Long non-coding (lnc)RNA profiling and the role of a key regulator lnc-PNRC2-1 in the transforming growth factor-<b>β</b>1-induced epithelial–mesenchymal transition of CNE1 nasopharyngeal carcinoma cells

Yang, Jie; Feng, Enzi; Ren, Yanping; Qiu, Shun; Zhao, Liufang; Li, Xiaojiang

doi:10.1177/0300060521996515

Cited by 5 publications

(2 citation statements)

References 37 publications

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“…MYOM3 was the top-ranking feature in our genes-and-microbes model and has a higher expression in LCC as determined using differential gene expression analysis. MYOM3 has not been studied in CRC but it has been linked to clinical outcomes in renal and lung cancer [ 42 , 43 ].…”

Section: Discussionmentioning

confidence: 99%

Identifying important microbial and genomic biomarkers for differentiating right- versus left-sided colorectal cancer using random forest models

et al. 2023

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Background Colorectal cancer (CRC) is a heterogeneous disease, with subtypes that have different clinical behaviours and subsequent prognoses. There is a growing body of evidence suggesting that right-sided colorectal cancer (RCC) and left-sided colorectal cancer (LCC) also differ in treatment success and patient outcomes. Biomarkers that differentiate between RCC and LCC are not well-established. Here, we apply random forest (RF) machine learning methods to identify genomic or microbial biomarkers that differentiate RCC and LCC. Methods RNA-seq expression data for 58,677 coding and non-coding human genes and count data for 28,557 human unmapped reads were obtained from 308 patient CRC tumour samples. We created three RF models for datasets of human genes-only, microbes-only, and genes-and-microbes combined. We used a permutation test to identify features of significant importance. Finally, we used differential expression (DE) and paired Wilcoxon-rank sum tests to associate features with a particular side. Results RF model accuracy scores were 90%, 70%, and 87% with area under curve (AUC) of 0.9, 0.76, and 0.89 for the human genomic, microbial, and combined feature sets, respectively. 15 features were identified as significant in the model of genes-only, 54 microbes in the model of microbes-only, and 28 genes and 18 microbes in the model with genes-and-microbes combined. PRAC1 expression was the most important feature for differentiating RCC and LCC in the genes-only model, with HOXB13, SPAG16, HOXC4, and RNLS also playing a role. Ruminococcus gnavus and Clostridium acetireducens were the most important in the microbial-only model. MYOM3, HOXC4, Coprococcus eutactus, PRAC1, lncRNA AC012531.25, Ruminococcus gnavus, RNLS, HOXC6, SPAG16 and Fusobacterium nucleatum were most important in the combined model. Conclusions Many of the identified genes and microbes among all models have previously established associations with CRC. However, the ability of RF models to account for inter-feature relationships within the underlying decision trees may yield a more sensitive and biologically interconnected set of genomic and microbial biomarkers.

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Section: Discussionmentioning

confidence: 99%

Identifying important microbial and genomic biomarkers for differentiating right- versus left-sided colorectal cancer using random forest models

et al. 2023

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“…Hence, although low SBF2-AS1 expression was correlated to lymph node metastasis and to advanced clinical stage, the mechanism of SBF2-AS1 regulating EMT in HNSCC may not involve TGF-β signaling. In a screen for lncRNAs regulated by TGF-β signaling during EMT, the lnc-PNRC2-1 was the most significantly upregulated lncRNA after TGF-β stimulation for 24 h in nasopharyngeal carcinoma cells ( 44 ). Knockdown of lnc-PNRC2-1 reduced the expression of EMT markers, although the mechanisms of lnc-PNRC2-1- modulating EMT and TGF-β signaling remain to be clarified.…”

Section: Head and Neck Cancermentioning

confidence: 99%

Unboxing the network among long non-coding RNAs and TGF-β signaling in cancer

Rodrigues-Junior,

Moustakas

2024

ujms

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Deeper analysis of molecular mechanisms arising in tumor cells is an unmet need to provide new diagnostic and therapeutic strategies to prevent and treat tumors. The transforming growth factor β (TGF-β) signaling has been steadily featured in tumor biology and linked to poor prognosis of cancer patients. One pro-tumorigenic mechanism induced by TGF-β is the epithelial-to-mesenchymal transition (EMT), which can initiate cancer dissemination, enrich the tumor stem cell population, and increase chemoresistance. TGF-β signals via SMAD proteins, ubiquitin ligases, and protein kinases and modulates the expression of protein-coding and non-coding RNA genes, including those encoding larger than 500 nt transcripts, defined as long non-coding RNAs (lncRNAs). Several reports have shown lncRNAs regulating malignant phenotypes by directly affecting epigenetic processes, transcription, and post-transcriptional regulation. Thus, this review aims to update and summarize the impact of TGF-β signaling on the expression of lncRNAs and the function of such lncRNAs as regulators of TGF-β signaling, and how these networks might impact specific hallmarks of cancer.

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lncRNAs’p potential roles in the pathogenesis of cancer via interacting with signaling pathways; special focus on lncRNA-mediated signaling dysregulation in lung cancer

Shelash,

Shabeeb,

Ahmad

et al. 2024

Med Oncol

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Long non-coding (lnc)RNA profiling and the role of a key regulator lnc-PNRC2-1 in the transforming growth factor-β1-induced epithelial–mesenchymal transition of CNE1 nasopharyngeal carcinoma cells

Cited by 5 publications

References 37 publications

Identifying important microbial and genomic biomarkers for differentiating right- versus left-sided colorectal cancer using random forest models

Identifying important microbial and genomic biomarkers for differentiating right- versus left-sided colorectal cancer using random forest models

Unboxing the network among long non-coding RNAs and TGF-β signaling in cancer

lncRNAs’p potential roles in the pathogenesis of cancer via interacting with signaling pathways; special focus on lncRNA-mediated signaling dysregulation in lung cancer

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