverse pattern in early-stage LUSC but not in LUAD. CONCLUSIONS: This hypothesis-generating study demonstrates that a "Subclonal-rich" architecture, driven by acquired MMRd, identifies high-risk, chemo-resistant SCLC. Our GMM approach suggests that pre-existing heterogeneity may serve as a potential, histology-dependent prognostic marker that warrants prospective validation for tailoring future therapeutic regimens.

Gaussian Mixture Model (GMM)↗