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At least 793 records · Page 44Linked to original sources
PubMed2006

A quantitative reverse transcriptase-polymerase chain reaction assay to identify metastatic carcinoma tissue of origin.

Identifying the primary site in patients with metastatic carcinoma of unknown primary origin can enable more specific therapeutic regimens and may prolong survival. Twenty-three putative tissue-specific markers for lung, colon, pancreatic, breast, prostate, and ovarian carcinomas were nominated by querying a gene expression profile database and by performing a literature search. Ten of these marker candidates were then selected based on validation by reverse transcriptase-polymerase chain reaction (RT-PCR) on 205 formalin-fixed, paraffin-embedded metastatic carcinoma specimens originating from these six and from other cancer types. Next, we optimized the RNA isolation and quantitative RT-PCR methods for these 10 markers and applied the quantitative RT-PCR assay to a set of 260 metastatic tumors. We then built a gene-based algorithm that predicted the tissue of origin of metastatic carcinomas with an overall leave-one-out cross-validation accuracy of 78%. Lastly, our assay demonstrated an accuracy of 76% when tested on an independent set of 48 metastatic samples, 37 of which were either a known primary or initially presented as carcinoma of unknown primary but were subsequently resolved.

Algorithms↗
PubMed2006

Root controls on soil microbial community structure in forest soils.

We assessed microbial community composition as a function of altered above- and belowground inputs to soil in forest ecosystems of Oregon, Pennsylvania, and Hungary as part of a larger Detritus Input and Removal Treatment (DIRT) experiment. DIRT plots, which include root trenching, aboveground litter exclusion, and doubling of litter inputs, have been established in forested ecosystems in the US and Europe that vary with respect to dominant tree species, soil C content, N deposition rate, and soil type. This study used phospholipid fatty-acid (PLFA) analysis to examine changes in the soil microbial community size and composition in the mineral soil (0-10 cm) as a result of the DIRT treatments. At all sites, the PLFA profiles from the plots without roots were significantly different from all other treatments. PLFA analysis showed that the rootless plots generally contained larger quantities of actinomycete biomarkers and lower amounts of fungal biomarkers. At one of the sites in an old-growth coniferous forest, seasonal changes in PLFA profiles were also examined. Seasonal differences in soil microbial community composition were greater than treatment differences. Throughout the year, treatments without roots continued to have a different microbial community composition than the treatments with roots, although the specific PLFA biomarkers responsible for these differences varied by season. These data provide direct evidence that root C inputs exert a large control on microbial community composition in the three forested ecosystems studied.

Plant Roots↗
PubMed2006

Clopidogrel withdrawal is associated with proinflammatory and prothrombotic effects in patients with diabetes and coronary artery disease.

Inhibition of the P2Y12 pathway by the platelet antagonist clopidogrel is associated with a marked reduction in platelet reactivity. Recent reports have shown that P2Y12 inhibition has anti-inflammatory effects as well. However, whether clopidogrel withdrawal is associated with proaggregatory and proinflammatory effects has not yet been explored. Since diabetic subjects are characterized by a prothrombotic and proinflammatory status, we hypothesize that these patients may be more vulnerable to these effects. A total 54 patients with diabetes on long-term (12 months) dual antiplatelet therapy (aspirin plus clopidogrel) were studied. Platelet aggregation (following 6 and 20 micromol/l ADP stimuli) and inflammatory markers (C-reactive protein and P-selectin expression) were assessed before and 1 month following clopidogrel withdrawal. Following clopidogrel withdrawal, aspirin responsiveness using platelet function analyzer-100 was determined as well. A significant increase in all the assessed platelet (P < 0.0001 for 6 and 20 micromol/l ADP-induced aggregation) and inflammatory (P < 0.05 for C-reactive protein, P < 0.001 for P-selectin expression in resting platelets, and P < 0.0001 for P-selectin expression in ADP-stimulated platelets) biomarkers was observed following clopidogrel withdrawal. Low responders to aspirin had increased platelet aggregation profiles (P < 0.05 for 6 and 20 micromol/l ADP-induced aggregation) but no differences in inflammatory markers. In conclusion, clopidogrel withdrawal is associated with an increase in platelet and inflammatory biomarkers in diabetic patients, supporting pleiotropic effects coupled with P2Y12 receptor antagonism.

Aged↗
PubMed2025

Clinicopathologic and genomic analyses of SMARCA4-mutated non-small cell lung carcinoma implicate the needs for tailored treatment strategies.

BACKGROUND: The clinicopathologic and therapeutic significance of SMARCA4 mutation in non-small cell lung carcinoma (NSCLC) remains unclear. METHODS: We retrieved 575 NSCLC cases from the clinical target sequencing cohort (N = 2157) to compare the clinicopathologic characteristics of groups subclassified based on the presence of truncated or non-truncated SMARCA4 mutations (SMARCA4-truncated, SMARCA4-non-truncated, and SMARCA4-wild type [WT]). The differences in gene expression profiles between these groups were evaluated using the TCGA-LUAD dataset. RESULTS: Fifty (2.3%) SMARCA4-truncated and 63 (2.9%) SMARCA4-non-truncated NSCLCs were identified. The majority of SMARCA4-truncated NSCLCs were present in male smokers (94.0%) and pathologically diagnosed as adenocarcinoma (76.0%). The SMARCA4-truncated group showed rare targetable driver alterations with a higher tumor mutation burden than the SMARCA4-WT group. Gene expression profile analysis revealed that cancer/testis antigen (CTA) expression was enriched in the SMARCA4-truncated group, with up to 57% of the cases displaying immunoreactivities for MAGEA4, CT45A, and/or PRAME. The SMARCA4-non-truncated group showed heterogeneous clinicopathologic, genomic, and immunohistochemical features that fell between SMARCA4-truncated and WT groups. Both SMARCA4-truncated and non-truncated groups showed significantly poor prognosis with pemetrexed-platinum chemotherapy, yet there was no significant difference in survival following immune checkpoint inhibitor monotherapy. CONCLUSION: SMARCA4-truncated NSCLC represents a variant of driver-negative NSCLC, mainly occurring in male smokers with poorly differentiated adenocarcinoma histology. In contrast, SMARCA4-non-truncated NSCLC indicates a heterogeneous subpopulation, exhibiting intermediate characteristics between the SMARCA4-truncated and SMARCA4-WT groups. While showing poor response to pemetrexed-platinum chemotherapy, increased CTA expression could be a novel therapeutic target in SMARCA4-mutated NSCLCs.

Humans↗
PubMed2006

Long-term ethanol consumption alters pancreatic gene expression in rats: a possible connection to pancreatic injury.

OBJECTIVES: Long-term ethanol consumption does not cause acute pancreatitis but rather sensitizes the pancreas to subsequent insults. The mechanisms responsible for this sensitization are unknown. To determine whether alterations in pancreatic gene expression might participate in ethanol-mediated sensitization, we performed gene-profiling analysis. METHODS: Animals were fed ethanol-containing Lieber-DeCarli or control diet (pair-fed). After 8 weeks, pancreatic RNA expression was analyzed using Affimetrix GeneChips. Changes in specific genes were verified using quantitative reverse transcriptase-polymerase chain reaction. RESULTS: Long-term ethanol feeding caused a significant alteration of pancreatic gene expression. Selection criteria of changes more than 3-fold and P < 0.05 yielded 114 probe sets. Activating transcription factor 3, heat shock protein 70, heat shock protein 27, and mesotrypsinogen were increased, whereas pancreatitis associate protein, folate carrier, and metallothionein were decreased. CONCLUSIONS: Ethanol had a profound effect on pancreatic gene expression. The genes identified as elevated and reduced in this study may contribute to pancreatic sensitivity to stress. This study indicates for the first time the identities of multiple genes whose expression levels are dramatically influenced by long-term ethanol feeding. The identified genes may help explain the relationship between long-term ethanol abuse and pancreatic disease and lead to possible preventative or therapeutic approaches to ethanol-induced pancreatic disease.

Activating Transcription Factor 3↗
PubMed2005

Reexploring the possible roles of some genes associated with nasopharyngeal carcinoma using microarray-based detection.

In gene expression profiling, nasopharyngeal carcinoma (NPC) 5-8F cells differ from 6-10B cells in terms of their high tumorigenicity and metastatic ability. Differentially expressed genes from the two cell types were analyzed by combining with MILANO (the automatic custom annotation of microarray results which is based on all the available published work in PubMed). The results showed that five genes, including CTSD, P63, CSE1L, BPAG1 and EGR1, have been studied or mentioned in published work on NPC. Subsequently, we reevaluated the roles of these genes in the pathogenesis of NPC by combining the data of gene chips from NPCs versus NPs and pooled cells from 5-8F, 6-10B and CNE2 versus NPs. The results suggested that the roles of BPAG1 and EGR1 are possibly different from those reported in previous NPC studies. These five genes are likely to be involved in the proliferation, apoptosis, invasion and metastasis of NPC. A reexploration of the genes will further define their roles in the pathogenesis of NPC.

Biomarkers, Tumor↗
PubMed2007

Predicting risk of breast cancer recurrence using gene-expression profiling.

The molecular profiling of breast tumors using the powerful microarray technology has uncovered the molecular heterogeneity of breast tumors and has offered novel insight into breast tumorigenesis. The estrogen receptor (ER) has been shown to be the most important discriminator dichotomizing breast cancer into two main subsets. At the same time, proliferation, as captured by the recently developed Genomic Grade Index (GGI) has been found to be the most important prognostic factor in breast cancer, far beyond ER status. Interestingly, this index encompasses a significant portion of the predictive power of many published prognostic signatures. The challenge now is to integrate all the prognostic gene signatures available to date towards a comprehensive genomic fingerprint of the primary tumor. In the future, we should be able to offer individualized treatment to our patients based on a clinical decision-making algorithm that takes into account the clinicopathological parameters, the genomic profile of the primary tumor, the presence of micrometastatic cells and pharmacogenetic data for drug response.

Animals↗
PubMed2025

Genomic and Transcriptomic Landscape of Epstein-Barr Virus-Positive Inflammatory Follicular Dendritic Cell Sarcoma: A Multicenter Study.

Epstein-Barr virus (EBV)-positive inflammatory follicular dendritic cell sarcoma (EBV+ IFDCS) is a rare indolent malignant neoplasm, which occurs almost exclusively in the liver or spleen and may arise from a common EBV-infected mesenchymal cell that differentiates along the follicular or fibroblastic dendritic cell pathway. Despite its rarity, it presents a pressing need for an improved understanding of its genetic underpinnings and potential treatment strategies for recurrent or disseminated cases. To address this, we conducted comprehensive whole-exome sequencing and transcriptome sequencing (mRNA-seq) analyses on 31 and 6 cases of EBV+ IFDCS, respectively, collected from multiple centers in China. We also compared the genetic features of EBV+ IFDCS with those of other EBV-associated malignancies. Our analyses revealed a relatively high somatic mutation rate and widespread copy number variations affecting the major histocompatibility complex-I/II in EBV+ IFDCS. Integrated mutational profiling identified key signaling pathways involved in epigenetic regulation, NF-&#x3ba;B signaling, RTK/RAS/PI(3)K, and the Hippo pathway. Furthermore, we identified several frequently altered genes that could serve as potential therapeutic targets in EBV+ IFDCS. Transcriptomic analysis unveiled significant upregulation of pathways related to virus infection, immune responses, and multiple immune checkpoint genes in EBV+ IFDCS. Comparative analysis demonstrated clear genetic distinctions between EBV+ IFDCS and other EBV-associated tumors. In conclusion, our study provides comprehensive insights into the unique genomic and transcriptomic landscape of EBV+ IFDCS. We have identified multiple genetic alterations that likely contribute to the development and progression of this malignancy. Our results suggest that targeted therapy and immune checkpoint inhibitors may hold promise as potential therapeutic approaches for patients with recurrent or disseminated EBV+ IFDCS.

Humans↗
PubMed2003

Developmental markers of B cells are superior to those of T cells for identification of stages with distinct gene expression profiles.

B and T lymphocytes develop through a series of cellular stages, which are defined by recombination status of the immunoglobulin and T cell receptor loci and can be separated by analysis of cell-surface markers. We evaluated how well 26 and 41 samples from five and eight developmental stages of B and T cell development, respectively, could be correctly assigned to their lineage of origin and developmental stage by analysis of the expression of 13,026 genes and expressed sequence tags (ESTs). The RNA expression patterns of eight genes correctly classified all 67 samples as belonging to the B cell or to the T cell lineage. Ninety-two to 100% of B-lineage samples could be correctly assigned to the protein-defined developmental stage by the RNA expression pattern of 29 genes. By contrast, RNA expression patterns of 39 genes were necessary to correctly assign 85-100% of T-lineage samples to the correct developmental stage. The sets of genes used for these classifications contain ESTs as well as known genes that have not previously been associated with lymphocyte development. Graphical display of the classifications shows that B-lineage samples are well separated from T-lineage samples, and samples from the five stages of B cell development are well separated from each other. By contrast, samples from the eight stages of T cell development cannot be separated precisely. We conclude that the protein markers currently widely used for separating stages of B cell development better identify molecularly distinct stages than those used for separating stages of T cell development.

Animals↗
PubMed2006

Huntington disease patients and transgenic mice have similar pro-catabolic serum metabolite profiles.

There has been considerable progress recently towards developing therapeutic strategies for Huntington's disease (HD), with several compounds showing beneficial effects in transgenic mouse models. However, human trials in HD are difficult, costly and time-consuming due to the slow disease course, insidious onset and patient-to-patient variability. Identification of molecular biomarkers associated with disease progression will aid the development of effective therapies by allowing further validation of animal models and by providing hopefully more sensitive measures of disease progression. Here, we apply metabolic profiling by gas chromatography-time-of-flight-mass spectrometry to serum samples from human HD patients and a transgenic mouse model in a hypothesis-generating search for disease biomarkers. We observed clear differences in metabolic profiles between transgenic mice and wild-type littermates, with a trend for similar differences in human patients and control subjects. Thus, the metabolites responsible for distinguishing transgenic mice also comprised a metabolic signature tentatively associated with the human disease. The candidate biomarkers composing this HD-associated metabolic signature in mouse and humans are indicative of a change to a pro-catabolic phenotype in early HD preceding symptom onset, with changes in various markers of fatty acid breakdown (including glycerol and malonate) and also in certain aliphatic amino acids. Our data raise the prospect of a robust molecular definition of progression of HD prior to symptom onset, and if validated in a genuinely prospective fashion these biomarker trajectories could facilitate the development of useful therapies for this disease.

Adult↗
PubMed2002

cDNA arrays: gene expression profiles of Hodgkin's disease and anaplastic large cell lymphoma cell lines.

cDNA arrays are a powerful tool for the identification of differentially expressed genes in malignant tumors. We used this technique to study the gene expression profiles of anaplastic large cell lymphoma (ALCL) and Hodgkin's disease (HD). Gene expression of 11 lymphoma cell lines was analyzed covering 1176 cDNA sequences. Comparing these data to the expression profiles of B- and T-lymphocytes, we identified 27 genes that were deregulated in all cell lines or in a particular entity. For the establishment of gene expression profiles the 27 genes were assigned to four groups composed of genes deregulated in (i) all lymphoma cell lines, (ii) ALCL and HD, (iii) only HD, and (iv) ALCL exclusively. Our results indicate that ALCL and HD share the differential expression of at least five genes. In addition, both entities are characterized by the differentially deregulated expression of four genes in HD and seven genes in ALCL. Because the expression profiling was performed on cell lines, further studies are needed to clarify the biological significance of the differentially expressed genes.

B-Lymphocytes↗
PubMed2006

Expression profiling of peripheral-blood mononuclear cells from patients with chronic hepatitis C undergoing interferon therapy.

BACKGROUND: Interferon (IFN) is now the standard treatment for chronic hepatitis C (CH-C); however, treatment efficacy is unpredictable before IFN therapy is started. METHODS: We investigated the gene-expression profiles of peripheral-blood mononuclear cells (PBMCs) from patients with CH-C showing different responses to IFN. Gene-expression profiles of PBMCs were analyzed in 21 patients with CH-C treated with IFN alone or in combination with ribavirin as well as in 6 healthy volunteers. Serial changes in the gene-expression profiles of PBMCs from individual patients were evaluated before treatment, 2 weeks after the start of IFN therapy, and 6 months after the completion of IFN therapy. RESULTS: Interestingly, the gene-expression profiles of PBMCs from patients with CH-C and healthy volunteers differed substantially; early T cell-activation antigen CD69 was significantly up-regulated in patients with CH-C, but immune-related molecules such as chemokine (C-C motif) receptor 2 and interleukin 7 receptor were significantly down-regulated. Selected combinations of expressed genes obtained before treatment and during IFN therapy by use of a fuzzy neural network combined with the SWEEP operator method predicted the outcome of IFN therapy with peak accuracies of 91.0% and 90.2%, respectively. CONCLUSIONS: These findings suggest that the gene-expression profiles of PBMCs from patients with CH-C may be useful biomarkers for IFN therapy.

Adult↗
PubMedDate not supplied

Characterization of proteins in human pancreatic cancer serum using differential gel electrophoresis and tandem mass spectrometry.

The purpose of this study was to develop techniques for identifying cancer biomarkers in human serum using differential in-gel electrophoresis (DIGE), and characterizing the protein biomarkers using tandem mass spectrometry (MS/MS). A major problem in profiling protein expression by DIGE comes from the presence of high concentrations of a small number of proteins. Therefore, serum samples were first chromatographed using an immunoaffinity HPLC column (Agilent Technologies), to selectively remove albumin, immunoglobulins, transferrin, haptoglobin, and antitrypsin. Serum samples from three individuals with pancreatic cancer and three individuals without cancer were compared. Serum samples were processed using the immunoaffinity column. Differential protein analysis was performed using DIGE. A total of 56 protein spot-features were found to be significantly increased and 43 significantly decreased in cancer serum samples. These spot features were excised, trypsin digested, and analyzed by MALDI/TOF/TOF (4700 Proteomics Analyzer, Applied Biosystems). We identified 24 unique proteins that were increased and 17 unique proteins that were decreased in cancer serum samples. Western blot analysis confirmed increased levels of several of these proteins in the pancreatic cancer serum samples. In an independent series of serum samples from 20 patients with pancreatic cancer and 14 controls, increased levels of apolipoprotein E, alpha-1-antichymotrypsin, and inter-alpha-trypsin inhibitor were found to be associated with pancreatic cancer. These results suggest that affinity column enrichment and 2-D DIGE can be used to identify numerous proteins differentially expressed in serum from individuals with pancreatic cancer.

Adult↗
PubMed2005

Identification of progression markers in B-CLL by gene expression profiling.

OBJECTIVE: B-cell chronic lymphocytic leukemia is a heterogeneous disease with a pronounced variation in the clinical course. With the purpose of identifying genes that could be related to disease progression, we have performed gene expression profiling on B-CLL patients with an indolent disease and patients with a progressive disease with need for therapy. MATERIALS AND METHODS: we applied the Affymetrix GeneChip technique to 11 B-CLL patients with stable and 10 patients with clinically progressive disease. Supervised and unsupervised clustering methods with different algorithms were used to identify genes that tend to give a distinction between stable and progressive disease. RESULTS: The supervised learning procedures identified groups of genes with a combined power to discriminate samples from progressive and stable disease with 70-90% accuracy. The gene for protein phosphatase 2 regulatory subunit B' (B56) gamma isoform (PPP2R5C) and the gene for retinoblastoma-like 2 (p130) (RBL2) were included among the best discriminators; both genes were downregulated in progressive as compared to stable B-CLL. In a hierarchical clustering analysis based on gene expression pattern three clinical subcategories could be identified: one with a more severe clinical outcome, a second one with good prognosis, and a third one that was intermediate between the other two groups. CONCLUSIONS: Our application of microarray analysis on a clinically well defined material has identified a number of genes with combined expression patterns related to stable or progressive disease in general. Unsupervised clustering suggested the existence of subclasses of samples in the progressive group that may be identifiable through gene expression patterns.

Aged↗
PubMed2003

Analysis of gene expression profile of pancreatic carcinoma using cDNA microarray.

AIM: To identify new diagnostic markers and drug targets, the gene expression profiles of pancreatic cancer were compared with that of adjacent normal tissues utilizing cDNA microarray analysis. METHODS: cDNA probes were prepared by labeling mRNA from samples of six pancreatic carcinoma tissues with Cy5-dUTP and mRNA from adjacent normal tissues with Cy3-dUTP respectively through reverse transcription. The mixed probes of each sample were then hybridized with 12 800 cDNA arrays (12 648 unique human cDNA sequences), and the fluorescent signals were scanned by ScanArray 3 000 scanner (General Scanning, Inc.). The values of Cy5-dUTP and Cy3-dUTP on each spot were analyzed and calculated by ImaGene 3.0 software (BioDiscovery, Inc.). Differentially expressed genes were screened according to the criterion that the absolute value of natural logarithm of the ratio of Cy5-dUTP to Cy3-dUTP was greater-than 0.69. RESULTS: Among 6 samples investigated, 301 genes, which accounted for 2.38 % of genes on the microarry slides, exhibited differentially expression at least in 5. There were 166 over-expressed genes including 136 having been registered in Genebank, and 135 under-expressed genes including 79 in Genebank in cancerous tissues. CONCLUSION: Microarray analysis may provide invaluable information on disease pathology, progression, resistance to treatment, and response to cellular microenvironments of pancreatic carcinoma and ultimately may lead to improving early diagnosis and discovering innovative therapeutic approaches for cancer.

Biomarkers, Tumor↗
PubMed2026

Comprehensive In Silico Analysis Identifies MSTO1 and LIG1 as Candidate Biomarkers With Diagnostic and Prognostic Relevance in Hepatocellular Carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and remains a major cause of cancer-related mortality worldwide. Its poor clinical outcomes are largely attributed to late-stage diagnosis and the limited accuracy of currently available diagnostic and prognostic biomarkers. Therefore, identifying novel molecular markers with improved sensitivity, specificity, and therapeutic relevance is essential for enhancing early detection and guiding personalized treatment strategies. AIMS: To identify and prioritize novel candidate HCC biomarkers with diagnostic and prognostic value and potential therapeutic vulnerability using integrated multi-omics, survival, functional dependency, and tumor microenvironment analyses. METHODS AND RESULTS: We examined the mRNA and protein expression levels of 8 DEGs in HCC tissues in the TCGA and CPTAC datasets using UALCAN, which showed that MSTO1 and LIG1 were overexpressed consistently in HCC relative to normal liver tissues. Moreover, elevated expression levels of these genes were significantly associated with higher tumor grade and advanced stage. Kaplan-Meier plotter survival data confirmed that increased expression of MSTO1 and LIG1 was associated with poorer overall survival. The DepMap CRISPR knockout data confirmed a functional dependency of both genes in HCC cell lines. CBioPortal analyses provided characterization of genomic alterations and enabled enrichment analysis of co-expressed genes, and the TCGA-UALCAN pan-cancer analyses supported the assessment of tissue specificity across tumor types. TIMER3 analyses linked candidate gene expression with immune cell infiltration patterns. Diagnostic performance by ROC analysis showed excellent discrimination for MSTO1 (AUC&#x2009;=&#x2009;0.987) and good discrimination for LIG1 (AUC&#x2009;=&#x2009;0.897). Multivariate Cox regression with Benjamini-Hochberg FDR correction across the eight genes supported MSTO1 as a candidate independent prognostic factor after adjustment for tumor stage, grade, etiology, age, and sex (HR&#x2009;=&#x2009;1.29, p&#x2009;=&#x2009;0.035), whilst LIG1 showed no independent prognostic value. Promoter methylation of MSTO1 and ADH4, assessed via UALCAN, showed that both genes were significantly differentially methylated in the promoter region of primary HCC tissues compared with normal liver tissues. Our study also confirmed the biological and clinical relevance of established HCC biomarkers: TERT, IRAK1, and ADH4. CONCLUSION: MSTO1 and LIG1 emerged as candidate diagnostic biomarkers in HCC. Additionally, MSTO1 showed a candidate prognostic association with overall survival that remained significant after adjusting for tumor stage, grade, and etiology, as well as patients' age, but not after further adjustment for AFP status. Functional data also highlighted MSTO1 as a candidate therapeutic dependency. On the other hand, LIG1 showed no independent prognostic association in either multivariate model. Their differential expression and functional essentiality in HCC cell lines highlighted their value for further experimental and independent-cohort validation before potential integration into biomarker development pipelines aimed at improving early detection and targeted therapy in HCC.

Humans↗
PubMed2003

Using gene expression ratios to predict outcome among patients with mesothelioma.

BACKGROUND: We have recently demonstrated that simple ratios of the expression levels of selected genes in tumor samples can be used to distinguish among types of thoracic malignancies. We examined whether this technique could predict treatment-related outcome for patients with mesothelioma. METHODS: We used gene expression profiling data previously collected from 17 mesothelioma patients with different overall survival times to define two outcome-related groups of patients and to train an expression ratio-based outcome predictor model. A Student's t test was used to identify genes among the two outcome groups that had statistically significant, inversely correlated expression levels; those genes were used to form prognostic expression ratios. We used a combination of several highly accurate expression ratios and cross-validation techniques to assess the internal consistency of this predictor model, quantitative reverse transcription-polymerase chain reaction of tumor RNA to confirm the microarray data, and Kaplan-Meier survival analysis to validate the model among an independent set of 29 mesothelioma tumors. All statistical tests were two-sided. RESULTS: We developed an expression ratio-based test capable of identifying 100% (17/17) of the samples used to train the model. This test remained highly accurate (88%, 15/17) after cross-validation. A four-gene expression ratio test statistically significantly (P =.0035) predicted treatment-related patient outcome in mesothelioma independent of the histologic subtype of the tumor. CONCLUSIONS: Gene expression ratio-based analysis accurately predicts treatment-related outcome in mesothelioma samples. This technique could impact the clinical treatment of mesothelioma by allowing the preoperative identification of patients with widely divergent prognoses.

Adult↗
PubMed2026

Integrated multi-omics analysis of metabolomics and proteomics uncovers dysregulated amino acid metabolism in HCC metastasis.

BACKGROUND: Metastasis is the primary cause of treatment failure and adverse prognosis in hepatocellular carcinoma (HCC), and the molecular basis of HCC metastasis remains poorly defined. This work investigated the potential mechanisms underlying HCC metastasis through integrated multi-omics analysis of metabolomics and proteomics. METHOD: This retrospective study included 105 individuals with HCC, with comparative analysis between metastatic and non-metastatic cases. We further evaluated the effects of metastasis on serum metabolomics and proteomics in HCC patients. RESULT: Widespread disturbances in amino acid metabolism were identified via untargeted metabolomics in HCC patients with metastasis, closely governing inflammation-related metabolic remodeling and oxidative stress responses. Specifically, we identified 91 and 59 distinct differential metabolites capable of indicating HCC metastasis, with the screening criteria set as log2 fold change > 1.5, adjusted P value < 0.05, and VIP > 1.5 in positive and negative modes, respectively. The alanine, aspartate and glutamate metabolism pathway correlated with HCC-associated lung metastasis, while the gluconeogenesis pathway was linked to HCC-associated bone metastasis. Compared with HCC (non-metastatic hepatocellular carcinoma), the key molecular alterations in the multi-omics network of HCC_M (HCC with metastasis) are implicated in inflammatory metabolic reprogramming, oxidative stress response, gluconeogenesis, glycolysis, and the tricarboxylic acid (TCA) cycle. Twenty-five proteins, including PKM2, PERCK, ALDH2, CPS1, GLS1, GLUD1, GOT1, and SLC38A2, were identified as potential biomarkers for HCC metastasis. CONCLUSION: By integrating untargeted metabolomic and proteomic profiling, we identified distinct metabolic and proteomic changes linked to HCC metastasis. This work also characterized the pathological characteristics and core pathways underlying HCC metastasis, while identifying potential therapeutic candidates.

Humans↗