Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Cancer genomics”

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.

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

At least 235 records · Page 13Linked to original sources
PubMed2026

Co-expression of the Mammaglobin (SCGB2A2) Gene With hsa-miR-184 and hsa-miR-190b Indicates Its Possible Role in Oncogenic Pathways in Breast Cancer.

BACKGROUND/AIM: Breast cancer is the most common cancer in women worldwide, and early detection remains a significant challenge. Recent studies have identified increased expression of Mammaglobin A (Q13296, Gene: SCGB2A2) mRNA in breast cancer, suggesting its potential as a disease marker, although its function is not fully understood. To elucidate Mammaglobin's role, this study sought to identify co-expressed miRNAs and analyze the biological pathways they regulate. MATERIALS AND METHODS: Using TCGAbiolinks and Firebrowse, miRNA and gene expression data were collected from 86 patients, including tumor and normal tissue samples from the Cancer Genome Atlas (TCGA) Breast Cancer cohort. Transcriptomic data were analyzed with DESeq2, and a Spearman correlation was calculated for significant p-values, which were further explored using enrichment tools and target gene databases. RESULTS: DESeq2 was used to identify differential expression of miRNAs between normal and tumor breast tissues. Out of 782 miRNAs differentially expressed in breast cancer, hsa-mir-184 and hsa-mir-190b showed a significant positive correlation with SCGB2A expression. These markers were also upregulated in breast cancer tissues compared to normal tissues. Bioinformatics analysis revealed that hsa-mir-184 and hsa-mir-190b play important roles in cancer and cellular proliferation. These miRNAs target a wide range of genes, including sorting nexin 9 (SNX9) and annexin 6 (ANXA6), which are involved in membrane stability, vesicular trafficking, and cell mobility, and they contribute to cancer metastasis. CONCLUSION: The positive correlation among the expression of hsa-miR-184, hsa-miR-190b, and SCGB2A2 suggests that they may participate in shared biological pathways. These pathways govern critical cellular processes, such as membrane trafficking and cell signaling, which are frequently disrupted in cancer. Consequently, these findings enable a better understanding of the role of Mammaglobin in breast cancer signaling.

MicroRNAs (miRNAs)↗
PubMed2026

Automated Classification of Lymphoma Subtypes From Histopathological Images Using a U-Net Deep Learning Model: Comparative Evaluation Study.

BACKGROUND: Accurate classification and grading of lymphoma subtypes are essential for treatment planning. Traditional diagnostic methods face challenges of subjectivity and inefficiency, highlighting the need for automated solutions based on deep learning techniques. OBJECTIVE: This study aimed to investigate the application of deep learning technology, specifically the U-Net model, in classifying and grading lymphoma subtypes to enhance diagnostic precision and efficiency. METHODS: In this study, the U-Net model was used as the primary tool for image segmentation integrated with attention mechanisms and residual networks for feature extraction and classification. A total of 620 high-quality histopathological images representing 3 major lymphoma subtypes were collected from The Cancer Genome Atlas and the Cancer Imaging Archive. All images underwent standardized preprocessing, including Gaussian filtering for noise reduction, histogram equalization, and normalization. Data augmentation techniques such as rotation, flipping, and scaling were applied to improve the model's generalization capability. The dataset was divided into training (70%), validation (15%), and test (15%) subsets. Five-fold cross-validation was used to assess model robustness. Performance was benchmarked against mainstream convolutional neural network architectures, including fully convolutional network, SegNet, and DeepLabv3+. RESULTS: The U-Net model achieved high segmentation accuracy, effectively delineating lesion regions and improving the quality of input for classification and grading. The incorporation of attention mechanisms further improved the model's ability to extract key features, whereas the residual structure of the residual network enhanced classification accuracy for complex images. In the test set (N=1250), the proposed fusion model achieved an accuracy of 92% (1150/1250), a sensitivity of 91.04% (1138/1250), a specificity of 89.04% (1113/1250), and an F1-score of 90% (1125/1250) for the classification of the 3 lymphoma subtypes, with an area under the receiver operating characteristic curve of 0.95 (95% CI 0.93-0.97). The high sensitivity and specificity of the model indicate strong clinical applicability, particularly as an assistive diagnostic tool. CONCLUSIONS: Deep learning techniques based on the U-Net architecture offer considerable advantages in the automated classification and grading of lymphoma subtypes. The proposed model significantly improved diagnostic accuracy and accelerated pathological evaluation, providing efficient and precise support for clinical decision-making. Future work may focus on enhancing model robustness through integration with advanced algorithms and validating performance across multicenter clinical datasets. The model also holds promise for deployment in digital pathology platforms and artificial intelligence-assisted diagnostic workflows, improving screening efficiency and promoting consistency in pathological classification.

Humans↗
PubMed2001

Global profiling of gene expression in cancer using genomics and proteomics.

Global profiling of gene expression at the genomic level using DNA microarrays and at the protein level using a variety of technologies have followed separate paths. Studies of gene expression for several types of tumors, using DNA microarrays, have been published recently that are informative with respect to delineating distinct patterns of gene expression among subsets of related tumors. Proteomics-based profiling uniquely allows delineation of global changes in protein expression patterns resulting from transcriptional and post-transcriptional control, post-translational modifications and shifts in proteins between different cellular compartments. Some of the current technologies for proteome profiling and the application of proteomics to the analysis of tumor tissues are reviewed. Given that comprehensive expression profiles obtained using genomics and proteomics are highly complementary, a combined approach to profiling may well uncover expression patterns that could not be predicted using a single approach.

Gene Expression Profiling↗
PubMed2005

Practical cancer genetics, genomics and proteomics.

Functional genomics is the science that defines the function of newly identified genes and translates this knowledge into related health and disease. Functional genomics is complex, involving multiple scientific disciplines including computational biology, genetics, physiology, structural biology, and molecular and cell biology. Due to its inherent complexity, the National Institutes of Health (NIH) has termed functional genomics "the science of collaboration." This paper will review microarray gene expression analysis, proteomics, and the impact of molecular medical genetics on medical care.

Gene Expression↗
PubMed2005

Cloning of novel LERGU mRNAs in GPR30 3' untranslated region and detection of 2 bp-deletion polymorphism in gastric cancer.

The improved IGCR (In-Gel Competitive Reassociation) method was applied to the analysis of human gastric cancer genomic DNA to identify its alterations, and it appeared that the IGCR library contained a fragment of 3'-untranslated region (3' UTR) of G-protein coupled receptor 30 (GPR30) mRNA. When we searched genomic DNA pairs of gastric cancer patients with this IGCR clone, we found the deletion polymorphism with or without 2 bp (Cytosine and Thymine; CT). We confirmed the existence of a novel mRNA in GPR30 3'UTR by northern blotting, cloned this novel mRNA and named it Leucine Rich Protein in GPR30 3'UTR (LERGU). The EST database search gave one alternative splicing form in this 3' UTR, which was named as LERGU-1. A novel alternative splicing form of this mRNA was also identified from the stomach total RNA, which was named LERGU-2. The LERGU mRNA was also detected in eight gastric cancer cell lines, but GPR30 mRNA scarcely existed. Furthermore, we detected the 2 bp-deletion form in genomic DNAs and mRNAs derived from gastric cancers, but not in other type cancers. Since the 2 bp-deletion position on LERGU corresponds to its alternative splicing site, this deletion may produce a frame-shifted protein. Overall, our findings suggest that a mutation or disappearance of the normal LERGU protein may have a function in the development of gastric cancer.

3' Untranslated Regions↗
PubMed2004

High-resolution global profiling of genomic alterations with long oligonucleotide microarray.

Cancer represents the phenotypic end point of multiple genetic lesions that endow cells with a full range of biological properties required for tumorigenesis. Among the hallmark features of the cancer genome are recurrent regional gains and losses that, upon detailed characterization, have provided highly productive discovery paths for new oncogenes and tumor suppressor genes. In this study, we describe the use of an oligonucleotide-based microarray platform and development of requisite assay conditions and bioinformatic mining tools that permits high-resolution genome-wide array-comparative genome hybridization profiling of human and mouse tumors. Using a commercially available 60-mer oligonucleotide microarray, we demonstrate that this platform provides sufficient sensitivity to detect single-copy difference in gene dosage of full complexity genomic DNA while offering high resolution. The commercial availability of the microarrays and associated reagents, along with the technical protocols and analytical tools described in this report, should provide investigators with the immediate capacity to perform DNA analysis of normal and diseased genomes in a global and detailed manner.

Animals↗
PubMed2002

Mucinous cancers have fewer genomic alterations than more common classes of breast cancer.

Mucinous cancers of the breast are distinguished histologically by their abundant pools of mucin and low degree of nuclear pleomorphism. Relative to the more common breast cancers of no distinctive type (ductal carcinoma), mucinous cancers have a relatively favorable prognosis. In a study of chromosomal changes in mucinous cancers, we evaluated the extent of loss of heterozygosity (LOH) at chromosomal regions commonly deleted in usual infiltrating ductal carcinoma, including markers on chromosomal arms 1p, 1q, 3p, 6q, 8p, 9p, 11p, 11q, 13q, 16q, 17p, and 17q. Remarkably, we found an average frequency of LOH of only 1.9 of these 12 chromosomal arms in 18 cases of mucinous carcinoma, compared to an average frequency of LOH of 6.4 of these same chromosomal arms in cases of infiltrating ductal cancer. In three of the 18 cases of mucinous carcinoma studied, including one case with regional lymph node metastases, no LOH was seen at any of the 12 chromosomal regions studied. We considered the possibility of other chromosomal loci being more commonly affected in mucinous cancers and conducted comparative genomic hybridization on six of the cases. These studies demonstrated a low overall frequency of genomic copy number changes (mean of 3.1 changes per case) and failed to reveal any other chromosomal locus with frequent losses that had not been evaluated by microsatellite analysis. Together, these data indicate that mucinous cancers of the breast do not have the extensive genomic alterations that are typically found in more common variants of breast cancer. Thus, mucinous cancers most likely have less genetic instability than most other forms of breast cancer and the molecular pathogenesis of this form of breast cancer is likely to be substantially different than that of usual ductal breast cancer.

Adenocarcinoma, Mucinous↗
PubMedDate not supplied

Genomic alterations in primary gastric cancers analyzed by comparative genomic hybridization and clinicopathological factors.

BACKGROUND/AIMS: Genetic changes during the oncogenesis and progression of gastric cancer remain unclear. The aim of our study was to analyze chromosomal aberrations in primary gastric cancers. METHODOLOGY: Using comparative genomic hybridization, we screened 47 primary gastric cancers for changes in the number of copies of DNA sequences. RESULTS: Gains of chromosome arms 20q (55%), 20p (36%), 17q (32%), 19q (30%) and 16p (30%), and losses of chromosome arms 4q (40%), 17p (40%), 5q (38%), 18q (30%) and 4p (28%) were detected most frequently. In addition, a high level of amplification was observed at 3q21 (2%), 6p21 (4%), 7q31 (6%), 8q23-24 (2%), 19q12-13 (2%), and 20q13 (2%). Among these alterations, the gain of 20q was the most frequent change. We then compared these changes with clinicopathological factors and identified signet ring cell carcinomas in 6 cases. Our study demonstrated no amplification of chromosome 20q in signet ring cell carcinoma in contrast to that in the other histologic types of gastric cancer. CONCLUSIONS: Our findings may be related to the morphologic and clinical features of signet ring cell carcinoma, and several oncogenes mapped on 20q may play an important role as determinants of the clinical and histologic features of gastric cancer.

Adenocarcinoma↗
PubMedDate not supplied

Genome-wide analysis of deoxyribonucleic acid in endometrial cancer using comparative genomic hybridization microarrays.

The aim of this study was to identify amplified oncogenes in endometrial cancer using array-based comparative genomic hybridization (array CGH). Despite its prevalence, the molecular mechanisms of endometrial carcinogenesis are still poorly understood. The selected array CGH allows the simultaneous examination of 58 oncogenes commonly amplified in human cancers and is capable of achieving increased mapping resolution compared with conventional CGH. A subset of 8 specimens from a bank of 60 malignant and normal specimens was selected for array analysis to identify potential genes of interest. TaqMan polymerase chain reaction was carried out on the 60 specimens to examine if aberrations at the genomic level correlated with gene expression and to compare expression in normal and malignant samples. Oncogenes amplified in the endometrial cancers included AR, PIK3CA, MET, HRAS, NRAS, D17S1670, FGFR1, CTSB, RPS6KB1, LAMC2, MYC, PDGFRA, FGF4/FGF3, PAKI, and FGR. Three genes were examined at the messenger RNA level. AR and PIK3CA were higher in normal specimens, and MET was higher in malignant samples, suggesting a role for MET in endometrial cancer. Newer arrays examining more genes and larger sample numbers are necessary to elucidate the carcinogenic pathway in endometrial cancer.

Adenocarcinoma↗
PubMed2003

Genomics in colorectal cancer: godsend or gimmick?

Genomics and the new techniques it embraces, such as cDNA and tissue arrays, comparative genomic hybridization and single nucleotide polymorphism analysis, offer the opportunity to generate massive amounts of data relating to screening, aetiology, diagnosis, prognostication and optimal treatment in colorectal cancer. To make progress, studies must be designed to stringently test these new technologies and compare them to the tried and tested gold standards. This is best done in the form of large randomized controlled trials. Genomics undoubtedly offers the prospect of exciting new developments in the field of colorectal cancer but these should be regarded with caution until they are proved superior to present practices and models.

Colorectal Neoplasms↗
PubMed2026

Beyond genes and hotspots: Protein-informed interpretation of cancer mutations.

Interpreting the biological and clinical significance of somatic mutations in cancer genomes remains challenging. In this issue of Cancer Cell, Hyeon et al. identify and map significantly mutated regions to protein domains and structural features, establishing a proteo-genomics framework for interpreting cancer mutations beyond genes and hotspots.

Journal Article↗
PubMed1996

Infrequent alterations of the p53 gene in rat skin cancers induced by ionizing radiation.

Radiation carcinogenesis almost certainly involves multiple genetic alterations. Identification of such genetic alterations would provide information to help understand better the molecular mechanism of radiation carcinogenesis. The energy released by ionizing radiation has the potential to produce DNA strand breaks, major gene deletions or rearrangements, and other base damages. Alterations of the p53 gene, a common tumour suppressor gene altered in human cancers, were examined in radiation-induced rat skin cancers. Genomic DNA from a total of 33 rat skin cancers induced by ionizing radiation was examined by Southern blot hybridization for abnormal restriction fragment patterns in the p53 gene. An abnormal p53 restriction pattern was found in one of 16 cancers induced by electron radiation and in one of nine cancers induced by neon ions. The genomic DNA from representative cancers, including the two with an abnormal restriction pattern, was further examined by polymerase chain reaction amplification and direct sequencing in exons 5-8 of the p53 gene. The results showed that one restriction fragment length polymorphism (RFLP)-positive cancer induced by electron radiation had a partial gene deletion which was defined approximately between exons 2-8, while none of the other cancers showed sequence changes. Our results indicate that the alterations in the critical binding region of the p53 gene are infrequent in rat skin cancers induced by either electron or neon ion radiation.

Animals↗
PubMed2025

Asymmetric integration of various cancer datasets for identifying risk-associated variants and genes.

MOTIVATION: Cancer genomic research provides an opportunity to identify cancer risk-associated genes, but often suffers from undesirable low statistical power due to a limited sample size. Integrated analysis with different cancers has the potential to enhance statistical power for identifying pan-cancer risk genes. However, substantial heterogeneity across various cancers makes this challenging. RESULTS: Recently, a novel asymmetric integration method was developed that can deal with data heterogeneity and exclude unhelpful datasets from the analysis. We adapted and applied this method to integrate genotype datasets with matched case and control individuals from the Michigan Genomics Initiative, using each cancer as the primary dataset of interest and the other cancers as auxiliary datasets, respectively. Conditional logistic regression models were coupled with the asymmetric integrated framework to handle the matched case-control study design and permutation tests were performed to control for false discovery rates (FDRs). At the same FDR level, the integrated analysis found more potential genetic variants and genes that are associated with the risks of various cancers, showcasing the promise of the proposed approach for integrated analysis of cancer datasets. AVAILABILITY AND IMPLEMENTATION: Our method is available as source code at https://github.com/rxxwang/integrate_cancer.

Journal Article↗
PubMed2026

CancerOmicsStudio (CoS): a web server for integrative and interpretable analysis of multi-omics cancer data.

MOTIVATION: Large-scale omics resources, including The Cancer Genome Atlas, Genomics of Drug Sensitivity in Cancer, and the Cancer Dependency Map, have become essential for cancer research. However, these datasets are distributed across different platforms, formats and analysis frameworks, which limits their practical use by researchers without extensive computational expertise. RESULTS: We developed CancerOmicsStudio (CoS), a web server for integrative and interpretable analysis of multi-omics cancer data across 33 cancer types. CoS provides five major modules: CosAI, Traditional Analysis, Drug Sensitivity, CRISPR Dependency and Single-Cell Tumor Microenvironment. The Traditional Analysis module supports expression comparison, diagnostic evaluation, survival analysis, enrichment analysis and gene correlation. The Drug Sensitivity and CRISPR Dependency modules enable systematic evaluation of gene-drug response associations and gene essentiality in cancer cell lines. The Single-Cell Tumor Microenvironment module supports tumor microenvironment analysis at single-cell resolution. In total, approximately 1.23 million results have been precomputed to enable rapid retrieval. CosAI further allows users to submit natural-language queries and obtain results through a Real-time Analysis as Retrieval framework, with responses summarized by a lightweight language model. AVAILABILITY AND IMPLEMENTATION: CancerOmicsStudio is freely available at Zenodo (doi: 10.5281/zenodo.18744990) and https://cos.wanglab.bio.

Humans↗
PubMed2025

Ancestry and somatic profile predict acral melanoma origin and prognosis.

Acral melanoma, which is not ultraviolet (UV)-associated, is the most common type of melanoma in several low- and middle-income countries including Mexico. Latin American samples are significantly underrepresented in global cancer genomics studies, which directly affects patients in these regions as it is known that cancer risk and incidence may be influenced by ancestry and environmental exposures. To address this, we characterise the genome and transcriptome of 123 acral melanoma tumours from 92 Mexican patients, a population notable because of its genetic admixture. Compared with other studies of melanoma, we found fewer frequent mutations in classical driver genes such as BRAF, NRAS or NF1. While most patients had predominantly Amerindian genetic ancestry, those with higher European ancestry had increased frequency of BRAF mutations and a lower median number of structural variants. The tumours with activating BRAF mutations have a transcriptional profile more similar to cutaneous non-volar melanocytes, suggesting that acral melanomas in these patients may arise from a distinct cell of origin compared to other tumours arising in these locations. KIT mutations were found in a subset of these tumours, and quadruple wild-type samples (non BRAF/NRAS/NF1/KIT) differed from mutated samples in their structural genomic profile and overall and recurrence-free survival patterns. Transcriptional profiling defined three expression clusters; these characteristics were associated with recurrence-free and overall survival. We highlight potential novel low-frequency drivers, such as PTPRJ, NF2 and RDH5. Our study enhances knowledge of this understudied disease and underscores the importance of including samples from diverse ancestries in cancer genomics studies.

Journal Article↗
PubMed2024

Construction and Analysis of a Mitochondrial Metabolism-Related Prognostic Model for Breast Cancer to Evaluate Survival and Immunotherapy.

As one of the most prevalent malignancies among women, breast cancer (BC) is tightly linked to metabolic dysfunction. However, the correlation between mitochondrial metabolism-related genes (MMRGs) and BC remains unclear. The training and validation datasets for BC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases, respectively. MMRG-related data were obtained from the Molecular Signatures Database. A risk score prognostic model incorporating MMRGs was established based on univariate, LASSO, and multivariate Cox regression analyses. Independent factors affecting BC prognosis were identified through regression analysis and presented in a nomogram. Single-sample gene set enrichment analysis was employed to assess the immune levels of high-risk (HR) and low-risk (LR) groups. The sensitivity of BC patients in the two groups to common anti-tumor drugs was evaluated by utilizing the Genomics of Drug Sensitivity in Cancer database. 12 MMRGs significantly associated with survival were selected from 1234 MMRGs. A 12-gene risk score prognostic model was built. In the multivariate regression analysis incorporating classical clinical factors, the MMRG-related risk score remained an independent prognostic factor. As revealed by tumor immune microenvironment analysis, the LR group with higher survival rates had elevated immune levels. The drug sensitivity results unmasked that the LR group demonstrated higher sensitivity to Irinotecan, Nilotinib, and Oxaliplatin, while the HR group demonstrated higher sensitivity to Lapatinib. The development of MMRG characteristics provides a comprehensive understanding of mitochondrial metabolism in BC, aiding in the prediction of prognosis and tumor microenvironment, and offering promising therapeutic choices for BC patients with different MMRG risk scores.

Humans↗