LncRNA RP3-326I13.1 promotes cisplatin resistance in lung adenocarcinoma by binding to HSP90B and upregulating MMP13

Zhou, Huixin; Huang, Xiaolu; Shi, Wenjing; Xu, Shihao; Chen, Jie; Huang, Kate; Wang, Yumin

doi:10.1080/15384101.2022.2051971

Cited by 11 publications

(5 citation statements)

References 38 publications

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“…These all suggest that lncRNAs may be used as a new biomarker to improve the prognosis and treatment of LUAD patients. Zhou et al ( Zhou et al, 2022 ) found that lncRNAs can promote the occurrence and development of LUAD by binding to HSP90. However, the current research on the pathogenesis of MCRLncs in LUAD is still lacking.…”

Section: Introductionmentioning

confidence: 99%

Prognostic model and immunotherapy prediction based on molecular chaperone-related lncRNAs in lung adenocarcinoma

Tao

et al. 2022

Front. Genet.

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Introduction: Molecular chaperones and long non-coding RNAs (lncRNAs) have been confirmed to be closely related to the occurrence and development of tumors, especially lung cancer. Our study aimed to construct a kind of molecular chaperone-related long non-coding RNAs (MCRLncs) marker to accurately predict the prognosis of lung adenocarcinoma (LUAD) patients and find new immunotherapy targets.Methods: In this study, we acquired molecular chaperone genes from two databases, Genecards and molecular signatures database (MsigDB). And then, we downloaded transcriptome data, clinical data, and mutation information of LUAD patients through the Cancer Genome Atlas (TCGA). MCRLncs were determined by Spearman correlation analysis. We used univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to construct risk models. Kaplan-meier (KM) analysis was used to understand the difference in survival between high and low-risk groups. Nomogram, calibration curve, concordance index (C-index) curve, and receiver operating characteristic (ROC) curve were used to evaluate the accuracy of the risk model prediction. In addition, we used gene ontology (GO) enrichment analysis and kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses to explore the potential biological functions of MCRLncs. Immune microenvironmental landscapes were constructed by using single-sample gene set enrichment analysis (ssGSEA), tumor immune dysfunction and exclusion (TIDE) algorithm, “pRRophetic” R package, and “IMvigor210” dataset. The stem cell index based on mRNAsi expression was used to further evaluate the patient’s prognosis.Results: Sixteen MCRLncs were identified as independent prognostic indicators in patients with LUAD. Patients in the high-risk group had significantly worse overall survival (OS). ROC curve suggested that the prognostic features of MCRLncs had a good predictive ability for OS. Immune system activation was more pronounced in the high-risk group. Prognostic features of the high-risk group were strongly associated with exclusion and cancer-associated fibroblasts (CAF). According to this prognostic model, a total of 15 potential chemotherapeutic agents were screened for the treatment of LUAD. Immunotherapy analysis showed that the selected chemotherapeutic drugs had potential application value. Stem cell index mRNAsi correlates with prognosis in patients with LUAD.Conclusion: Our study established a kind of novel MCRLncs marker that can effectively predict OS in LUAD patients and provided a new model for the application of immunotherapy in clinical practice.

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Section: Introductionmentioning

confidence: 99%

Prognostic model and immunotherapy prediction based on molecular chaperone-related lncRNAs in lung adenocarcinoma

Tao

et al. 2022

Front. Genet.

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“…For the characterization of proteins that specifically interact with lncRNA, mass spectrometry was employed after the pull-down of a biotinylated lncRNA and protein binding complex with streptavidin magnetic beads. For instance, using the Magnetic RNA-Protein Pull-Down Kit, the lncRNAs TPA [ 41 ], XIST [ 52 ], and RP3-326I-13.1 (PINCR) [ 53 ] were in vitro transcribed and desthiobiotin-labeled for pull-down with streptavidin magnetic beads and subjected to the identification of several interacting proteins such as HSP90B by mass spectrometry [ 41 , 53 ]. Similarly, ADAR1 (adenosine deaminase RNA specific 1) was identified as a binding protein to the immunosuppressive lncRNA LINC00624 that was in vitro transcribed in the presence of biotin-16-UTP and pulled down with Dynabeads C1 and mass spectrometry [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

Optimization of Biotinylated RNA or DNA Pull-Down Assays for Detection of Binding Proteins: Examples of IRP1, IRP2, HuR, AUF1, and Nrf2

Tsuji

2023

IJMS

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Investigation of RNA- and DNA-binding proteins to a defined regulatory sequence, such as an AU-rich RNA and a DNA enhancer element, is important for understanding gene regulation through their interactions. For in vitro binding studies, an electrophoretic mobility shift assay (EMSA) was widely used in the past. In line with the trend toward using non-radioactive materials in various bioassays, end-labeled biotinylated RNA and DNA oligonucleotides can be more practical probes to study protein–RNA and protein–DNA interactions; thereby, the binding complexes can be pulled down with streptavidin-conjugated resins and identified by Western blotting. However, setting up RNA and DNA pull-down assays with biotinylated probes in optimum protein binding conditions remains challenging. Here, we demonstrate the step-by step optimization of pull-down for IRP (iron-responsive-element-binding protein) with a 5′-biotinylated stem-loop IRE (iron-responsive element) RNA, HuR, and AUF1 with an AU-rich RNA element and Nrf2 binding to an antioxidant-responsive element (ARE) enhancer in the human ferritin H gene. This study was designed to address key technical questions in RNA and DNA pull-down assays: (1) how much RNA and DNA probes we should use; (2) what binding buffer and cell lysis buffer we can use; (3) how to verify the specific interaction; (4) what streptavidin resin (agarose or magnetic beads) works; and (5) what Western blotting results we can expect from varying to optimum conditions. We anticipate that our optimized pull-down conditions can be applicable to other RNA- and DNA-binding proteins along with emerging non-coding small RNA-binding proteins for their in vitro characterization.

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“…Recent studies show that DLX6-AS1 is increased in cisplatin resistant lung squamous cell carcinoma cells, and it is activated by H3K4me1 to enhance DDP resistance via modulating miR-181a-5p/miR-382-5p/CELF1 axis [ 70 ]. Furthermore, lncRNA RP3-326I13.1 (also known as PINCR) was overexpressed in A549/DDP cells and LAD tissues, and it induces DDP resistance through binding to HSP90B and upregulating human matrix metalloproteinase-13 (MMP-13), while knockdown of RP3-326I13.1 could induce G1 phase arrest [ 71 ]. A study performed by Chen et al showed that the expression of RP11-838N2.3 in A549/DDP was evidently increased from high-throughput gene chip.…”

Section: Lncrnas and Resistance To Platinum Drugsmentioning

confidence: 99%

Long non-coding RNA and Evolving drug resistance in lung cancer

Wang,

Fu,

Zhong

et al. 2023

Heliyon

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LncRNA RP3-326I13.1 promotes cisplatin resistance in lung adenocarcinoma by binding to HSP90B and upregulating MMP13

Cited by 11 publications

References 38 publications

Prognostic model and immunotherapy prediction based on molecular chaperone-related lncRNAs in lung adenocarcinoma

Prognostic model and immunotherapy prediction based on molecular chaperone-related lncRNAs in lung adenocarcinoma

Optimization of Biotinylated RNA or DNA Pull-Down Assays for Detection of Binding Proteins: Examples of IRP1, IRP2, HuR, AUF1, and Nrf2

Long non-coding RNA and Evolving drug resistance in lung cancer

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