Profiling of microRNAs in tumor interstitial fluid of breast cancer patients - a novel resource to identify biomarkers for prognostic classification and detection of cancer
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE83934 | GEO | 2017/05/01
SECONDARY ACCESSION(S): PRJNA327438
REPOSITORIES: GEO
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Project description:It has been hypothesized based on accumulated data that a class of small noncoding RNAs, termed microRNAs, are key factors in intercellular communication. Here, microRNAs present in interstitial breast tumor fluids have been analyzed to identify relevant markers for a diagnosis of breast cancer and to elucidate the cross-talk that exists among cells in a tumor microenvironment. Matched tumor interstitial fluid samples (TIF, n = 60), normal interstitial fluid samples (NIF, n = 51), corresponding tumor tissue specimens (n = 54), and serum samples (n = 27) were collected from patients with breast cancer, and detectable microRNAs were analyzed and compared. In addition, serum data from 32 patients with breast cancer and 22 healthy controls were obtained for a validation study. To identify potential serum biomarkers of breast cancer, first the microRNA profiles of TIF and NIF samples were compared. A total of 266 microRNAs were present at higher level in the TIF samples as compared to normal counterparts. Sixty-one of these microRNAs were present in > 75% of the serum samples and were subsequently tested in a validation set. Seven of the 61 microRNAs were associated with poor survival, while 23 were associated with the presence of immune cells and adipocytes. To our knowledge, these data demonstrate for the first time that profiling of microRNAs in TIF can identify novel biomarkers for the prognostic classification and detection of breast cancer. In addition, the present findings demonstrate that microRNAs may represent the cross-talk that occurs between tumor cells and their surrounding stroma.
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Project description:Despite significant advancements in breast cancer (BC) research, clinicians lack robust serological protein markers for accurate diagnostics and tumor stratification. Tumor interstitial fluid (TIF) accumulates aberrantly externalized proteins within the local tumor space, which can potentially gain access to the circulatory system. As such, TIF may represent a valuable starting point for identifying relevant tumor-specific serological biomarkers. The aim of the study was to perform comprehensive proteomic profiling of TIF to identify proteins associated with BC tumor status and subtype. A liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis of 35 TIFs of three main subtypes: luminal (19), Her2 (4), and triple-negative (TNBC) (12) resulted in the identification of > 8800 proteins. Unsupervised hierarchical clustering segregated the TIF proteome into two major clusters, luminal and TNBC/Her2 subgroups. High-grade tumors enriched with tumor infiltrating lymphocytes (TILs) were also stratified from low-grade tumors. A consensus analysis approach, including differential abundance analysis, selection operator regression, and random forest returned a minimal set of 24 proteins associated with BC subtypes, receptor status, and TIL scoring. Among them, a panel of 10 proteins, AGR3, BCAM, CELSR1, MIEN1, NAT1, PIP4K2B, SEC23B, THTPA, TMEM51, and ULBP2, was found to stratify the tumor subtype-specific TIFs. In particular, upregulation of BCAM and CELSR1 differentiates luminal subtypes, while upregulation of MIEN1 differentiates Her2 subtypes. Immunohistochemistry analysis showed a direct correlation between protein abundance in TIFs and intratumor expression levels for all 10 proteins. Sensitivity and specificity were estimated for this protein panel by using an independent, comprehensive breast tumor proteome dataset. The results of this analysis strongly support our data, with eight of the proteins potentially representing biomarkers for stratification of BC subtypes. Five of the most representative proteomics databases currently available were also used to estimate the potential for these selected proteins to serve as putative serological markers.
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Project description:BackgroundGastric cancer (GC) is one of the most common cancer worldwide. It is essential to identify non-invasive diagnostic and prognostic biomarkers of GC. The aim of the present study was to screen candidate biomarkers associated with the pathogenesis and prognosis of GC by a novel strategy.MethodsThe expression level of gene higher in cancer than in adjacent non-cancer tissue was defined as "positive", and the top 5% genes with "positive rate" were filtered out as candidate diagnostic biomarkers in three Gene Expression Omnibus (GEO) datasets. Further, a prognostic risk model was constructed by multivariate Cox regression analysis in GEO dataset and validated in The Cancer Genome Atlas (TCGA). The expression level of candidate biomarkers was determined in serum and serum-derived exosomes of GC patients. Moreover, the effect of biomarkers in exosomes on migration of GC cells was analyzed by transwell assay.ResultsTen candidate biomarkers (AGT, SERPINH1, WNT2, LIPG, PLAU, COL1A1, MMP7, MXRA5, CXCL1 and COL11A1) were identified with efficient diagnostic value in GC. A prognostic gene signature consisted of AGT, SERPINH1 and MMP7 was constructed and showed a good performance in predicting overall survivals in TCGA. Consistently, serum levels of the three biomarkers also showed high sensitivity and specificity in distinguishing GC patients from controls. In addition, the expression level of the three biomarkers were associated with malignant degree and decreased after surgery in GC patients. Moreover, the expression level of AGT and MMP7 in exosomes correlated positively with serum level. The exosomes derived from serum of GC patients can promote migration of SGC-7901 cells. After neutralized the expression level of three proteins in exosomes with antibodies, the migration of GC cells was obviously suppressed.ConclusionsOur findings provided a novel strategy to identify diagnostic biomarkers based on public datasets, and suggested that the three-gene signature was a candidate diagnostic and prognostic biomarker for patients with GC.
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Project description:Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy. Here we show that shotgun and targeted protein sequencing can be used to identify potential prognostic biomarkers in formalin-fixed paraffin-embedded specimens from 9 patients with PDAC with "short" survival (<12 months) and 10 patients with "long" survival (>45 months) undergoing surgical resection. A total of 24 and 147 proteins were significantly upregulated [fold change ?2 or ?0.5 and P<0.05; or different detection frequencies (?5 samples)] in patients with "short" survival (including GLUT1) and "long" survival (including C9orf64, FAM96A, CDH1 and CDH17), respectively. STRING analysis of these proteins indicated a tight protein-protein interaction network centered on TP53. Ingenuity pathway analysis linked proteins representing "activated stroma factors" and "basal tumor factors" to poor prognosis of PDAC. It also highlighted TCF1 and CTNNB1 as possible upstream regulators. Further parallel reaction monitoring verified that seven proteins were upregulated in patients with "short" survival (MMP9, CLIC3, MMP8, PRTN3, P4HA2, THBS1 and FN1), while 18 proteins were upregulated in patients with "long" survival, including EPCAM, LGALS4, VIL1, CLCA1 and TPPP3. Thus, we verified 25 protein biomarker candidates for PDAC prognosis at the tissue level. Furthermore, an activated stroma status and protein-protein interactions with TP53 might be linked to poor prognosis of PDAC.
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