Pan-organ transcriptome variation across 21 cancer types

Hu, Wangxiong; Yang, Yanmei; Li, Xiaofen; Zheng, Shu

doi:10.18632/oncotarget.14303

Cited by 7 publications

(5 citation statements)

References 39 publications

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“…Two other studies have demonstrated that expression signatures can help classify the tissue for cancers of unknown primary origin, which presents a possible application of using the transcriptome signatures with tissue specificity in oncology (45, 46). Our work, besides adding novel knowledge to this field, corroborates studies such as that from Hu et al, which showed that in cancer, there is a decrease in the expression of some tissue-specific genes, and Pei et al, which showed that it is common for cancers to acquire specific expression profiles from other organs (47, 48).…”

Section: Discussionsupporting

confidence: 90%

Pan-cancer transcriptomic analysis reveals a tissue-specific loss of identity

Santos

Chatsirisupachai

Avelar

et al. 2022

Preprint

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Understanding changes in cell identity in cancer and ageing is of great importance. In this work, we analyzed how gene expression changes in human tissues are associated with tissue specificity during cancer and ageing using transcriptome data from TCGA and GTEx. We found a loss of cell identity during ageing in around 40% of tissues. In cancer, we observed a pattern of downregulation of genes specific to the tissue of origin of the tumor for most cancer types. Moreover, we observed an activation of genes not usually expressed in a tissue as well as an upregulation of genes specific to other tissues in cancer. These patterns in cancer were associated with patient survival. The age of the patient, however, does not influence these patterns. Thus, we identified a moderate but statistically significant pattern of loss of cell identity in ageing, and a clear pattern in cancer, where during tumorigenesis, cells express genes specific to other organs while suppressing the expression of genes from their original tissue. The loss of cellular identity observed in cancer is associated with prognosis and is not influenced by age, suggesting that it is a crucial stage in carcinogenesis.

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Section: Discussionsupporting

confidence: 90%

Pan-cancer transcriptomic analysis reveals a tissue-specific loss of identity

Santos

Chatsirisupachai

Avelar

et al. 2022

Preprint

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“…All level three mRNA expression datasets (RNASeqV2) were obtained from the TCGA (October 2015). Gene expression data analysis was performed similar to our previous work (12). Briefly, differentially expressed mRNA analysis between LCC and RCC was performed by the limma package for R/Bioconductor.…”

Section: Gene Expression Data Processing and Normalizationmentioning

confidence: 99%

“…Matched mRNA-miRNA samples were retained for coexpression analysis. The coexpression network was constructed by weighted correlation network analysis (WGCNA) package for R (15) and explored as in our previous work (12). The subnetworks constituted by the 50 top hub genes in the specific module were visualized by VisANT (16).…”

Section: Network Constructionmentioning

confidence: 99%

Multi-omics Approach Reveals Distinct Differences in Left- and Right-Sided Colon Cancer

Yang

et al. 2018

Molecular Cancer Research

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Increasing evidence suggests that left-sided colon cancer (LCC) and right-sided colon cancer (RCC) are emerging as two different colorectal cancer types with distinct clinical characteristics. However, the discrepancy in the underlying molecular event between these types of cancer has not been thoroughly elucidated to date and warrants comprehensive investigation. To this end, an integrated dataset from The Cancer Genome Atlas was used to compare and contrast LCC and RCC, covering mutation, DNA methylation, gene expression, and miRNA. Briefly, the signaling pathway cross-talk is more prevalent in RCC than LCC, such as RCC-specific PI3K pathway, which often exhibits cross-talk with the RAS and P53 pathways. Meanwhile, methylation signatures revealed that RCC was hypermethylated relative to LCC. In addition, differentially expressed genes ( = 253) and differentially expressed miRNAs ( = 16) were determined between LCC and RCC. Especially for Prostate Cancer Susceptibility Candidate 1 (), a gene that was closely associated with hypermethylation, was the top significantly downregulated gene in RCC. Multi-omics comparison of LCC and RCC suggests that there are more aggressive markers in RCC and that tumor heterogeneity occurs within the location-based subtypes of colon cancer. These results clarify the debate regarding the conflicting prognosis between LCC and RCC, as proposed by different studies. The underlying molecular features present in LCC and RCC identified in this study are beneficial for adopting reasonable therapeutic approaches to prolong overall survival and progression-free survival in colorectal cancer patients. .

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“…NFE2L2 was positively associated with immune infiltration in pan-cancer ( Ju et al, 2020b ). Pan-cancer research has allowed us to understand that the same cancer may be very different at the molecular level ( Hu et al, 2017 ), while diverse cancers may share the same molecular profile ( Comprehensive genomic cha, 2008 ). Thereby deepening the pan-cancer level studies with a large sample size will hopefully discover new biomarkers which can be used to develop new cancer treatment strategies.…”

Section: Introductionmentioning

confidence: 99%

Identification of Potential Biomarkers for Pan-Cancer Diagnosis and Prognosis Through the Integration of Large-Scale Transcriptomic Data

Zhu

Xing

et al. 2022

Front. Pharmacol.

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Cancer is one of the leading causes of death worldwide, bringing a significant burden to human health and society. Accurate cancer diagnosis and biomarkers that can be used as robust therapeutic targets are of great importance as they facilitate early and effective therapies. Shared etiology among cancers suggests the existence of pan-cancer biomarkers, performance of which could benefit from the large sample size and the heterogeneity of the studied patients. In this study, we conducted a systematic RNA-seq study of 9,213 tumors and 723 para-cancerous tissue samples of 28 solid tumors from the Cancer Genome Atlas (TCGA) database, and 7,008 normal tissue samples from the Genotype-Tissue Expression (GTEx) database. By differential gene expression analysis, we identified 214 up-regulated and 186 downregulated differentially expressed genes (DEGs) in more than 80% of the studied tumors, respectively, and obtained 20 highly linked up- and downregulated hub genes from them. These markers have rarely been reported in multiple tumors simultaneously. We further constructed pan-cancer diagnostic models to classify tumors and para-cancerous tissues using 10 up-regulated hub genes with an AUC of 0.894. Survival analysis revealed that these hub genes were significantly associated with the overall survival of cancer patients. In addition, drug sensitivity predictions for these hub genes in a variety of tumors obtained several broad-spectrum anti-cancer drugs targeting pan-cancer. Furthermore, we predicted immunotherapy sensitivity for cancers based on tumor mutational burden (TMB) and the expression of immune checkpoint genes (ICGs), providing a theoretical basis for the treatment of tumors. In summary, we identified a set of biomarkers that were differentially expressed in multiple types of cancers, and these biomarkers can be potentially used for diagnosis and used as therapeutic targets.

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Pan-organ transcriptome variation across 21 cancer types

Cited by 7 publications

References 39 publications

Pan-cancer transcriptomic analysis reveals a tissue-specific loss of identity

Pan-cancer transcriptomic analysis reveals a tissue-specific loss of identity

Multi-omics Approach Reveals Distinct Differences in Left- and Right-Sided Colon Cancer

Identification of Potential Biomarkers for Pan-Cancer Diagnosis and Prognosis Through the Integration of Large-Scale Transcriptomic Data

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