Development and Validation of Anoiki-Related Lncrna Signature Prediction Model for KIRC Prognosis
Authors: Su Yao , Yang Jin 
Published: February 2024

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.

method:
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.

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.

result:
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.