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Gene expression profiling identifies Fibronectin 1 and CXCL9 as candidate biomarkers for breast cancer screening.

ABSTRACT: BACKGROUND: There is a need to develop blood-based bioassays for breast cancer (BC) screening. In this study, differential gene expression between BC samples and benign tumours was used to identify candidate biomarkers for blood-based screening. METHODS: We identified two proteins (Fibronectin 1 and CXCL9) from a gene expression data set that included 120 BC samples and 45 benign lesions. These proteins fulfil the following criteria: differential gene expression between cancer and benign lesion, protein released in the extracellular medium and stable in the serum, commercially available ELISA kit, ELISA accuracy in a feasibility study. Protein concentrations were determined by ELISA. Blood samples were from normal volunteers (n=119) and early BC patients (n=133). RESULTS: Seventy-three per cent of patients had cT1-T2 tumour. Patients had higher CXCL9 and Fibronectin 1 concentrations than volunteers. CXCL9 mean concentration was 851 and 635 pg ml(-1) for patients and volunteers respectively (P=0.013). CXCL9 concentration was significantly higher in patients with estrogen receptor (ER)-negative compared with volunteers (P=0.003), data consistent with gene expression profile. Fibronectin 1 mean concentration was 190 microg ml(-1) for patients and 125 microg ml(-1) for volunteers (P<0.001). Areas under the curve for BC diagnosis were 0.78 and 0.62 for Fibronectin 1 and CXCL9 respectively. A combined score including Fibronectin 1 and CXCL9 dosages presented 53% of sensitivity and 98% of specificity. Similar performances were observed for ER-negative tumours. CONCLUSIONS: This study suggests that Fibronectin 1/CXCL9 dosage in serum could screen a significant rate of BC, including ER-negative, and that differential gene expression analysis is a good approach to select candidate biomarkers to set up blood assays cancer screening.

SUBMITTER: Ruiz-Garcia E

PROVIDER: S-EPMC2822945 | biostudies-other | 2010 Feb

REPOSITORIES: biostudies-other

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Project description:Tuberculosis (TB) is a serious infectious disease in that 90% of those latently infected with Mycobacterium tuberculosis present no symptoms, but possess a 10% lifetime chance of developing active TB. To prevent the spread of the disease, early diagnosis is crucial. However, current methods of detection require improvement in sensitivity, efficiency or specificity. In the present study, we conducted a microarray experiment, comparing the gene expression profiles in the peripheral blood mononuclear cells among individuals with active TB, latent infection, and healthy conditions in a Taiwanese population.Bioinformatics analysis revealed that most of the differentially expressed genes belonged to immune responses, inflammation pathways, and cell cycle control. Subsequent RT-PCR validation identified four differentially expressed genes, NEMF, ASUN, DHX29, and PTPRC, as potential biomarkers for the detection of active and latent TB infections. Receiver operating characteristic analysis showed that the expression level of PTPRC may discriminate active TB patients from healthy individuals, while ASUN could differentiate between the latent state of TB infection and healthy condidtion. In contrast, DHX29 may be used to identify latently infected individuals among active TB patients or healthy individuals. To test the concept of using these biomarkers as diagnostic support, we constructed classification models using these candidate biomarkers and found the Naïve Bayes-based model built with ASUN, DHX29, and PTPRC to yield the best performance.Our study demonstrated that gene expression profiles in the blood can be used to identify not only active TB patients, but also to differentiate latently infected patients from their healthy counterparts. Validation of the constructed computational model in a larger sample size would confirm the reliability of the biomarkers and facilitate the development of a cost-effective and sensitive molecular diagnostic platform for TB.

Project description:ObjectiveTo determine the gene expression profile in individuals with new latent tuberculosis infection (LTBI), and to compare them with people with active tuberculosis (TB) and those exposed to TB but not infected.DesignA prospective cohort study. Recruitment and follow-up were conducted between September 2016 to December 2018. Gene expression and data processing and analysis from April 2019 to April 2021.SettingTwo male Colombian prisons.Participants15 new tuberculin skin test (TST) converters (negative TST at baseline that became positive during follow-up), 11 people that continued with a negative TST after two years of follow-up, and 10 people with pulmonary ATB.Main outcome measuresGene expression profile using RNA sequencing from PBMC samples. The differential expression was assessed using the DESeq2 package in Bioconductor. Genes with |logFC| >1.0 and an adjusted p-value < 0.1 were differentially expressed. We analyzed the differences in the enrichment of KEGG pathways in each group using InterMiner.ResultsThe gene expression was affected by the time of incarceration. We identified group-specific differentially expressed genes between the groups: 289 genes in people with a new LTBI and short incarceration (less than three months of incarceration), 117 in those with LTBI and long incarceration (one or more years of incarceration), 26 in ATB, and 276 in the exposed but non-infected individuals. Four pathways encompassed the largest number of down and up-regulated genes among individuals with LTBI and short incarceration: cytokine signaling, signal transduction, neutrophil degranulation, and innate immune system. In individuals with LTBI and long incarceration, the only enriched pathway within up-regulated genes was Emi1 phosphorylation.ConclusionsRecent infection with MTB is associated with an identifiable RNA pattern related to innate immune system pathways that can be used to prioritize LTBI treatment for those at greatest risk for developing active TB.

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Gene expression profiling identifies Fibronectin 1 and CXCL9 as candidate biomarkers for breast cancer screening.

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