Eight-gene signature predicts recurrence in lung adenocarcinoma

Yong-jian, Zhang; Fan, Qingxia; Guo, Yingying; Zhu, Koujun

doi:10.3233/cbm-190329

Cited by 10 publications

(8 citation statements)

References 37 publications

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“…In a rat carotid balloon injury model, MYBPH could inhibit vascular smooth muscle cell migration and attenuate neointimal hyperplasia by inhibiting ROCK1 [17]. Recent studies have suggested that MYBPH can be used to predict the prognosis of invasive breast cancer and lung adenocarcinoma [18,19]. In the present study, we showed that knockdown of MYBPH attenuated cell migration and reduced tumorigenicity in vivo.…”

Section: Discussionsupporting

confidence: 60%

High Myosin Binding Protein H Expression Predicts Poor Prognosis in Glioma Patients

Yan¹,

Zhang²,

Guo³

et al. 2021

Preprint

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Glioma is the most common and fatal primary brain tumor in humans. Myosin binding protein H (MYBPH), which was first identified as an important myofibrillar constituent of vertebrate skeletal and cardiac muscles, reduces cell motility and metastasis. However, its role in gliomas remains unclear. We evaluated the expression of MYBPH in glioma using Gene Expression Profiling Interactive Analysis (http://gepia.cancer-pku.cn/) and Chinese Glioma Genome Atlas (https://www.cgga.org.cn/). The results showed that MYBPH was highly expressed in glioma tissues. Moreover, MYBPH expression was significantly associated with high tumor aggressiveness and poor outcomes in glioma patients. Mechanistically, the results suggested that MYBPH might promote tumor progression by improving tumor invasion and migration. Our results establish MYBPH as an important prognostic biomarker that could be considered a potential epigenetic and immunotherapeutic target for treatment. We showed that MYBPH is a novel biomarker that is variably expressed in glioblastoma (GBM). The association of high MYBPH expression with poor prognosis in newly diagnosed GBM patients and increased expression in recurrent GBM is indicative of its role in tumor aggressiveness.

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Section: Discussionsupporting

confidence: 60%

High Myosin Binding Protein H Expression Predicts Poor Prognosis in Glioma Patients

Yan¹,

Zhang²,

Guo³

et al. 2021

Preprint

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“…Compared with their signatures, TSISig had a higher AUC value in terms of predicting prognosis which indicated TSISig might have a better predictive ability. Although compared with the genes signature of Yongjian Zhang et al TSISig had a lower AUC value, 33 TSISig was the first genes signature that could simultaneously predict the recurrence of LUAD and the efficacy of immunotherapy and radiotherapy. Further functional verification showed that TSISig was negatively correlated with immune score and positively correlated with mRNAsi.…”

Section: Discussionmentioning

confidence: 91%

Tumor stemness and immune infiltration synergistically predict response of radiotherapy or immunotherapy and relapse in lung adenocarcinoma

et al. 2021

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Cancer stem cells (CSCs) have been shown to accelerate tumor recurrence, radiotherapy, and chemotherapy resistance. Immunotherapy is a powerful anticancer treatment that can significantly prolong the overall survival of patients with lung adenocarcinoma (LUAD). However, little is known about the function of genes related to tumor stemness and immune infiltration in LUAD. After integrating the tumor stemness index based on mRNA expression (mRNAsi), immune score, mRNA expression, and clinical information from the TCGA database, we screened 380 tumor stemness and immune (TSI)‐related genes and constructed a five TSI‐specific‐gene (CPS1, CCR2, NT5E, ANLN, and ABCC2) signature (TSISig) using a machine learning method. Survival analysis indicated that TSISig could stably predict the prognosis of patients with LUAD. Comparison of mRNAsi and immune score between high‐ and low‐TSISig groups suggested that TSISig characterized tumor stemness and immune infiltration. In addition, enrichment of immune subpopulations showed that the low‐TSISig group held more immune subpopulations. GSEA revealed that TSISig had a strong association with the cell cycle and human immune response. Further analysis revealed that TSISig not only had a good predictive ability for prognosis but could also serve as an excellent predictor of tumor recurrence and response to radiotherapy and immunotherapy in LUAD patients. TSISig might regulate the development of LUAD by coordinating tumor stemness and immune infiltration. Finally, a connectivity map (CMap) analysis demonstrated that the HDAC inhibitor could target TSISig.

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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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Eight-gene signature predicts recurrence in lung adenocarcinoma

Cited by 10 publications

References 37 publications

High Myosin Binding Protein H Expression Predicts Poor Prognosis in Glioma Patients

High Myosin Binding Protein H Expression Predicts Poor Prognosis in Glioma Patients

Tumor stemness and immune infiltration synergistically predict response of radiotherapy or immunotherapy and relapse in lung adenocarcinoma

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

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