LncRNA KCNQ1OT1 enhances the radioresistance of lung squamous cell carcinoma by targeting the miR-491-5p/TPX2-RNF2 axis

Liu, Jiahui; Mi, Jianguo; Guan, Jinlei; Wang, Yuan; Wang, Yu; Hu, Yuanyang; Zhang, Xin; Yang, Jie

doi:10.21037/jtd-22-1261

Cited by 8 publications

(2 citation statements)

References 73 publications

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“…In the past decade, extensive studies have identified many types of lncRNAs that can serve as promising biomarkers for cancer diagnosis and targeted therapy. For instance, LINC01608 has been identified as a promising prognostic biomarker for hepatocellular carcinoma ( Liu et al, 2022 ), NALT1 promotes the targeting of PEG10 via sponge microRNA-574-5p to advance colorectal cancer progression ( Ye et al, 2022 ), and RNA demethylase ALKBH5 promotes lung cancer progression ( Shen et al, 2022 ). However, traditional biological experiments used to identify the association between lncRNA and diseases, such as PCR ( Heid et al, 1996 ) and microarray analysis ( Zhai et al, 2015 ), have always been limited by high costs and lack of specificity in exploring and understanding lncRNA.…”

Section: Introductionmentioning

confidence: 99%

Applying negative sample denoising and multi-view feature for lncRNA-disease association prediction

Yao,

Zhang,

et al. 2024

Front. Genet.

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Increasing evidence indicates that mutations and dysregulation of long non-coding RNA (lncRNA) play a crucial role in the pathogenesis and prognosis of complex human diseases. Computational methods for predicting the association between lncRNAs and diseases have gained increasing attention. However, these methods face two key challenges: obtaining reliable negative samples and incorporating lncRNA-disease association (LDA) information from multiple perspectives. This paper proposes a method called NDMLDA, which combines multi-view feature extraction, unsupervised negative sample denoising, and stacking ensemble classifier. Firstly, an unsupervised method (K-means) is used to design a negative sample denoising module to alleviate the imbalance of samples and the impact of potential noise in the negative samples on model performance. Secondly, graph attention networks are employed to extract multi-view features of both lncRNAs and diseases, thereby enhancing the learning of association information between them. Finally, lncRNA-disease association prediction is implemented through a stacking ensemble classifier. Existing research datasets are integrated to evaluate performance, and 5-fold cross-validation is conducted on this dataset. Experimental results demonstrate that NDMLDA achieves an AUC of 0.9907and an AUPR of 0.9927, with a 5-fold cross-validation variance of less than 0.1%. These results outperform the baseline methods. Additionally, case studies further illustrate the model’s potential in cancer diagnosis and precision medicine implementation.

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Section: Introductionmentioning

confidence: 99%

Applying negative sample denoising and multi-view feature for lncRNA-disease association prediction

Yao,

Zhang,

et al. 2024

Front. Genet.

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“…The change of immune status in pro- and anti-tumor immune cells can also affect RT sensitivity ( 14 ). LncRNA KCNQ1OT1 has been clarified as a therapeutic target to improve RT sensitivity ( 15 ). It is becoming increasingly clear that reversing tumor resistance to RT can dramatically improve the efficacy of tumor-targeting RT.…”

Section: Introductionmentioning

confidence: 99%

Targeting complement C5a to improve radiotherapy sensitivity in non-small cell lung cancer

Yuan¹,

Wang²,

Wu³

et al. 2023

Transl Lung Cancer Res

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Background Tumor local and distant relapse recurrence after radiotherapy (RT) is one of the critical factors leading to poor prognosis. The effective antitumor effects of RT are dependent upon the participation of innate and adaptive components of the immune system. C5a/C5aR1 signaling can regulate antitumor immune effect in the tumor microenvironment (TME). Thus, exploring the changes and mechanism in the TME induced by RT-mediated complement activation may provide a novel perspective for reversing radioresistance. Methods First, fractionated radiation of 8 Gy ×3 fractions were targeted at Lewis lung carcinoma (LLC) tumor-bearing female mice to measure the infiltration of CD8 + T cell and analyze the RNA sequencing (RNA-seq) in RT-recruited CD8 + T cells. Second, tumor growth was measured in LLC tumor-bearing mice treated with RT either with or without C5aR1 inhibitor to clarify the antitumor effect of RT combined with C5aR1 inhibitor. Third, we detected the expression of C5a/C5aR1 and their signaling pathways on radiated tumor tissues. Furthermore, we investigated the expression of C5a in tumor cells at different time points after different doses of RT. Results In our system, RT induced the increased infiltration of CD8 + T cells and local activation of complement C5a/C5aR. Concurrent administration of RT and blocking of C5aR improved radiosensitivity and tumor-specific immune response, which was reflected by high C5aR expression in CD8 + T cells. The AKT/NF-κB pathway was found to be an important signaling pathway in C5a/C5aR axis mediation by RT. Conclusions RT promotes the release of C5a from tumor cells and leads to up-regulation of C5aR1 expression via the AKT/NF-κB pathway. Inhibition of the combination of complement C5a and C5aR could improve RT sensitivity. Our work provides evidence that the combination of RT and C5aR blockade opens a new window of opportunity to promote anti-tumor therapeutic effects in lung cancer.

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scDiffCoAM: A complete framework to identify potential biomarkers for esophageal squamous cell carcinoma using scRNA-Seq data analysis

Saikia,

Bhattacharyya,

Kalita

2024

J Biosci

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LncRNA KCNQ1OT1 enhances the radioresistance of lung squamous cell carcinoma by targeting the miR-491-5p/TPX2-RNF2 axis

Cited by 8 publications

References 73 publications

Applying negative sample denoising and multi-view feature for lncRNA-disease association prediction

Applying negative sample denoising and multi-view feature for lncRNA-disease association prediction

Targeting complement C5a to improve radiotherapy sensitivity in non-small cell lung cancer

scDiffCoAM: A complete framework to identify potential biomarkers for esophageal squamous cell carcinoma using scRNA-Seq data analysis

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