Dataset Information

Hybridisation chain reaction-based visualisation and screening for lncRNA profiles in clear-cell renal-cell carcinoma.

ABSTRACT:

Background

Analysis of long noncoding RNA (lncRNA) localisation at both the tissue and subcellular levels can provide important insights into the cell types that are important for their function.

Methods

By applying new fluorescent in situ hybridisation technique called hybridisation chain reaction (HCR), we achieved a high-throughput lncRNA visualisation and evaluation of clinical samples.

Results

Assessing 1728 pairs of 16 lncRNAs and clear-cell renal-cell carcinoma (ccRCC) specimens, three lncRNAs (TUG1, HOTAIR and CDKN2B-AS1) were associated with ccRCC prognosis. Furthermore, we derived a new lncRNA risk group of ccRCC prognosis by combining the expression levels of these three lncRNAs. Examining genomic alterations underlying this classification revealed prominent features of tumours that could serve as potential biomarkers for targeting lncRNAs. We then derived combination of HCR with expansion microscopy and visualised nanoscale-resolution HCR signals in cell nuclei, uncovering intracellular colocalization of three lncRNA (TUG1, HOTAIR and CDKN2B-AS1) signals such as those located intra- or out of the nucleus or nucleolus in cancer cells.

Conclusion

LncRNAs are expected to be desirable noncoding targets for cancer diagnosis or treatments. HCR involves plural probes consisting of small DNA oligonucleotides, clinically enabling us to detect cancerous lncRNA signals simply and rapidly at a lower cost.

SUBMITTER: Kufukihara R

PROVIDER: S-EPMC9470571 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Publications

Hybridisation chain reaction-based visualisation and screening for lncRNA profiles in clear-cell renal-cell carcinoma.

Kufukihara Ryohei R Tanaka Nobuyuki N Takamatsu Kimiharu K Niwa Naoya N Fukumoto Keishiro K Yasumizu Yota Y Takeda Toshikazu T Matsumoto Kazuhiro K Morita Shinya S Kosaka Takeo T Aimono Eriko E Nishihara Hiroshi H Mizuno Ryuichi R Oya Mototsugu M

British journal of cancer 20220628 6

<h4>Background</h4>Analysis of long noncoding RNA (lncRNA) localisation at both the tissue and subcellular levels can provide important insights into the cell types that are important for their function.<h4>Methods</h4>By applying new fluorescent in situ hybridisation technique called hybridisation chain reaction (HCR), we achieved a high-throughput lncRNA visualisation and evaluation of clinical samples.<h4>Results</h4>Assessing 1728 pairs of 16 lncRNAs and clear-cell renal-cell carcinoma (ccRC ...[more]

PMID: 35764788

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Project description:Cuproptosis is a new form of cell death, the second form of metal ion-induced cell death defined after ferroptosis. Recently, cuproptosis has been suggested to be associated with tumorigenesis. However, the relationship between cuproptosis and patient prognosis in clear cell renal cell carcinoma (ccRCC) in the context of immunotherapy remains unknown. The aim of this study was to investigate the correlation between cuproptosis-related long non-coding RNA (lncRNA) and ccRCC in terms of immunity as well as prognosis. Clinical information on lncRNAs associated with differences in cuproptosis genes in ccRCC and normal tissues was collected from The Cancer Genome Atlas (TCGA) dataset. Univariate Cox regression was used to screen lncRNAs. A total of 11 lncRNAs closely associated with cuproptosis were further screened and established using the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression, and the samples were randomly divided into training and test groups. A risk prognostic model was constructed using the training group, and the model was validated using the test group. We investigated the predictive ability of the prognostic risk model in terms of clinical prognosis, tumor mutation, immune escape, immunotherapy, tumor microenvironment, immune infiltration levels, and tumor drug treatment of ccRCC. Using the median risk score, patients were divided into low and high-risk groups. Kaplan-Meier curves showed that the overall survival (OS) of patients in the high-risk group was significantly worse than low-risk group (p < 0.001). Receiver operating characteristic (ROC) curves further validated the reliability of our model. The model consistently and accurately predicted prognosis at 1, 3, and 5 years, with an AUC above 0.7. Tumor cell genes generally precede morphological abnormalities; therefore, the model we constructed can effectively compensate for the traditional method of evaluating the prognosis of patients with renal cancer, and our model was also clinically meaningful in predicting ccRCC staging. In addition, lower model risk scores determined by mutational load indicated a good chance of survival. The high-risk group had greater recruitment of immune cells, while the anti-immune checkpoint immunotherapy was less efficacious overall than that of the low-risk group. Tumor and immune-related pathways were enriched, and anti-tumor agents were selected to improve the survival of ccRCC. This prognostic risk model is based on the levels of cuproptosis-associated lncRNAs and provides a new perspective in the clinical assessment and precise treatment of ccRCC.

Dataset Information

Hybridisation chain reaction-based visualisation and screening for lncRNA profiles in clear-cell renal-cell carcinoma.

Background

Methods

Results

Conclusion

Publications

Hybridisation chain reaction-based visualisation and screening for lncRNA profiles in clear-cell renal-cell carcinoma.

Similar Datasets

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