A Normalization-Free and Nonparametric Method Sharpens Large-Scale Transcriptome Analysis and Reveals Common Gene Alteration Patterns in Cancers

Li, Qi-Gang; He, Yonghan; Wu, Huan; Yang, Cuiping; Pu, Shao-Yan; Fan, Songqing; Jiang, Liping; Shen, Qingwu; Wang, Xiaoxiong; Chen, Xiao-Qiong; Yu, Qin; Liu, Ying; Sun, Chang; Wang, Xiangting; Zhou, Jumin; Li, Haipeng; Chen, Yongbin; Kong, Qing‐Peng

doi:10.7150/thno.19425

Cited by 13 publications

(24 citation statements)

References 36 publications

(46 reference statements)

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“…To comprehensively identify the gene set that was specifically expressed in AIS tissue, we applied two differential expression analysis methods, edgeR [ 11 ] and CVAA [ 12 ], based on the gene expression profiles of paired normal and AIS, AIS and invasive cancer samples. The edgeR is one of the most widely used differential expression (DE) analysis method, while CVAA is a newly developed normalization-free and nonparametric DE analysis method.…”

Section: Resultsmentioning

confidence: 99%

“…The identification of the differentially expressed genes is critical in cancer studies. Several computational methods for differential expression analysis were developed [ 11 , 12 , 40 , 42 ]. Most of these methods are normalized based and assume the distribution of the gene expression profile.…”

Section: Discussionmentioning

confidence: 99%

“…The threshold |log2 fold change > 1| & FDR < 0.05 was used in our study for defining significantly differentially expressed genes. The R package of the CVAA (version 0.1.0) method was obtained from the author and applied under the default setting [ 12 ]. The genes were ranked by CVAA based on the significance of the differential expression.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, we first identified the genes that were specifically expressed in AIS tissue samples compared with normal and invasive cancer cases simultaneously. The differential expression analysis was performed by using two computational methods, the most widely used edgeR [ 11 ] and the newly developed Cross-Value Association Analysis (CVAA) [ 12 ]. The combined results of these two methods were used for downstream analysis.…”

Section: Introductionmentioning

confidence: 99%

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Genomic analyses based on pulmonary adenocarcinoma in situ reveal early lung cancer signature

Yang

Zhang

et al. 2018

BMC Med Genomics

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BackgroundNon-small cell lung cancer (NSCLC) represents more than about 80% of the lung cancer. The early stages of NSCLC can be treated with complete resection with a good prognosis. However, most cases are detected at late stage of the disease. The average survival rate of the patients with invasive lung cancer is only about 4%. Adenocarcinoma in situ (AIS) is an intermediate subtype of lung adenocarcinoma that exhibits early stage growth patterns but can develop into invasion.MethodsIn this study, we used RNA-seq data from normal, AIS, and invasive lung cancer tissues to identify a gene module that represents the distinguishing characteristics of AIS as AIS-specific genes. Two differential expression analysis algorithms were employed to identify the AIS-specific genes. Then, the subset of the best performed AIS-specific genes for the early lung cancer prediction were selected by random forest. Finally, the performances of the early lung cancer prediction were assessed using random forest, support vector machine (SVM) and artificial neural networks (ANNs) on four independent early lung cancer datasets including one tumor-educated blood platelets (TEPs) dataset.ResultsBased on the differential expression analysis, 107 AIS-specific genes that consisted of 93 protein-coding genes and 14 long non-coding RNAs (lncRNAs) were identified. The significant functions associated with these genes include angiogenesis and ECM-receptor interaction, which are highly related to cancer development and contribute to the smoking-free lung cancers. Moreover, 12 of the AIS-specific lncRNAs are involved in lung cancer progression by potentially regulating the ECM-receptor interaction pathway. The feature selection by random forest identified 20 of the AIS-specific genes as early stage lung cancer signatures using the dataset obtained from The Cancer Genome Atlas (TCGA) lung adenocarcinoma samples. Of the 20 signatures, two were lncRNAs, BLACAT1 and CTD-2527I21.15 which have been reported to be associated with bladder cancer, colorectal cancer and breast cancer. In blind classification for three independent tissue sample datasets, these signature genes consistently yielded about 98% accuracy for distinguishing early stage lung cancer from normal cases. However, the prediction accuracy for the blood platelets samples was only 64.35% (sensitivity 78.1%, specificity 50.59%, and AUROC 0.747).ConclusionsThe comparison of AIS with normal and invasive tumor revealed diseases-specific genes and offered new insights into the mechanism underlying AIS progression into an invasive tumor. These genes can also serve as the signatures for early diagnosis of lung cancer with high accuracy. The expression profile of gene signatures identified from tissue cancer samples yielded remarkable early cancer prediction for tissues samples, however, relatively lower accuracy for boold platelets samples.Electronic supplementary materialThe online version of this article (10.1186/s12920-018-0413-3) contains supplementary material, which is available to authorize...

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Genomic analyses based on pulmonary adenocarcinoma in situ reveal early lung cancer signature

Yang

Zhang

et al. 2018

BMC Med Genomics

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“…Given its key role in DNA replication, we hypothesize that over-expression of TICRR may contribute to rapid cellular proliferation of cancer cells via accelerating the hyper-replication of DNA. Indeed, among the numerous differentially expressed genes (DEGs) recently identified via analyzing 5,540 cancerous transcriptomes (15), we found that TICRR is consistently up-regulated expression across all the cancer types under analysis.…”

Section: Introductionmentioning

confidence: 96%

TICRR Contributes to Tumorigenesis Through Accelerating DNA Replication in Cancers

et al. 2019

Front. Oncol.

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DNA replication is precisely regulated in cells and its dysregulation can trigger tumorigenesis. Here we identified that the TOPBP1 interacting checkpoint and replication regulator ( TICRR ) mRNA level was universally and highly expressed in 15 solid cancer types. Depletion of TICRR significantly inhibited tumor cell growth, colony formation and migration in vitro , and strikingly inhibited tumor growth in the xenograft model. We reveal that knockdown of TICRR inhibited not only the initiation but also the fork progression of DNA replication. Suppression of DNA synthesis by TICRR silencing caused DNA damage accumulation, subsequently activated the ATM/CHK2 dependent p53 signaling, and finally induced cell cycle arrest and apoptosis at least in p53-wild cancer cells. Further, we show that a higher TICRR level was associated with poorer overall survival (OS) and disease free survival (DFS) in multiple cancer types. In conclusion, our study shows that TICRR is involved in tumorigenesis by regulating DNA replication, acting as a common biomarker for cancer prognosis and could be a promising target for drug-development and cancer treatment.

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MrgprF acts as a tumor suppressor in cutaneous melanoma by restraining PI3K/Akt signaling

Shen

Han

et al. 2022

Sig Transduct Target Ther

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The incidence of cutaneous melanoma (CM) has been increasing annually worldwide. In this study, we identify that MrgprF, a MAS related GPR family member, is decreased in cutaneous melanoma tissues and cell lines due to hypermethylation of its promoter region, and show that patients with CM expressing high levels of MrgprF exhibit an improved clinical outcome. We demonstrate that MrgprF forced expression inhibits tumor cell proliferation, migration, xenograft tumor growth, and metastasis. On the contrary, MrgprF knockdown promotes tumor cell proliferation and transformation of immortalized human keratinocyte-HaCaT cells, supporting the inhibitory role of MrgprF during tumor progression. Mechanistic studies reveal that MrgprF reduces the phosphoinositol‑3‑kinase (PI3K) complex formation between p101 and p110γ subunits, the critical step for phosphatidylinositol-(3, 4)-P2 (PIP2) conversion to phosphatidylinositol-(3, 4, 5)-P3 (PIP3), and then reduces the activation of PI3K/Akt signaling. This effect can be reversed by Akt specific agonist SC79. In addition, AMG 706, a previously documented inhibitor for endothelial cell proliferation, is identified as a potential agonist for MrgprF, and can impede tumor growth both in vitro and in vivo. Taken together, our findings suggest that MrgprF, a novel tumor suppressor in cutaneous melanoma, may be useful as a therapeutic target in the future.

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A Normalization-Free and Nonparametric Method Sharpens Large-Scale Transcriptome Analysis and Reveals Common Gene Alteration Patterns in Cancers

Cited by 13 publications

References 36 publications

Genomic analyses based on pulmonary adenocarcinoma in situ reveal early lung cancer signature

Genomic analyses based on pulmonary adenocarcinoma in situ reveal early lung cancer signature

TICRR Contributes to Tumorigenesis Through Accelerating DNA Replication in Cancers

MrgprF acts as a tumor suppressor in cutaneous melanoma by restraining PI3K/Akt signaling

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