KRT8 Serves as a Novel Biomarker for LUAD and Promotes Metastasis and EMT via NF-κB Signaling

Chen, Hao; Chen, Xiaobin; Pan, Bo; Zheng, Chao-Hui; Hong, Lei; Han, Wei

doi:10.3389/fonc.2022.875146

Cited by 13 publications

(12 citation statements)

References 31 publications

(42 reference statements)

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“…According to former research, EPCAM, NKD1, NTRK2, ZEB2, VIM, KRT8, KLF4, and YWHAZ have been used as prognostic markers or have been recognized as potential markers for NSCLC (25)(26)(27)(28)(29)(30)(31). Therefore, we identified two new potential biomarker genes: ESAM and NES.…”

Section: Discussionmentioning

confidence: 97%

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Identification of Biomarker Genes Based on Multi-Omics Analysis in Non-Small Cell Lung Cancer

Liu

et al. 2022

Preprint

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Background: Non-small cell lung cancer (NSCLC) is a complex disease with a high mortality rate and a poor prognosis, but its molecular mechanisms and effective biomarkers are still unclear. Comprehensive analysis of multiple histological data can effectively exclude random events and is helpful in improving the reliability of the findings. In this study, we used three types of omics data, RNA-seq, microRNA-seq, and DNA methylation data, from public databases to explore the potential biomarker genes of two major subtypes of NSCLC. Results: Through the combined differential analysis of multi-omics, we found 873 and 1378 potential high-risk genes in LUAD and LUSC, respectively. Then, we used WGCNA and PPI analyses to identify hub-genes and LASSO regression to construct prognostic models, and we obtained 15 prognostic genes. We also used survival analysis, univariate COX analysis, and GEO datasets to validate prognostic genes. Finally, we found ten genes associated with NSCLC, and eight of them have been reported in previous research. Conclusions: In this study, two novel biomarker genes were identified: NES and ESAM. The two genes were both gene expression down-regulation and DNA methylation up-regulation, and regulated by miR-122 and miR-154. Moreover, the NES gene can contribute to the clinical diagnosis and prognosis of NSCLC.

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Section: Discussionmentioning

confidence: 97%

“…Statistics in Medicine. 2013;32(30):5381-97. The numbers of differentially expressed genes, microRNA, and LncRNAs in NSCLC.…”

mentioning

confidence: 99%

Identification of Biomarker Genes Based on Multi-Omics Analysis in Non-Small Cell Lung Cancer

Liu

et al. 2022

Preprint

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“…These have obvious benefits to clinicians for the assessment of patient prognosis (36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49). The same data were used to construct a risk score model containing 9 factors (C1QTNF6, FSCN1, KPNA2, GLI2, TYMS, BIRC3, RBBP7, KRT8, and GPR65) for prognostic evaluation (Figure 2) (50)(51)(52)(53)(54)(55). The model was validated using external data cohorts (Figure 3) and identified as robust and accurate for prognostic evaluation (Figure 4).…”

Section: Discussionmentioning

confidence: 99%

Significance of macrophage infiltration in the prognosis of lung adenocarcinoma patients evaluated by scRNA and bulkRNA analysis

Zhu¹,

Zheng²,

Liu³

et al. 2022

Front. Immunol.

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PurposeTo investigate the significance of macrophage infiltration to the prognosis of lung adenocarcinoma.MethodsR language bioinformatics analysis technology, was used to obtain macrophage infiltration-related module genes through WGCNA (Weighted Gene Co-Expression Network Analysis). Marker genes of macrophage subtypes were identified using single-cell sequencing of lung adenocarcinoma tissue. Risk score models were constructed and validated using external data cohorts and clinical samples.ResultsAnalysis of cohorts TCGA-LUAD, GSE11969, GSE31210, GSE50081, GSE72094 and GSE8894, revealed a negative correlation between macrophage infiltration and survival. Immunohistochemical analyses of clinical samples were consistent with these data. Based on cell-cluster-markers and TAMs-related-genes, TOP8 genes were obtained (C1QTNF6, CCNB1, FSCN1, HMMR, KPNA2, PRC1, RRM2, and TK1) with a significant association to prognosis. Risk score models including 9 factors (C1QTNF6, FSCN1, KPNA2, GLI2, TYMS, BIRC3, RBBP7, KRT8, GPR65) for prognosis were constructed. The efficacy, stability and generalizability of the risk score models were validated using multiple data cohorts (GSE19188, GSE26939, GSE31210, GSE50081, GSE42127, and GSE72094).ConclusionsMacrophage infiltration negatively correlates with prognosis in patients with lung adenocarcinoma. Based on cell-cluster-markers and TAMs-related-genes, both TOP8 genes (C1QTNF6, CCNB1, FSCN1, HMMR, KPNA2, PRC1, RRM2, TK1) and risk score models using C1QTNF6, FSCN1, KPNA2, GLI2, TYMS, BIRC3, RBBP7, KRT8, GPR65 could predict disease prognosis.

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“…PYGL is significantly associated with overall survival in HNC patients and may be an independent risk factor for HNSCC prognosis [ 39 ]. Keratin 8 (KRT8) overexpression enhanced cell proliferation and migration in gastric cancer and lung cancer, while its decreased expression markedly inhibited cell proliferation, migration, and EMT process [ 40 , 41 ]. A multiscale integrated analysis figured out KRT8 as a pan-cancer early biomarker [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

Identification and Analysis of Senescence-Related Genes in Head and Neck Squamous Cell Carcinoma by a Comprehensive Bioinformatics Approach

Deng

Ruan

et al. 2022

Mediators of Inflammation

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Head and neck cancer is the sixth most frequent cancer all over the world, with the majority of subtypes of head and neck squamous cell carcinoma (HNSCC). Cellular senescence-associated genes have been confirmed to play a critical role in cancer and have the potential to be prognostic biomarkers for cancer. Clinical information of HNSCC samples and expression data were acquired from public databases. Expression profiles of genes related to cellular senescence were used to identify molecular subtypes by consensus clustering. To screen differentially expressed genes (DEGs) between different subtypes, differential analysis was performed. We used the univariate Cox regression to identify prognostic DEGs and performed least absolute shrinkage and selection operator (LASSO) to optimize and construct a prognostic model. CIBERSORT, ESTIMATE, and TIDE tools were applied to estimate immune characteristics. Four molecular subtypes were established based on cellular senescence-associated genes. Differential prognosis was observed among different subtypes with C4 having the longest overall survival and C1 having the worst prognosis. C4 subtype also showed the highest immune infiltration. We screened a total of eight cellular senescence prognosis-related genes and established a cellular senescence-related signature score (CSRS.Score) that could stratify samples into high-CSRS.Score and low-CSRS.Score groups. The high-CSRS.Score group had worse prognosis, lower immune infiltration, and lower response to immunotherapy. We further improved the prognostic model and survival prediction by combining CSRS.Score with clinicopathological features using a decision tree model, which had high predictive accuracy and survival prediction. This study demonstrated an important role of cellular senescence in HNSCC. The identified eight cellular senescence-associated genes have the potential to provide ideas for adjuvant treatment and personalized treatment of HNSCC patients.

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KRT8 Serves as a Novel Biomarker for LUAD and Promotes Metastasis and EMT via NF-κB Signaling

Cited by 13 publications

References 31 publications

Identification of Biomarker Genes Based on Multi-Omics Analysis in Non-Small Cell Lung Cancer

Identification of Biomarker Genes Based on Multi-Omics Analysis in Non-Small Cell Lung Cancer

Significance of macrophage infiltration in the prognosis of lung adenocarcinoma patients evaluated by scRNA and bulkRNA analysis

Identification and Analysis of Senescence-Related Genes in Head and Neck Squamous Cell Carcinoma by a Comprehensive Bioinformatics Approach

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