Identification of 10,000 Phosphosites and Integrative Proteomic Characterization of Trace FFPE Samples in Early-stage gastrointestinal cancer
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ABSTRACT:
ORGANISM(S): Homo Sapiens
SUBMITTER: 
Chen Ding
PROVIDER: PXD026736 | iProX | Wed Jun 16 00:00:00 BST 2021
REPOSITORIES: iProX
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Project description:BackgroundThe surveillance and therapy of early-stage cancer would be better for patients' prognosis. However, the extreme trace amount of tissue samples in different stages have limited in portraying the characterization of early-stage cancer. Therefore, we focused on and presented comprehensive proteomic and phosphoproproteomic profiling of the trace FFPE samples from early-stage gastrointestinal cancer, and then explored the potential biomarkers of early-stage gastrointestinal cancer.MethodsIn this study, a quantitative proteomic method with chromatography with mass spectrometry (LC-MS/MS) was used to analyse the proteomic difference between the trace early-stage esophageal squamous cell carcinoma (EESCC) and early-stage duodenum adenocarcinoma cancer (EDAC).ResultsWe identified ~ 6000 proteins and > 10,000 phosphosites in single trace FFPE samples. Comparative analysis disclosed the diverse proteomic features of tumor tissues compared with paired normal tissue of EESCC and EDAC, and revealed the difference of EESCC and EDAC was derived from their origin normal tissue. The distinct separation of EESCC and EDAC illustrated the functions of cell cycle (RB1 T373, EGFR T693) in EESCC, and the positive impacts of apoptosis, metabolic processes (MTOR and MTOR S1261) in EDAC. Furthermore, we deconvoluted the immune infiltration of early-stage gastrointestinal cancer, in which higher immune cell signatures were detected in EDAC, and showed the specific cytokines in EESCC and EDAC. We performed kinases-substates relationship analysis and elucidated the specific proteomic kinase characterization of EESCC and EDAC, and proposed the medicative effects and corresponding drugs for EESCC and EDAC at the clinic.ConclusionWe disclosed the specific immune characterization of the early-stage gastrointestinal cancer, and presented potential makers of EESCC (EGFR, PDGFRB, CDK4, WEE1) and EDAC (MTOR, MAP2K1, MAPK3). This study represents a major stepping stone towards investigating the carcinogenesis mechanism of gastrointestinal cancer, and providing a rich resource for medicative strategy in the clinic.
| S-EPMC8994365 | biostudies-literature
Project description:Most tissue biopsies from patients in hospital environments are formalin-fixed and paraffin-embedded (FFPE) for long-term storage. This fixation process produces a modification in the proteins called "crosslinks", which improves protein stability necessary for their conservation. Currently, these samples are mainly used in clinical practice for performing immunohistochemical analysis, since these modifications do not suppose a drawback for this technique; however, crosslinks difficult the protein extraction process. Accordingly, these modifications make the development of a good protein extraction protocol necessary. Due to the specific characteristics of each tissue, the same extraction buffers or deparaffinization protocols are not equally effective in all cases. Therefore, it is necessary to obtain a specific protocol for each tissue. The present work aims to establish a deparaffinization and protein extraction protocol from FFPE kidney samples to obtain protein enough of high quality for the subsequent proteomic analysis. Different deparaffination, protocols and protein extraction buffers will be tested in FFPE kidney samples. The optimized conditions will be applied in the identification by LC-MS/MS analysis of proteins extracted from 5, 10, and 15 glomeruli obtained through the microdissection of FFPE renal samples.
| S-EPMC8158658 | biostudies-literature
Project description:Esophageal squamous cell carcinoma (ESCC) is malignant while the carcinogenesis is still unclear. Here, we perform a comprehensive multi-omics analysis of 786 trace-tumor-samples from 154 ESCC patients, covering 9 histopathological stages and 3 phases. Proteogenomics elucidates cancer-driving waves in ESCC progression, and reveals the molecular characterization of alcohol drinking habit associated signatures. We discover chromosome 3q gain functions in the transmit from nontumor to intraepithelial neoplasia phases, and find TP53 mutation enhances DNA replication in intraepithelial neoplasia phase. The mutations of AKAP9 and MCAF1 upregulate glycolysis and Wnt signaling, respectively, in advanced-stage ESCC phase. Six major tracks related to different clinical features during ESCC progression are identified, which is validated by an independent cohort with another 256 samples. Hyperphosphorylated phosphoglycerate kinase 1 (PGK1, S203) is considered as a drug target in ESCC progression. This study provides insight into the understanding of ESCC molecular mechanism and the development of therapeutic targets.
| S-EPMC10039899 | biostudies-literature
Project description:The gene expression analysis of formalin-fixed paraffin-embedded (FFPE) tissues is often hampered by poor RNA quality, which results from the oxidation, cross-linking and other chemical modifications induced by the inclusion in paraffin. Yet, FFPE samples are a valuable source for molecular studies and can provide great insights into disease progression and prognosis. With the advancement of genomic technologies, new methods have been established that offer reliable and accurate gene expression workflows on samples of poor quality. NanoString is a probe-based technology that allows the direct counting of the mRNA transcripts and can be applied to degraded samples. Here, we have tested 2 RNA extraction methods for FFPE samples, and we have performed a titration experiment to evaluate the impact of RNA degradation and RNA input on the gene expression profiles assessed using the NanoString IO360 panel. We have selected FFPE samples of different DV200 values and assessed them on the nCounter platform with 2 different amounts of input RNA. This study concludes that the nCounter is a robust and reliable platform to assess the gene expression of RNA samples with DV200 > 30%; its robustness and ease of use could be of particular benefit to clinical settings.
| S-EPMC9706083 | biostudies-literature
Project description:Proteomic profiling of forty trace samples (20 duodenum cancer samples and 20 gastric cancer samples).
2021-06-16 | PXD026756 |
Project description:Artifact chimeric reads are enriched in next-generation sequencing data generated from formalin-fixed paraffin-embedded (FFPE) samples. Previous work indicated that these reads are characterized by erroneous split-read support that is interpreted as evidence of structural variants. Thus, a large number of false-positive structural variants are detected. To our knowledge, no tool is currently available to specifically call or filter structural variants in FFPE samples. To overcome this gap, we developed 2 R packages: SimFFPE and FilterFFPE. SimFFPE is a read simulator, specifically designed for next-generation sequencing data from FFPE samples. A mixture of characteristic artifact chimeric reads, as well as normal reads, is generated. FilterFFPE is a filtration algorithm, removing artifact chimeric reads from sequencing data while keeping real chimeric reads. To evaluate the performance of FilterFFPE, we performed structural variant calling with 3 common tools (Delly, Lumpy, and Manta) with and without prior filtration with FilterFFPE. After applying FilterFFPE, the mean positive predictive value improved from 0.27 to 0.48 in simulated samples and from 0.11 to 0.27 in real samples, while sensitivity remained basically unchanged or even slightly increased. FilterFFPE improves the performance of SV calling in FFPE samples. It was validated by analysis of simulated and real data.
| S-EPMC8458033 | biostudies-literature
Project description:MicroRNAs (miRNAs) are small non-coding RNAs responsible for fine-tuning of gene expression at post-transcriptional level. The alterations in miRNA expression levels profoundly affect human health and often lead to the development of severe diseases. Currently, high throughput analyses, such as microarray and deep sequencing, are performed in order to identify miRNA biomarkers, using archival patient tissue samples. MiRNAs are more robust than longer RNAs, and resistant to extreme temperatures, pH, and formalin-fixed paraffin-embedding (FFPE) process. Here, we have compared the stability of miRNAs in FFPE cardiac tissues using next-generation sequencing. The mode read length in FFPE samples was 11 nucleotides (nt), while that in the matched frozen samples was 22 nt. Although the read counts were increased 1.7-fold in FFPE samples, compared with those in the frozen samples, the average miRNA mapping rate decreased from 32.0% to 9.4%. These results indicate that, in addition to the fragmentation of longer RNAs, miRNAs are to some extent degraded in FFPE tissues as well. The expression profiles of total miRNAs in two groups were highly correlated (0.88 <r < 0.92). However, the relative read count of each miRNA was different depending on the GC content (p<0.0001). The unequal degradation of each miRNA affected the abundance ranking in the library, and miR-133a was shown to be the most abundant in FFPE cardiac tissues instead of miR-1, which was predominant before fixation. Subsequent quantitative PCR (qPCR) analyses revealed that miRNAs with GC content of less than 40% are more degraded than GC-rich miRNAs (p<0.0001). We showed that deep sequencing data obtained using FFPE samples cannot be directly compared with that of fresh frozen samples. The combination of miRNA deep sequencing and other quantitative analyses, such as qPCR, may improve the utility of archival FFPE tissue samples.
| S-EPMC5029930 | biostudies-literature
Project description:Gene expression analysis by RNA sequencing (RNA-seq) enables unique insights into clinical samples that can potentially lead to mechanistic understanding of the basis of various diseases as well as resistance and/or susceptibility mechanisms. However, FFPE tissues, which represent the most common method for preserving tissue morphology in clinical specimens, are not the best sources for gene expression profiling analysis. The RNA obtained from such samples is often degraded, fragmented, and chemically modified, which leads to suboptimal sequencing libraries. In turn, these generate poor quality sequence data that may not be reliable for gene expression analysis and mutation discovery. In order to make the most of FFPE samples and obtain the best possible data from low quality samples, it is important to take certain precautions while planning experimental design, preparing sequencing libraries, and during data analysis. This includes the use of appropriate metrics for precise sample quality control (QC), identifying the best methods for various steps during the sequencing library generation, and careful library QC. In addition, applying correct software tools and parameters for sequence data analysis is critical in order to identify artifacts in RNA-seq data, filter out contamination and low quality reads, assess uniformity of gene coverage, and measure the reproducibility of gene expression profiles among biological replicates. These steps can ensure high accuracy and reproducibility for profiling of very heterogeneous RNA samples. Here we describe the various steps for sample QC, library preparation and QC, sequencing, and data analysis that can help to increase the amount of useful data obtained from low quality RNA, such as that obtained from FFPE-RNA tissues.
| S-EPMC8815082 | biostudies-literature
Project description:We implemented an optimized processing, using alternative Chip Description Files (CDFs) and fRMA normalization, which improve the quality of downstream analysis. We profiled 44 FFPE primary breast cancer samples using Affymetrix HG-U133 Plus 2.0 microarray platform after RNA amplification with the Nugen WT-Ovation FFPE System
| S-ECPF-GEOD-38554 | biostudies-other
Project description:PurposeEndometrioid endometrial carcinoma (EEC) is the major histologic type of endometrial cancer, the most prevalent gynecologic malignancy in the United States. EEC recurrence or metastasis is associated with a poor prognosis. Early-stage EEC is generally curable, but a subset has high risk of recurrence or metastasis. Prognosis estimation for early-stage EEC mainly relies on clinicopathologic characteristics, but is unreliable. We aimed to identify patients with high-risk early-stage EEC who are most likely to benefit from more extensive surgery and adjuvant therapy by building a prognostic model that integrates clinical variables and protein markers.Experimental designWe used two large, independent early-stage EEC datasets as training (n = 183) and validation cohorts (n = 333), and generated the levels of 186 proteins and phosphoproteins using reverse-phase protein arrays. By applying an initial filtering and the elastic net to the training samples, we developed a prognostic model for overall survival containing two clinical variables and 18 protein markers and optimized the risk group classification.ResultsThe Kaplan-Meier survival analyses in the validation cohort confirmed an improved discriminating power of our prognostic model for patients with early-stage EEC over key clinical variables (log-rank test, P = 0.565 for disease stage, 0.567 for tumor grade, and 1.3 × 10(-4) for the integrative model). Compared with clinical variables (stage, grade, and patient age), only the risk groups defined by the integrative model were consistently significant in both univariate and multivariate analyses across both cohorts.ConclusionsOur prognostic model is potentially of high clinical value for stratifying patients with early-stage EEC and improving their treatment strategies.
| S-EPMC4715969 | biostudies-literature