Differentially expressed lncRNAs and mRNAs identified by NGS analysis in colorectal cancer patients

Li, Meng; Zhao, Lianmei; Li, Suo‐lin; Li, Jing; Gao, Bo; Wang, Fei‐fei; Wang, Sheng‐pu; Hu, Xuhua; Cao, Jian; Wang, Gui‐ying

doi:10.1002/cam4.1696

Cited by 40 publications

(53 citation statements)

References 27 publications

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“…Feodorova et al revealed that NOTUM, TCN1, MACC1, YKL40, GPC3, AXIN2, and IL6 are significantly upregulated in CRC and have roles in tumour invasion and metastasis 11 . Our previous next-generation sequencing (NGS) results verified that TCN1 is one of the top five overexpressed mRNAs in CRC 12 . Taken together, these findings indicate that TCN1 is a CRC-related gene that requires further study.…”

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confidence: 64%

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High expression of TCN1 is a negative prognostic biomarker and can predict neoadjuvant chemosensitivity of colon cancer

Liu

Wang

Yue

et al. 2020

Sci Rep

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Transcobalamin (TCN1) is a vitamin B12 (cobalamin)-binding protein that regulates cobalamin homeostasis. Recent studies and bioinformatic analyses have found that TCN1 is highly expressed in cancer tissues and is associated with tumour aggressiveness and poor prognosis. the present study aimed to detect TCN1 as a novel biomarker for prognosis and chemosensitivity of colon cancer. nextgeneration sequencing showed that TCN1 was one of several upregulated mRnAs in colon cancer, which was verified by further bioinformatics analyses. Western blotting (n = 9) and quantitative real time polymerase chain reaction (qRT-PCR, n = 30) revealed that TCN1 was highly expressed in colon cancer tissues at both the protein and mRNA level. A total of 194 cases of colon cancer were examined by immunohistochemistry and revealed that TCN1 expression level was related to advanced stages (P < 0.005). Kaplan-Meier analysis verified that patients with lower TCN1 expression usually had longer overall survival (P = 0.008). In addition, TCN1 was highly expressed in pulmonary metastatic tumour tissues (n = 37, P = 0.025) and exhibited higher levels in right-sided colon cancer than in left-sided colon cancer (P = 0.029). TCN1 expression in specimens that had received neoadjuvant chemotherapy decreased compared with that in colonoscopy biopsy tissues (n = 42, P = 0.009). Further bioinformatics analyses verified that apoptosis pathways might have a role in high TCN1 expression. All the studies revealed that TCN1 expression in colon cancer was significantly associated with malignant biological behaviour. Therefore, TCN1 could be used as a novel biomarker for colon cancer aggressiveness and prognosis and might also be a potential biomarker for predicting neoadjuvant chemosensitivity. According to the data from GLOBOCAN 2018, colorectal cancer (CRC) is one of the most common intestinal tumours and ranks as the fourth leading cause of morbidity and the second leading cause of cancer-related mortality worldwide 1. Despite major developments in surgery and therapeutics, long-term survival remains far from satisfactory 2,3 , mainly because CRC is often detected at more advanced stages. Currently, diagnosis, relapse, and metastasis monitoring of CRC largely relies on colonoscopy and imaging data, which is usually delayed. Therefore, new sensitive biomarkers are urgently required to ensure early diagnosis and to predict progression and administer timely treatments. Transcobalamin I (TCN1) is a type of vitamin B12 (cobalamin)-binding protein that transports cobalamin from the stomach to the intestines. It plays various roles in maintaining the basic function of cell proliferation and

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confidence: 64%

“…We hypothesized that TCN1 might have a role in colon cancer. The NGS results in CRC revealed that TCN1 ranked as the second most upregulated mRNA 12 . Bioinformatic analysis based on the COAD data set verified that TCN1 was indeed overexpressed in CRC.…”

Section: Discussionmentioning

confidence: 99%

High expression of TCN1 is a negative prognostic biomarker and can predict neoadjuvant chemosensitivity of colon cancer

Liu

Wang

Yue

et al. 2020

Sci Rep

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“…FIRRE upregulation is associated with poor OS time in diffuse large B-cell lymphoma, CRC and HNSCC (67). However, FIRRE upregulation is also associated with significantly improved OS time in CRC (68). The present study revealed that FIRRE was upregulated in numerous types of cancer, possibly indicating that this lncRNA behaves differently under different circumstances.…”

Section: Discussionmentioning

confidence: 50%

A putative competing endogenous RNA network in cisplatin‑resistant lung adenocarcinoma cells identifying potentially rewarding research targets

Huang

Wei

et al. 2020

Oncol Lett

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Lung adenocarcinoma (LUAD) is the most common type of non-small cell lung cancer and has a poor 5 year survival rate (<10%). Cisplatin is one of the most effective chemotherapeutic treatments for LUAD, even though it is of limited overall utility due to acquired drug resistance. To identify possible genetic targets for the mitigation of cisplatin resistance, gene expression data from cisplatin-resistant cell lines were integrated with patient information. Expression data for cisplatin-resistant and cisplatin-sensitive A549 cell lines were obtained from the Gene Expression Omnibus database, while LUAD patient data was obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed mRNAs (DEmRNAs), microRNAs (DEmiRNAs) and long non-coding RNAs (DElncRNAs) were identified between the cisplatin-sensitive and cisplatin-resistant cells. Using the TCGA patient data, 33 DEmRNAs associated with survival were identified. A total of 74 DElncRNAs co-expressed with the survival-associated DEmRNAs, and 11 DEmiRNAs that regulated the survival-associated DEmRNAs, were also identified. A competing endogenous RNA (ceRNA) network was constructed based on the aforementioned results, which included 17 survival-associated DEmRNAs, 9 DEmiRNAs and 16 DElncRNAs. This network revealed 8 ceRNA pathway axes possibly associated with cisplatin resistance in A549 cells. Specifically, the network suggested that the lncRNAs HOXD-AS2, LINC01123 and FIRRE may act as ceRNAs to increase cisplatin resistance in human LUAD cells. Therefore, it was speculated that these lncRNAs represent potentially rewarding research targets.

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“…Results shown are sorted from highest to lowest percent abundance in the colon cancer cohort. D. Fold change of top upregulated and downregulated human genes(Li et al, 2018) between CRC and matched normal samples as found in this study through MetaFunc.…”

mentioning

confidence: 60%

“…From these, we found all the top 5 upregulated and top 5 downregulated genes as reported in the source publication (Li et al, 2018). Figure 2D shows their fold change as found through MetaFunc.…”

Section: Hostmentioning

confidence: 93%

MetaFunc: Taxonomic and Functional Analyses of High Throughput Sequencing for Microbiomes

Sulit¹,

Kolisnik²,

Frizelle

et al. 2020

Preprint

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BackgroundThe identification of functional processes taking place in microbiome communities augment traditional microbiome taxonomic studies, giving a more complete picture of interactions taking place within the community. While there are applications that perform functional annotation on metagenome or metatranscriptomes, very few of these are able to link taxonomic identity to function and are limited by their input types or databases used.ResultsHere we present MetaFunc, a workflow which takes input reads, and from these 1) identifies species present in the microbiome sample and 2) provides gene ontology (GO) annotations associated with the species identified. MetaFunc can also provide a differential abundance analysis step comparing species between sample conditions. In addition, MetaFunc allows mapping of reads to a host genome, and separates these reads, before proceeding with the microbiome analyses. From the host reads, MetaFunc is able to identify host genes, perform differential gene expression analysis, and gene-set enrichment analysis. A final correlation analysis between microbial species and host genes can also be performed. Finally, MetaFunc builds an R shiny application that allows users to view and interact with the microbiome results. In this paper we show how MetaFunc can be applied to metatranscriptomic datasets of colorectal cancer.ConclusionMetaFunc is a one-stop shop microbiome analysis pipeline that can identify taxonomies and their respective functional contributions in a microbiome sample through GO annotations. It can also analyse host reads in a microbiome sample, providing information on host gene expression, and allowing for correlations between the microbiome and host genes. MetaFunc comes with a user-friendly R shiny application that allows for easier visualisation and exploration of its results. MetaFunc is freely available through https://gitlab.com/schmeierlab/workflows/metafunc.git.

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Differentially expressed lncRNAs and mRNAs identified by NGS analysis in colorectal cancer patients

Cited by 40 publications

References 27 publications

High expression of TCN1 is a negative prognostic biomarker and can predict neoadjuvant chemosensitivity of colon cancer

High expression of TCN1 is a negative prognostic biomarker and can predict neoadjuvant chemosensitivity of colon cancer

A putative competing endogenous RNA network in cisplatin‑resistant lung adenocarcinoma cells identifying potentially rewarding research targets

MetaFunc: Taxonomic and Functional Analyses of High Throughput Sequencing for Microbiomes

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