4D-DIA proteomics and transcriptomics of thyroid cancer subtypes
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ABSTRACT: Formalin-fixed paraffin-embedded (FFPE) tissue samples were obtained from 120 patients, including anaplastic thyroid carcinoma (ATC, n=35), poorly differentiated thyroid carcinoma (PDTC, n=18), papillary thyroid carcinoma (PTC, n=37), and adjacent normal tissues (N, n=30). All samples were confirmed histologically using hematoxylin and eosin (HE) staining and classified according to the 2022 WHO Classification of Thyroid Tumors. For 4D-DIA proteomics analysis, 34 ATC samples, 18 PDTC samples, 36 PTC samples, and 30 N samples passed quality control for further examination. Shanghai OE Biotech Company provided services in micro-proteomics. Proteins were extracted from FFPE sections after dewaxing and rehydration, using the iST Sample Preparation Kit 8x. ue to RNA degradation and occasionally sample contamination in some of the FFPE samples, 10 ATC samples, 5 PDTC samples, 31 PTC samples, and 23 N samples passed quality control for further examination. Shanghai OE Biotech Company offered total transcriptome sequencing services, covering mRNA, lncRNA, and circRNA. Total RNA was isolated from FFPE samples using the RecoverAll Total Nucleic Acid Isolation Kit. Libraries were constructed from fragmented RNA and sequenced on the Illumina NovaSeq 6000 platform. Clean reads were obtained by trimming adapters and filtering low-quality sequences using Fastq. Reads were aligned to the human genome with HISAT2, and gene expression levels were quantified as FPKM.
Project description:Thyroid cancer (TC) is a broad classification of neoplasms that includes differentiated thyroid cancer (DTC) as a common histological subtype. DTC is characterized by an increased mortality rate in advanced stages, which contributes to the overall high mortality rate of DTC. This progression is mainly attributed to alterations in molecular driver genes, resulting in changes in phenotypes such as invasion, metastasis and dedifferentiation. Clinical management of DTC is challenging due to insufficient diagnostic and therapeutic options. The advent of-omics technology has presented a promising avenue for the diagnosis and treatment of DTC. Identifying molecular markers that can predict the early progression of DTC to a late adverse outcome is essential for precise diagnosis and treatment. The present review aimed to enhance our understanding of DTC by integrating big data with biological systems through-omics technology, specifically transcriptomics and proteomics, which can shed light on the molecular mechanisms underlying carcinogenesis.
Project description:Thyroid cancer is one of the most common endocrine cancers, with an increasing trend in the last few decades. Although papillary thyroid cancer is the most frequent subtype compared with follicular or anaplastic thyroid cancer, it can dedifferentiate to a more aggressive phenotype, and the recurrence rate is high. The cells of follicular adenomas and follicular carcinomas appear identical in cytology, making the preoperative diagnosis difficult. On the other hand, anaplastic thyroid cancer poses a significant clinical challenge due to its aggressive nature with no effective therapeutic options. In the past several years, the roles of genetic alterations of thyroid tumors have been documented, with a remarkable correlation between genotype and phenotype, indicating that distinct molecular changes are associated with a multistep tumorigenic process. Besides mRNA expression profiles, small noncoding microRNA (miRNA) expression also showed critical functions for cell differentiation, proliferation, angiogenesis, and resistance to apoptosis and finally activating invasion and metastasis in cancer. Several high-throughput sequencing studies demonstrate that miRNA expression signatures contribute clinically relevant information including types of thyroid cancer, tumor grade, and prognosis. This review summarizes recent findings on miRNA signatures in thyroid cancer subtypes.
Project description:BackgroundThe Colorectal Cancer Subtyping Consortium established four Consensus Molecular Subtypes (CMS) in colorectal cancer: CMS1 (microsatellite-instability [MSI], Immune), CMS2 (Canonical, epithelial), CMS3 (Metabolic), and CMS4 (Mesenchymal). However, only MSI tumour patients have seen a change in their disease management in clinical practice. This study aims to characterise the proteome of colon cancer CMS and broaden CMS's clinical utility.MethodsOne-hundred fifty-eight paraffin samples from stage II-III colon cancer patients treated with adjuvant chemotherapy were analysed through DIA-based mass-spectrometry proteomics.ResultsCMS1 exhibited overexpression of immune-related proteins, specifically related to neutrophils, phagocytosis, antimicrobial response, and a glycolytic profile. These findings suggested potential therapeutic strategies involving immunotherapy and glycolytic inhibitors. CMS3 showed overexpression of metabolic proteins. CMS2 displayed a heterogeneous protein profile. Notably, two proteomics subtypes within CMS2, with different protein characteristics and prognoses, were identified. CMS4 emerged as the most distinct group, featuring overexpression of proteins related to angiogenesis, extracellular matrix, focal adhesion, and complement activation. CMS4 showed a high metastatic profile and suggested possible chemoresistance that may explain its worse prognosis.ConclusionsDIA proteomics revealed new features for each colon cancer CMS subtype. These findings provide valuable insights into potential therapeutic targets for colorectal cancer subtypes in the future.
Project description:BackgroundPreviously, a total of five breast cancer subtypes have been identified based on variation in gene expression patterns. These expression profiles were also shown to be associated with different prognostic value. In this study tumour samples from 27 breast cancer patients, previously subtyped by expression analysis using DNA microarrays, and four controls from normal breast tissue were included. A new MetriGenix 4D array proposed for diagnostic use was evaluated.MethodsWe applied MetriGenix custom 4D arrays for the detection of previously defined molecular subtypes of breast cancer. MetriGenix 4D arrays have special features including probe immobilization in microchannels with chemiluminescence detection that enable shorter hybridization time.ResultsThe MetriGenix 4D array platform was evaluated with respect to both the accuracy in classifying the samples as well as the performance of the system itself. In a cross validation analysis using "Nearest Shrunken Centroid classifier" and the PAM software, 77% of the samples were classified correctly according to earlier classification results.ConclusionThe system shows potential for fast screening; however, improvements are needed.
Project description:The deadliest anaplastic thyroid cancer (ATC) often transforms from indolent differentiated thyroid cancer (DTC); however, the complex intratumor transformation process is poorly understood. We investigated an anaplastic transformation model by dissecting both cell lineage and cell fate transitions using single-cell transcriptomic and genetic alteration data from patients with different subtypes of thyroid cancer. The resulting spectrum of ATC transformation included stress-responsive DTC cells, inflammatory ATC cells (iATCs), and mitotic-defective ATC cells and extended all the way to mesenchymal ATC cells (mATCs). Furthermore, our analysis identified 2 important milestones: (a) a diploid stage, in which iATC cells were diploids with inflammatory phenotypes and (b) an aneuploid stage, in which mATCs gained aneuploid genomes and mesenchymal phenotypes, producing excessive amounts of collagen and collagen-interacting receptors. In parallel, cancer-associated fibroblasts showed strong interactions among mesenchymal cell types, macrophages shifted from M1 to M2 states, and T cells reprogrammed from cytotoxic to exhausted states, highlighting new therapeutic opportunities for the treatment of ATC.
Project description:Breast cancer is a heterogeneous disease comprising a variety of entities with various genetic backgrounds. Estrogen receptor-positive, human epidermal growth factor receptor 2-negative tumors typically have a favorable outcome; however, some patients eventually relapse, which suggests some heterogeneity within this category. In the present study, we used proteomics and miRNA profiling techniques to characterize a set of 102 either estrogen receptor-positive (ER+)/progesterone receptor-positive (PR+) or triple-negative formalin-fixed, paraffin-embedded breast tumors. Protein expression-based probabilistic graphical models and flux balance analyses revealed that some ER+/PR+ samples had a protein expression profile similar to that of triple-negative samples and had a clinical outcome similar to those with triple-negative disease. This probabilistic graphical model-based classification had prognostic value in patients with luminal A breast cancer. This prognostic information was independent of that provided by standard genomic tests for breast cancer, such as MammaPrint, OncoType Dx and the 8-gene Score.
Project description:The consensus molecular subtypes (CMS) of colorectal cancer (CRC) is the most widely-used gene expression-based classification and has contributed to a better understanding of disease heterogeneity and prognosis. Nevertheless, CMS intratumoral heterogeneity restricts its clinical application, stressing the necessity of further characterizing the composition and architecture of CRC. Here, we used Spatial Transcriptomics (ST) in combination with single-cell RNA sequencing (scRNA-seq) to decipher the spatially resolved cellular and molecular composition of CRC. In addition to mapping the intratumoral heterogeneity of CMS and their microenvironment, we identified cell communication events in the tumor-stroma interface of CMS2 carcinomas. This includes tumor growth-inhibiting as well as -activating signals, such as the potential regulation of the ETV4 transcriptional activity by DCN or the PLAU-PLAUR ligand-receptor interaction. Our study illustrates the potential of ST to resolve CRC molecular heterogeneity and thereby help advance personalized therapy.
Project description:The availability of data from profiling of cancer patients with multiomics is rapidly increasing. However, integrative analysis of such data for personalized target identification is not trivial. Multiomics2Targets is a platform that enables users to upload transcriptomics, proteomics, and phosphoproteomics data matrices collected from the same cohort of cancer patients. After uploading the data, Multiomics2Targets produces a report that resembles a research publication. The uploaded matrices are processed, analyzed, and visualized using the tools Enrichr, KEA3, ChEA3, Expression2Kinases, and TargetRanger to identify and prioritize proteins, genes, and transcripts as potential targets. Figures and tables, as well as descriptions of the methods and results, are automatically generated. Reports include an abstract, introduction, methods, results, discussion, conclusions, and references and are exportable as citable PDFs and Jupyter Notebooks. Multiomics2Targets is applied to analyze version 3 of the Clinical Proteomic Tumor Analysis Consortium (CPTAC3) pan-cancer cohort, identifying potential targets for each CPTAC3 cancer subtype. Multiomics2Targets is available from https://multiomics2targets.maayanlab.cloud/.
Project description:BackgroundThyroid cancer is the most common endocrine tumor with a steady increase in incidence. It is classified into multiple histopathological subtypes with potentially distinct molecular mechanisms. Identifying the most relevant genes and biological pathways reported in the thyroid cancer literature is vital for understanding of the disease and developing targeted therapeutics.ResultsWe developed a large-scale text mining system to generate a molecular profiling of thyroid cancer subtypes. The system first uses a subtype classification method for the thyroid cancer literature, which employs a scoring scheme to assign different subtypes to articles. We evaluated the classification method on a gold standard derived from the PubMed Supplementary Concept annotations, achieving a micro-average F1-score of 85.9% for primary subtypes. We then used the subtype classification results to extract genes and pathways associated with different thyroid cancer subtypes and successfully unveiled important genes and pathways, including some instances that are missing from current manually annotated databases or most recent review articles.ConclusionsIdentification of key genes and pathways plays a central role in understanding the molecular biology of thyroid cancer. An integration of subtype context can allow prioritized screening for diagnostic biomarkers and novel molecular targeted therapeutics. Source code used for this study is made freely available online at https://github.com/chengkun-wu/GenesThyCan.
Project description:Triple-negative breast cancer (TNBC), a heterogeneous tumour that lacks the expression of oestrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2), is often characterized by aggressiveness and tends to recur or metastasize. TNBC lacks therapeutic targets compared with other subtypes and is not sensitive to endocrine therapy or targeted therapy except chemotherapy. Therefore, identifying the prognostic characteristics and valid therapeutic targets of TNBC could facilitate early personalized treatment. Due to the rapid development of various technologies, researchers are increasingly focusing on integrating 'big data' and biological systems, which is referred to as 'omics', as a means of resolving it. Transcriptomics and proteomics analyses play an essential role in exploring prospective biomarkers and potential therapeutic targets for triple-negative breast cancers, which provides a powerful engine for TNBC's therapeutic discovery when combined with complementary information. Here, we review the recent progress of TNBC research in transcriptomics and proteomics to identify possible therapeutic goals and improve the survival of patients with triple-negative breast cancer. Also, researchers may benefit from this article to catalyse further analysis and investigation to decipher the global picture of TNBC cancer.