Complex heatmap visualization

iMeta, Год журнала: 2022, Номер 1(3)

Опубликована: Авг. 1, 2022

Abstract Heatmap is a widely used statistical visualization method on matrix‐like data to reveal similar patterns shared by subsets of rows and columns. In the R programming language, there are many packages that make heatmaps. Among them, ComplexHeatmap package provides richest toolset for constructing highly customizable can easily establish connections between multisource information automatically concatenating adjusting list heatmaps as well complex annotations, which makes it applied in analysis fields, especially bioinformatics, find hidden structures data. this article, we give comprehensive introduction current state , including its modular design, rich functionalities, broad applications.

Язык: Английский

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Microglia: Immune and non-immune functions

Immunity, Год журнала: 2021, Номер 54(10), С. 2194 - 2208

Опубликована: Окт. 1, 2021

Язык: Английский

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Small molecule metabolites: discovery of biomarkers and therapeutic targets

Signal Transduction and Targeted Therapy, Год журнала: 2023, Номер 8(1)

Опубликована: Март 20, 2023

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks diseases. Metabolite signatures that have close proximity subject's phenotypic informative dimension, are useful for predicting diagnosis prognosis diseases as well monitoring treatments. The lack early biomarkers could poor serious outcomes. Therefore, noninvasive methods with high specificity selectivity desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool biomarker pathway analysis, revealing possible mechanisms human various deciphering therapeutic potentials. It help identify functional related variation delineate biochemical changes indicators pathological damage prior disease development. Recently, scientists established large number profiles reveal underlying networks target exploration in biomedicine. This review summarized analysis on potential value small-molecule candidate metabolites clinical events, may better diagnosis, prognosis, drug screening treatment. We also discuss challenges need be addressed fuel next wave breakthroughs.

Язык: Английский

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Proteogenomic characterization of pancreatic ductal adenocarcinoma

Cell, Год журнала: 2021, Номер 184(19), С. 5031 - 5052.e26

Опубликована: Сен. 1, 2021

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 tissues. Proteomic, phosphoproteomic, glycoproteomic analyses were used to characterize proteins their modifications. In addition, whole-genome sequencing, whole-exome methylation, RNA sequencing (RNA-seq), microRNA (miRNA-seq) performed on same tissues facilitate an integrated determine impact genomic protein expression, signaling pathways, post-translational To ensure robust downstream analyses, tumor neoplastic cellularity was assessed via multiple orthogonal strategies using features verified pathological estimation based histological review. This characterization will serve as valuable resource for community, paving way early detection identification novel therapeutic targets.

Язык: Английский

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A proteogenomic portrait of lung squamous cell carcinoma

Cell, Год журнала: 2021, Номер 184(16), С. 4348 - 4371.e40

Опубликована: Авг. 1, 2021

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape LSCC, providing deeper exposition LSCC biology potential implications. identify NSD3 as an alternative driver in FGFR1-amplified tumors and low-p63 overexpressing target survivin. SOX2 is considered undruggable, but our analyses provide rationale for exploring chromatin modifiers such LSD1 EZH2 to SOX2-overexpressing tumors. Our data support complex regulation metabolic pathways by crosstalk between post-translational modifications including ubiquitylation. Numerous immune-related observations suggest directions further investigation. Proteogenomic dissection CDKN2A mutations argue more nuanced assessment RB1 protein expression phosphorylation before declaring CDK4/6 inhibition unsuccessful. Finally, triangulation LUAD, HNSCC identified both unique common vulnerabilities. These proteogenomics resources may guide research into treatment LSCC.

Язык: Английский

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Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer

Cell, Год журнала: 2020, Номер 183(7), С. 1962 - 1985.e31

Опубликована: Ноя. 25, 2020

We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA and proteomics phosphoproteomics profiling, of 218 tumors across 7 histological types childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span boundaries, suggesting treatments used for one type may be applied effectively to other sharing similar features. Immune landscape characterization reveals diverse microenvironments within diagnoses. further reveal functional effects somatic mutations copy number variations (CNVs) not evident in transcriptomics data. Kinase-substrate association co-expression network analysis important mechanisms tumorigenesis. This is the first large-scale traditional boundaries uncover foundational pediatric biology inform rational treatment selection.

Язык: Английский

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Machine learning for multi-omics data integration in cancer

iScience, Год журнала: 2022, Номер 25(2), С. 103798 - 103798

Опубликована: Янв. 22, 2022

Multi-omics data analysis is an important aspect of cancer molecular biology studies and has led to ground-breaking discoveries. Many efforts have been made develop machine learning methods that automatically integrate omics data. Here, we review tools categorized as either general-purpose or task-specific, covering both supervised unsupervised for integrative multi-omics We benchmark the performance five approaches using from Cancer Cell Line Encyclopedia, reporting accuracy on type classification mean absolute error drug response prediction, evaluating runtime efficiency. This provides recommendations researchers regarding suitable method selection their specific applications. It should also promote development novel methodologies integration, which will be essential discovery, clinical trial design, personalized treatments.

Язык: Английский

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Cancer proteogenomics: current impact and future prospects

Nature reviews. Cancer, Год журнала: 2022, Номер 22(5), С. 298 - 313

Опубликована: Март 2, 2022

Язык: Английский

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Proteogenomic characterization of 2002 human cancers reveals pan-cancer molecular subtypes and associated pathways

Nature Communications, Год журнала: 2022, Номер 13(1)

Опубликована: Май 13, 2022

Abstract Mass-spectrometry-based proteomic data on human tumors—combined with corresponding multi-omics data—present opportunities for systematic and pan-cancer proteogenomic analyses. Here, we assemble a compendium dataset of proteomics 2002 primary tumors from 14 cancer types 17 studies. Protein expression genes broadly correlates mRNA levels or copy number alterations (CNAs) across tumors, but notable exceptions. Based unsupervised clustering, separate into 11 distinct proteome-based subtypes spanning multiple tissue-based types. Two are enriched brain one subtype associating MYC, Wnt, Hippo pathways high CNA burden, another metabolic low burden. Somatic alteration in pathway associates higher activity as inferred by proteome transcriptome data. A substantial fraction cancers shows MYC without gain mutations noncanonical roles MYC. Our proteogenomics survey reveals the interplay between genome tumor lineages.

Язык: Английский

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Transcriptome analysis reveals tumor microenvironment changes in glioblastoma

Cancer Cell, Год журнала: 2023, Номер 41(4), С. 678 - 692.e7

Опубликована: Март 9, 2023

A better understanding of transcriptional evolution IDH-wild-type glioblastoma may be crucial for treatment optimization. Here, we perform RNA sequencing (RNA-seq) (n = 322 test, n 245 validation) on paired primary-recurrent resections patients treated with the current standard care. Transcriptional subtypes form an interconnected continuum in a two-dimensional space. Recurrent tumors show preferential mesenchymal progression. Over time, hallmark genes are not significantly altered. Instead, tumor purity decreases over time and is accompanied by co-increases neuron oligodendrocyte marker and, independently, tumor-associated macrophages. decrease observed endothelial genes. These composition changes confirmed single-cell RNA-seq immunohistochemistry. An extracellular matrix-associated gene set increases at recurrence bulk, RNA, immunohistochemistry indicate it expressed mainly pericytes. This signature associated worse survival recurrence. Our data demonstrate that glioblastomas evolve microenvironment (re-)organization rather than molecular cells.

Язык: Английский

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