Development and Validation of Anoiki-Related Lncrna Signature Prediction Model for KIRC Prognosis
Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net..
BACKGROUND: Various cancer types have been studied and understood using long noncoding RNA (lncRNA). Despite this, only a few studies have examined anoikis-related lncRNAs in kidney renal clear cell carcinoma (KIRC). As a result, this study evaluated a powerful prognostic model for KIRC patients based on anoikis-lncRNAs and identified potential biological targets.
METHODS: Anoikis-related lncRNAs associated with patient prognosis were identified using Pearson correlation, variance, and univariate Cox regression analyses. A predictive model that incorporated 4 anoikis-related lncRNAs has been constructed using the least absolute shrinkage and selection operator (LASSO) regression algorithm. The prognostic performance of the proposed model has also been assessed utilizing Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) curve analyses. An ESTIMATE analysis was carried out on the low- as well as high-risk subtypes to evaluate immune cell infiltration status. Furthermore, CIBERSORT, TIMER, and QUANTISEQ along with other algorithms were applied for determining the infiltration status of numerous immune cells across both groups. In addition, immune checkpoint gene expression in both groups was also determined. Finally, drug sensitivity assays and in vitro experiments were performed to validate the results.
RESULTS: A total of sixty-three lncRNAs associated with anoikis and KIRC prognosis were identified via univariate cox analysis, and four lncRNAs (Z99289.2, AC084876.1, LINC00460, and AC090337.2.) were selected as hub lncRNAs. A prognostic signature has been developed based on the expression levels and coefficiency of these four lncRNAs while establishing its efficacy in part and whole TCGA KIRC cohort. Furthermore, by using this risk signature, high- as well as low-risk KIRC patients could be distinguished more precisely it can predict patient outcomes as well. The survival predictions by the nomogram exhibited an absolute degree of concordance with actual situations. In vitro experiments verified that LINC00460 downregulation contributed to the growth inhibition of KIRC cell lines and promoted apoptosis of cancer cells.
CONCLUSION: This study suggests that anoikis-related lncRNAs could serve as valuable prognostic markers for KIRC. Additionally, they may provide insight into future KIRC treatment options by reflecting on the situation of the kidney immune microenvironment.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Combinatorial chemistry & high throughput screening - (2024) vom: 04. Jan. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
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| Links: |
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| Themen: |
Anoikis |
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| Anmerkungen: |
Date Revised 02.02.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.2174/0113862073271880231114100544 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM367947579 |
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| 245 | 1 | 0 | |a Development and Validation of Anoiki-Related Lncrna Signature Prediction Model for KIRC Prognosis |
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| 500 | |a Date Revised 02.02.2024 | ||
| 500 | |a published: Print-Electronic | ||
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| 520 | |a Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net. | ||
| 520 | |a BACKGROUND: Various cancer types have been studied and understood using long noncoding RNA (lncRNA). Despite this, only a few studies have examined anoikis-related lncRNAs in kidney renal clear cell carcinoma (KIRC). As a result, this study evaluated a powerful prognostic model for KIRC patients based on anoikis-lncRNAs and identified potential biological targets | ||
| 520 | |a METHODS: Anoikis-related lncRNAs associated with patient prognosis were identified using Pearson correlation, variance, and univariate Cox regression analyses. A predictive model that incorporated 4 anoikis-related lncRNAs has been constructed using the least absolute shrinkage and selection operator (LASSO) regression algorithm. The prognostic performance of the proposed model has also been assessed utilizing Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) curve analyses. An ESTIMATE analysis was carried out on the low- as well as high-risk subtypes to evaluate immune cell infiltration status. Furthermore, CIBERSORT, TIMER, and QUANTISEQ along with other algorithms were applied for determining the infiltration status of numerous immune cells across both groups. In addition, immune checkpoint gene expression in both groups was also determined. Finally, drug sensitivity assays and in vitro experiments were performed to validate the results | ||
| 520 | |a RESULTS: A total of sixty-three lncRNAs associated with anoikis and KIRC prognosis were identified via univariate cox analysis, and four lncRNAs (Z99289.2, AC084876.1, LINC00460, and AC090337.2.) were selected as hub lncRNAs. A prognostic signature has been developed based on the expression levels and coefficiency of these four lncRNAs while establishing its efficacy in part and whole TCGA KIRC cohort. Furthermore, by using this risk signature, high- as well as low-risk KIRC patients could be distinguished more precisely it can predict patient outcomes as well. The survival predictions by the nomogram exhibited an absolute degree of concordance with actual situations. In vitro experiments verified that LINC00460 downregulation contributed to the growth inhibition of KIRC cell lines and promoted apoptosis of cancer cells | ||
| 520 | |a CONCLUSION: This study suggests that anoikis-related lncRNAs could serve as valuable prognostic markers for KIRC. Additionally, they may provide insight into future KIRC treatment options by reflecting on the situation of the kidney immune microenvironment | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Yang, Jin |e verfasserin |4 aut | |
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