Comprehensive analysis of lncRNAs as biomarkers for diagnosis, prognosis, and treatment response in clear cell renal cell carcinoma

Chao, Xiaoyu; Wang, Pei; Ma, Xiaoyu; Li, Zhenfen; Xia, Yong; Guo, Ying; Ge, Lite; Tian, Linzhu; Zheng, Hong; Du, Yaowu; Li, Jitian; Zuo, Zhili; Xie, Longxiang; Guo, Xiangqian

doi:10.1016/j.omto.2021.08.003

Cited by 8 publications

(3 citation statements)

References 83 publications

(199 reference statements)

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“…Long non-coding RNAs (lncRNAs) have emerged as promising biomarkers, since they modulate genes at various levels, from epigenetics to post-transcription [11]. In line with other recent publications [12,13], the comprehensive review by Rysz and colleagues describes the roles of specific lncRNAs in RCC and suggests their potential as diagnostic and prognostic markers. Understanding the intricate mechanisms by which lncRNAs influence the disease progression in RCC is essential for the development of targeted therapies that can improve patient outcome [14].…”

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confidence: 89%

Molecular Mechanisms of Tumor Progression and New Therapeutic Strategies for Urological Cancers

Ruiz de Porras,

Font

2023

IJMS

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confidence: 89%

Molecular Mechanisms of Tumor Progression and New Therapeutic Strategies for Urological Cancers

Ruiz de Porras,

Font

2023

IJMS

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“…Despite specific enhancement modes for KIRC, misdiagnosis consistently happens in clinical settings (37), imposing a socio-economic burden on healthcare systems globally. Myriads of studies have identified novel diagnostic biomarkers for kidney cancer, spanning long non-coding RNAs, circulating tumor DNA, and circulating tumor cells; despite this, there is still scope for improvement in specificity as well as sensitivity, and the clinical applicability of such emerging biomarkers remains to be further validated (38)(39)(40)(41). It is inspiring that blood platelet and blood-derived exosome-based polygenic models manifested excellent diagnostic efficacy, offering an accessible complement to existing screening modalities (42-44).…”

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confidence: 99%

Dissecting order amidst chaos of programmed cell deaths: construction of a diagnostic model for KIRC using transcriptomic information in blood-derived exosomes and single-cell multi-omics data in tumor microenvironment

Wang

Zheng

et al. 2023

Front. Immunol.

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BackgroundKidney renal clear cell carcinoma (KIRC) is the most frequently diagnosed subtype of renal cell carcinoma (RCC); however, the pathogenesis and diagnostic approaches for KIRC remain elusive. Using single-cell transcriptomic information of KIRC, we constructed a diagnostic model depicting the landscape of programmed cell death (PCD)-associated genes, namely cell death-related genes (CDRGs).MethodsIn this study, six CDRG categories, including apoptosis, necroptosis, autophagy, pyroptosis, ferroptosis, and cuproptosis, were collected. RNA sequencing (RNA-seq) data of blood-derived exosomes from the exoRBase database, RNA-seq data of tissues from The Cancer Genome Atlas (TCGA) combined with control samples from the GTEx databases, and single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database were downloaded. Next, we intersected the differentially expressed genes (DEGs) of the KIRC cohort from exoRBase and the TCGA databases with CDRGs and DEGs obtained from single-cell datasets, further screening out the candidate biomarker genes using clinical indicators and machine learning methods and thus constructing a diagnostic model for KIRC. Finally, we investigated the underlying mechanisms of key genes and their roles in the tumor microenvironment using scRNA-seq, single-cell assays for transposase-accessible chromatin sequencing (scATAC-seq), and the spatial transcriptomics sequencing (stRNA-seq) data of KIRC provided by the GEO database.ResultWe obtained 1,428 samples and 216,155 single cells. After the rational screening, we constructed a 13-gene diagnostic model for KIRC, which had high diagnostic efficacy in the exoRBase KIRC cohort (training set: AUC = 1; testing set: AUC = 0.965) and TCGA KIRC cohort (training set: AUC = 1; testing set: AUC = 0.982), with an additional validation cohort from GEO databases presenting an AUC value of 0.914. The results of a subsequent analysis revealed a specific tumor epithelial cell of TRIB3high subset. Moreover, the results of a mechanical analysis showed the relatively elevated chromatin accessibility of TRIB3 in tumor epithelial cells in the scATAC data, while stRNA-seq verified that TRIB3 was predominantly expressed in cancer tissues.ConclusionsThe 13-gene diagnostic model yielded high accuracy in KIRC screening, and TRIB3high tumor epithelial cells could be a promising therapeutic target for KIRC.

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“…Clear cell renal cell carcinoma (ccRCC) acts as the dominant histological subtype of renal cell carcinoma, accounting for around 75–85% of all subtypes 3 , 4 . The clinical outcomes of ccRCC patients after surgical excision is moderate, but the 5-year overall survival rate dramatically diminishes in metastasis or recurrence ccRCC patients 5 , 6 . These advanced ccRCC patients, estimated to be around 30% of all ccRCC patients, show their inefficiency upon traditional chemotherapies and radiotherapies, but they could be somewhat relieved by adjuvant therapy, such as immunotherapies or targeted therapies 3 , 7 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based multi-model prediction for disease-free survival status of patients with clear cell renal cell carcinoma after surgery: a multicenter cohort study

Chen,

Gao,

Guo

et al. 2024

International Journal of Surgery

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Background: Although separate analysis of individual factor can somewhat improve the prognostic performance, integration of multimodal information into a single signature is necessary to stratify patients with clear cell renal cell carcinoma (ccRCC) for adjuvant therapy after surgery. Methods: A total of 414 patients with whole slide images, computed tomography images, and clinical data from three patient cohorts were retrospectively analyzed. The authors performed deep learning and machine learning algorithm to construct three single-modality prediction models for disease-free survival of ccRCC based on whole slide images, cell segmentation, and computed tomography images, respectively. A multimodel prediction signature (MMPS) for disease-free survival were further developed by combining three single-modality prediction models and tumor stage/grade system. Prognostic performance of the prognostic model was also verified in two independent validation cohorts. Results: Single-modality prediction models performed well in predicting the disease-free survival status of ccRCC. The MMPS achieved higher area under the curve value of 0.742, 0.917, and 0.900 in three independent patient cohorts, respectively. MMPS could distinguish patients with worse disease-free survival, with HR of 12.90 (95% CI: 2.443–68.120, P<0.0001), 11.10 (95% CI: 5.467–22.520, P<0.0001), and 8.27 (95% CI: 1.482–46.130, P<0.0001) in three different patient cohorts. In addition, MMPS outperformed single-modality prediction models and current clinical prognostic factors, which could also provide complements to current risk stratification for adjuvant therapy of ccRCC. Conclusion: Our novel multimodel prediction analysis for disease-free survival exhibited significant improvements in prognostic prediction for patients with ccRCC. After further validation in multiple centers and regions, the multimodal system could be a potential practical tool for clinicians in the treatment for ccRCC patients.

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Comprehensive analysis of lncRNAs as biomarkers for diagnosis, prognosis, and treatment response in clear cell renal cell carcinoma

Cited by 8 publications

References 83 publications

Molecular Mechanisms of Tumor Progression and New Therapeutic Strategies for Urological Cancers

Molecular Mechanisms of Tumor Progression and New Therapeutic Strategies for Urological Cancers

Dissecting order amidst chaos of programmed cell deaths: construction of a diagnostic model for KIRC using transcriptomic information in blood-derived exosomes and single-cell multi-omics data in tumor microenvironment

Deep learning-based multi-model prediction for disease-free survival status of patients with clear cell renal cell carcinoma after surgery: a multicenter cohort study

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