AC010973.2 promotes cell proliferation and is one of six stemness-related genes that predict overall survival of renal clear cell carcinoma

Abstract

Extensive research indicates that tumor stemness promotes tumor progression. Nonetheless, the underlying roles of stemness-related genes in renal clear cell carcinoma (ccRCC) are unclear. Data used in bioinformatics analysis were downloaded from The Cancer Genome Atlas (TCGA) database. Moreover, the R software, SPSS, and GraphPad Prism 8 were used for mapping and statistical analysis. First, the stemness index of each patient was quantified using a machine learning algorithm. Subsequently, the differentially expressed genes between high and low stemness index were identified as stemness-related genes. Based on these genes, a stable and effective prognostic model was identified to predict the overall survival of patients using a random forest algorithm (Training cohort; 1-year AUC: 0.67; 3-year AUC: 0.79; 5-year AUC: 0.73; Validation cohort; 1-year AUC: 0.66; 3-year AUC: 0.71; 5-year AUC: 0.7). The model genes comprised AC010973.2, RNU6-125P, AP001209.2, Z98885.1, KDM5C-IT1, and AL021368.3. Due to its highest importance evaluated by randomforst analysis, the AC010973.2 gene was selected for further research. In vitro experiments demonstrated that AC010973.2 is highly expressed in ccRCC tissue and cell lines. Meanwhile, its knockdown could significantly inhibit the proliferation of ccRCC cells based on colony formation and CCK8 assays. In summary, our findings reveal that the stemness-related gene AC01097.3 is closely associated with the survival of patients. Besides, it remarkably promotes cell proliferation in ccRCC, hence a novel potential therapeutic target.

Figure 1

Identification of stemness indices-related DEGs. Notes: Volcano plots for DEGs in high-mRNAsi and low-mRNAsi ccRCC samples from (A), high-EREG-mRNAsi and low-EREG-mRNAsi ccRCC samples from (B), and tumor and normal tissues (C). (D) Venn diagram for DEGs from 3 different data sets. DEGs: differentially expressed genes; ccRCC: clear cell renal cell carcinoma.

Figure 2

Function analysis on DEGs. Notes: (A) GO enrichment analysis of 732 DEGs, BP: biological process; CC: cell component; MF: molecular function. (B) PPI network of 732 DEGs after removing nodes without edges. (C) Top 30 nodes of PPI network. (D) Top 15 nodes of PPI network. GO: gene ontology; PPI: protein–protein interaction.

Figure 3

Establishment and assessment of the risk signature for ccRCC. Notes: (A) Calculation of relative importance of each DEG in the prognosis of ccRCC patients using Random Forest algorithm. In (B) Training cohort and (C) Validation cohort, diagrams are risk score map (left top), survival status map (left medium), gene expression heat map (left bottom), time‐dependent ROC curves (right top) and Kaplan–Meier survival curves (right bottom). ROC: receiver operating characteristics.

Figure 4

Decision Curves Analysis (DCA) of clinical model and risk signature. Notes: (A) Nomogram of clinical parameters-based prediction model for prognosis of patients. (B) Decision curves for different predicting models. Horizontal line: assume all patients as low risk (Pi < Pr); Green curve: assume all patients as high risk (Pi > Pr); Red curve: clinical prediction model; blue curve: gene signature; Yellow curve: combination use of two models. Calibration curves for nomogram in predicting survival probability at 1 year, 3 years and 5 years. The calibration measurement was conducted through bootstrapping 1000 resamples in TCGA-KIRC database (C-E).

Figure 5

Clinical correlation analysis and GSVA of AC010973.2. Notes: (A) Violin plots for distribution of AC010973.2 value in different groups of diverse clinicopathological features in XXX patients from KIRC database. (B) Diagram of GSVA on AC010973.2. The collection used was H-hallmark gene sets. Y-axis represented diverse hallmark gene sets. X-axis represented gene sets expression level in high (yellow bars) and low (blue bars) AC010973.2 groups. (C) ROC curves for AC010973.2 alone to predict 1-year, 3-year and 5-year OS of ccRCC patients. AUC for them were exhibited in the plot.

Figure 6

Upregulation of AC010973.2 mRNA expression in ccRCC. Notes: (A) AC010973.2 mRNA expression in tumors and non-malignant tissues from ccRCC patients. (B) AC010973.2 mRNA expression in ccRCC cell lines and normal renal tubular epithelial cells. (C) mRNA expression in AC010973.2 knockdown cell lines.

Figure 7

AC010973.2 regulated apoptosis signaling pathway and promoted cell proliferation. Notes: After the transfection with siRNA-AC010973.2, protein levels of Caspase-3, Bax and Bcl-2 in ACHN (A) and Caki-1 (B) cell lines through Western Blot; the blots were cut prior to hybridization with antibodies for saving antibodies. (C) Colony formation assay of ACHN and Caki-1 after the knockdown of AC010973.2. (D) CCK8 assay of ACHN and Caki-1 after the knockdown of AC010973.2.