Use of four genes in exosomes as biomarkers for the identification of lung adenocarcinoma and lung squamous cell carcinoma

Cao, Bingji; Wang, Pengyu; Gu, Lina; Liu, Junfeng

doi:10.3892/ol.2021.12510

Cited by 23 publications

(13 citation statements)

References 77 publications

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“…By contrast, multiple top-ranked TFs identified for the SC class were common oncogenes shared by the involved SC types. For example, TP63 has been implicated as an oncogene in several SC types, including HNSC (head and neck squamous cell carcinoma) [51], LUSC [52], and ESSC [53]. In our analysis, TP63 was the top one TF for the SC class (Fig.…”

Section: Applying Hyperchip To a Large Pan-cancer Atac-seq Data Setmentioning

confidence: 79%

HyperChIP for identifying hypervariable signals across ChIP/ATAC-seq samples

Chen

Yuan

et al. 2021

Preprint

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With the reduction in sequencing costs, studies become prevalent that profile the chromatin landscape for tens or even hundreds of human individuals by using ChIP/ATAC-seq techniques. Identifying genomic regions with hypervariable ChIP/ATAC-seq signals across given samples is essential for such studies. In particular, the hypervariable regions (HVRs) across tumors from different patients indicate their heterogeneity and can contribute to revealing potential cancer subtypes and the associated epigenetic markers. We present HyperChIP as the first complete statistical tool for the task. HyperChIP uses scaled variances that account for the mean-variance dependence to rank genomic regions, and it increases the statistical power by diminishing the influence of true HVRs on model fitting. Applying it to a large pan-cancer ATAC-seq data set, we found that the identified HVRs not only provided a solid basis to uncover the underlying similarity structure among the involved tumor samples, but also led to the identification of transcription factors pertaining to the similarity structure when coupled with a motif-scanning analysis.

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Section: Applying Hyperchip To a Large Pan-cancer Atac-seq Data Setmentioning

confidence: 79%

HyperChIP for identifying hypervariable signals across ChIP/ATAC-seq samples

Chen

Yuan

et al. 2021

Preprint

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“…By contrast, multiple top-ranked TFs identified for the SC class were common oncogenes shared by the involved SC types. For example, TP63 has been implicated as an oncogene in several SC types, including head and neck squamous cell carcinoma (HNSC) [ 51 ], LUSC [ 52 ], and ESSC [ 53 ]. In our analysis, TP63 was the top 1 TF for the SC class (Fig.…”

Section: Resultsmentioning

confidence: 99%

HyperChIP: identification of hypervariable signals across ChIP-seq or ATAC-seq samples

Chen

Yuan

et al. 2022

Genome Biol

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Identifying genomic regions with hypervariable ChIP-seq or ATAC-seq signals across given samples is essential for large-scale epigenetic studies. In particular, the hypervariable regions across tumors from different patients indicate their heterogeneity and can contribute to revealing potential cancer subtypes and the associated epigenetic markers. We present HyperChIP as the first complete statistical tool for the task. HyperChIP uses scaled variances that account for the mean-variance dependence to rank genomic regions, and it increases the statistical power by diminishing the influence of true hypervariable regions on model fitting. A pan-cancer case study illustrates the practical utility of HyperChIP.

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“…We identify nine genes and find the four genes ( PITPNM3 , MXD4 , ABCB1 , BATF ) that are related to HNSCC in the literature ( Aravind et al, 2021 ; Wu et al, 2019b ; da Silva et al, 2021 ; Duz & Karatas, 2021 ; Wang et al, 2020 ; and Wen et al, 2015 ). We identify fifteen genes and find the seven genes ( EPB41L5 , INPP5J , KRT16 , MS4A1 , MYLIP , PEBP1 , SFTPB ) that are related to LUAD in the literature ( Li et al, 2020a ; Zhang et al, 2020a ; Yuanhua et al, 2019 ; Song et al, 2020 ; Liu et al, 2021b ; Li et al, 2020b ; Zhang et al, 2021 ; Cao et al, 2021 ; and Zhang et al, 2019 ). We identify ten genes and find the four genes ( EDA2R , PCMT1 , QPRT , SKP1 ) that are related to BRCA in the literature ( Liu, Kain & Wang, 2012 ; Kyritsis et al, 2021 ; Liu et al, 2021a ; and Tian et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

Feature screening for survival trait with application to TCGA high-dimensional genomic data

Wang¹,

Li²,

Hou³

2022

PeerJ

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Background In high-dimensional survival genomic data, identifying cancer-related genes is a challenging and important subject in the field of bioinformatics. In recent years, many feature screening approaches for survival outcomes with high-dimensional survival genomic data have been developed; however, few studies have systematically compared these methods. The primary purpose of this article is to conduct a series of simulation studies for systematic comparison; the second purpose of this article is to use these feature screening methods to further establish a more accurate prediction model for patient survival based on the survival genomic datasets of The Cancer Genome Atlas (TCGA). Results Simulation studies prove that network-adjusted feature screening measurement performs well and outperforms existing popular univariate independent feature screening methods. In the application of real data, we show that the proposed network-adjusted feature screening approach leads to more accurate survival prediction than alternative methods that do not account for gene-gene dependency information. We also use TCGA clinical survival genetic data to identify biomarkers associated with clinical survival outcomes in patients with various cancers including esophageal, pancreatic, head and neck squamous cell, lung, and breast invasive carcinomas. Conclusions These applications reveal advantages of the new proposed network-adjusted feature selection method over alternative methods that do not consider gene-gene dependency information. We also identify cancer-related genes that are almost detected in the literature. As a result, the network-based screening method is reliable and credible.

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Use of four genes in exosomes as biomarkers for the identification of lung adenocarcinoma and lung squamous cell carcinoma

Cited by 23 publications

References 77 publications

HyperChIP for identifying hypervariable signals across ChIP/ATAC-seq samples

HyperChIP for identifying hypervariable signals across ChIP/ATAC-seq samples

HyperChIP: identification of hypervariable signals across ChIP-seq or ATAC-seq samples

Feature screening for survival trait with application to TCGA high-dimensional genomic data

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