Background: Kidney carcinoma is a major cause of carcinoma-related death, with the prognosis for advanced or metastatic renal cell carcinoma still very poor. The aim of this study was to investigate feasible prognostic biomarkers that can be used to construct a prognostic index model for clear cell renal cell carcinoma (ccRCC) patients. Methods: The mRNA expression profiles of ccRCC samples were downloaded from the The Cancer Genome Atlas (TCGA) dataset and the correlation of AIF1L with malignancy, tumor stage and prognosis were evaluated. Differentially expressed genes (DEGs) between AIF1L-low and AIF1L-high expression groups were selected. Those with prognostic value as determined by univariate and multivariate Cox regression analysis were then used to construct a prognostic index model capable of predicting the outcome of ccRCC patients. Results: The expression level of AIF1L was lower in ccRCC samples than in normal kidney samples. AIF1L expression showed an inverse correlation with tumor stage and a positive association with better prognosis. ccRCC samples were divided into high- and low-expression groups according to the median value of AIF1L expression. In the AIF1L-high expression group, 165 up-regulated DEGs and 601 down-regulated DEGs were identified. Three genes (AIF1L, SERPINC1 and CES1) were selected following univariate and multivariate Cox regression analysis. The hazard ratio (HR) and 95% confidence intervals (CI) for these genes were: AIF1L (HR = 0.83, 95% CI: 0.76–0.91), SERPINC1 (HR = 1.33, 95% CI: 1.12–1.58), and CES1 (HR = 0.87, 95% CI: 0.78–0.97). A prognostic index model based on the expression level of the three genes showed good performance in predicting ccRCC patient outcome, with an area under the ROC curve (AUC) of 0.671. Conclusion: This research provides a better understanding of the correlation between AIF1L expression and ccRCC. We propose a novel prognostic index model comprising AIF1L, SERPINC1 and CES1 expression that may assist physicians in determining the prognosis of ccRCC patients.
Kidney carcinoma is one of the three malignant tumors of the urinary system. It had a global incidence of approximately 431,000 new cases and was responsible for 179,000 related deaths in 2020 . The incidence of kidney carcinoma is much higher in developed regions such as North America and Europe than in Asia and Africa [1, 2]. Renal cell carcinoma (RCC) is the most normal histological subtype of kidney carcinoma and represents approximately 90% of all cases. ccRCC is the most common RCC subtype and accounts for 75% of cases . Although the large majority of early, localized RCC can be cured by surgical treatment, the 5-year overall survival rate for advanced and metastatic RCC (mRCC) is only 5–10% . Molecular-targeted therapeutic drugs such as Vascular endothelial growth factor (VEGF)/Vascular Endothelial Growth Factor Receptor (VEGFR) inhibitors and immunotherapy agents such as PD-1 antibodies have markedly improved the clinical prognosis of mRCC patients [5, 6, 7, 8]. However, their long-term benefit for patient survival remains unsatisfactory [9, 10]. The complexity of tumor heterogeneity and the clonal evolution of tumors ultimately leads to clinical drug resistance . Advanced or metastatic RCC therefore remains as one of the most treatment-resistant cancer types. In order to improve patient outcomes, there is an urgent need for well-defined diagnostic biomarkers that can be used for early detection, risk stratification, and to overcome drug resistance.
EF-hand (EFh) domain-containing proteins have been implicated in malignant progression . Allograft inflammatory factor 1 (AIF1, also referred to as IBA1) contains Efh and plays a critical role in the initiation and progression of cancers [13, 14, 15, 16, 17, 18]. AIF1L (allograft inflammatory factor 1-like, also referred to as IBA2) is a homolog of AIF1 [19, 20] and has a similar overall structure and molecular function . Nevertheless, the two proteins may have diverse functions, as suggested by the different expression patterns seen in different tissues . AIF1 is preferentially expressed in the spleen, tonsil, lymph node, thymus, and lung [22, 23], whereas AIF1L is notably expressed in the kidney. A potential role for AIF1L in tumorigenesis of the kidney and the associated molecular mechanisms have yet to be described.
In the present study, AIF1L was found to be significantly downregulated in ccRCC. A total of 539 ccRCC tumors were clustered according to the median value of AIF1L expression value and separated into AIF1L-high and AIF1L-low expression group. Univariate and multivariate Cox regression analysis were then used to identify differentially expressed genes (DEGs) with prognostic value. A prognostic index model based on the expression levels of AIF1L, SERPINC1, and CES1 was then constructed to predict clinical outcome and to guide treatment.
Level three sequencing data and clinical follow-up data for 539 clear cell renal cell carcinoma (ccRCC) samples and 72 corresponding healthy kidney samples was extracted from the TCGA dataset. The Fragments Per Kilobase of exon model per Million mapped fragments (FPKM) expression profile was then converted to Transcripts Per Kilobase Million (TPM) based on the sum of expression of all genes in a sample being 100,000. The microarray gene expression profile and related clinical data for GSE40435 , containing 101 pairs of ccRCC and adjacent non-tumor renal tissue, was downloaded and used to validate the results of this study.
The students t-test was used to evaluate statistical differences in mRNA expression between ccRCC and normal tissues. Similarly, paired t-tests were applied between paired ccRCC and adjacent normal tissues. Differences in AIF1L expression between subgroups of various clinicopathological parameters were analyzed by the Kruskal-Wallis test. Survival curves for AIF1L-low and -high expression groups were plotted by Kaplan-Meier analysis and compared using log-rank tests.
To identify genes associated with AIF1L expression, DEGs between the
AIF1L-high and AIF1L-low groups were selected by the “edgeR”
package in R language . The median value for AIF1L expression was
used to generate the AIF1L-high and AIF1L-low groups. The
fold-change and p values were calculated for each gene. Genes with a
DEGs were further selected according to their prognostic value as determined by univariate and multivariate prognostic index Cox proportional hazard regression models . The expression profiles of selected DEGs were then used to construct a prognostic index model. ccRCC patient samples were classified into high and low groups according to the prognostic index’s median cut-off. Overall survival was studied using Kaplan-Meir analysis. The AUC was calculated to assess discrimination of the prognostic index model in the TCGA samples.
All statistical analyses were conducted using R language. Kaplan-Meier survival
and univariate and multivariate analyses were performed using the R package
“survival”. ROC curves were plotted using the R package “survival ROC”. In
all statistical analyses, significance was accepted at a p-value
To investigate the relationship between AIF1L and the malignant
phenotype in ccRCC, transcriptome data for AIF1L in the TCGA dataset was
analyzed for 539 ccRCC tumors and 72 normal kidney samples. AIF1L mRNA
expression was markedly lower in ccRCC tissues compared to normal kidney samples
Correlation of AIF1L expression with malignancy, tumor stage, and prognosis in ccRCC. (A) Student t-test result for the comparison of AIF1L expression between ccRCC and normal kidney samples from the TCGA dataset. (B) Paired t-test result for AIF1L expression between paired ccRCC and normal kidney samples from the TCGA dataset. (C) Kruskal-Wallis test result for AIF1L expression between Stage I, Stage II, Stage III and StageIV tumor samples. (D) Overall survival analysis for ccRCC samples from the TCGA dataset with high or low AIF1L expression. (E) Recurrence-free survival analysis for ccRCC samples from the TCGA dataset with high or low AIF1L expression.
The data for 539 ccRCC samples and 72 corresponding healthy kidney samples was extracted from the TCGA dataset. The median value for AIF1L expression was used to obtain high- and low-expression AIF1L groups. In total, 766 DEGs were identified using “edgeR”, comprising 165 increased and 601 decreased DEGs in the AIF1L-high group (Fig. 2D). A heatmap was then plotted to reveal the top 50 increased expression and top 50 decreased expression genes (Fig. 2E).
Validation in an independent dataset and DEG analysis. (A) Students t-test result for AIF1L expression between ccRCC and normal kidney samples. (B) Paired t-test result for AIF1L expression between paired ccRCC and normal kidney samples. (C) Students t-test result for AIF1L expression in Stage I–II and Stage III–IV samples. (D) Volcano plot visualizing the DEGs. The data for 539 ccRCC samples and 72 corresponding healthy kidney samples was extracted from the TCGA datase. The vertical lines demarcate the log2 fold-change values, while the horizontal line marks a –log10 p-value of 0.05. Red represents the upregulated genes, while blue represents the downregulated genes. (E) Heatmap for the DEGs. The data for 539 ccRCC samples and 72 corresponding healthy kidney samples was extracted from the TCGA datase. The samples were divided into two groups based on the median value for AIF1L expression. Abbreviations: DEG, differently expressed genes.
GO and KEGG enrichment analyses were conducted to identify involved pathways for the DEGs. Cellular Component (CC) enrichment analysis revealed the DEGs were mainly enriched in signaling pathways such as “collagen-containing-extracellular-matrix”, “blood-microparticle”, “endoplasmic-reticulum-lumen”, and “high-density-lipoprotein-particle” (Table 1). In Biological Process (BP), the DEGs were mainly involved in response pathways such as the “humoral-immune-response”, “antimicrobial-humoral-response”, “negative-regulation-of-peptidase”, “hormone-metabolic-process”, and “negative-regulation-of-endopeptidase-activity”. In Molecular Function (MF), the DEGs were mainly involved in inhibitor and binding activities such as “peptidase-inhibitor-activity”, “endopeptidase-inhibitor-activity”, “serine-type endopeptidase-inhibitor activity”, and “endopeptidase-regulator-activity”. KEGG pathway analysis of DEGs further revealed immune-related pathways and metabolism-related pathways such as “complement-and-coagulation-cascades”, “retinol-metabolism” and “metabolism-of-xenobiotics-by cytochrome-P450” signaling pathways (Table 2).
|GO:0062023||collagen-containing extracellular matrix||57||CC|
|GO:0005788||endoplasmic reticulum lumen||40||CC|
|GO:0034364||high-density lipoprotein particle||10||CC|
|GO:0006959||humoral immune response||50||BP|
|GO:0019730||antimicrobial humoral response||26||BP|
|GO:0010466||negative regulation of peptidase activity||34||BP|
|GO:0042445||hormone metabolic process||31||BP|
|GO:0010951||negative regulation of endopeptidase activity||33||BP|
|GO:0030414||peptidase inhibitor activity||31||MF|
|GO:0004866||endopeptidase inhibitor activity||30||MF|
|GO:0004867||serine-type endopeptidase inhibitor activity||22||MF|
|GO:0061135||endopeptidase regulator activity||30||MF|
|hsa04610||Complement and coagulation cascades||23|
|hsa00140||Steroid hormone biosynthesis||14|
|hsa00980||Metabolism of xenobiotics by cytochrome P450||14|
|hsa00982||Drug metabolism-cytochrome P450||13|
|hsa05204||Chemical carcinogenesis-DNA adducts||11|
|hsa04974||Protein digestion and absorption||13|
Univariate and multivariate Cox regression analysis was used to evaluate the
prognostic significance of DEGs. Three genes (AIF1L,
and CES1) were selected by this analysis. The hazard ratio (HR) and 95%
confidence interval (CI) for these were: AIF1L (HR = 0.83, 95% CI:
0.76–0.91), SERPINC1 (HR = 1.33, 95% CI: 1.12–1.58), and
CES1 (HR = 0.87, 95% CI: 0.78–0.97). Kaplan-Meier survival analysis
revealed that high SERPINC1 expression and low CES1 expression
were associated with worse prognosis (Fig. 3A–B). Given their significant
association with prognosis, AIF1L,
SERPINC1 and CES were
regarded as prognosis-related mRNA signatures in order to develop a prognostic
index model. The prognostic index of each patient sample was calculated as
follows: prognostic index = (–0.17)
Construction of a prognostic index model based on the expression level of three genes (AIF1L, SERPINC1, and CES1). (A–B) Kaplan-Meier survival plots for SERPINC1 and CES1. High expression of SERPINC1 and low expression of CES1 indicated a poorer prognosis. (C) Detailed information on the low and high prognostic index groups in the TCGA dataset (upper); survival status and survival time for the TCGA ccRCC cohort (lower). (D) Heatmap for AIF1L, SERPINC1, and CES1 expression in the TCGA dataset. (E) ROC curve estimating the performance of the prognostic index model for predicting first-year survival in the TCGA dataset. (F) Kaplan-Meier survival plots for high- and low-risk groups in the TCGA dataset.
Metastasis is found in 25–30% of ccRCC patients at the initial diagnosis [29, 30]. Tumor metastasis results in death in
EF-hand (EFh) domain-containing proteins are associated with numerous disease
states, including chronic inflammation and tumor progression . The
AIF1L protein structure encompasses two central EFh motifs that lack
This study also identified two DEGs, SERPINC1 and CES1, that are related to AIF1L. Results from the TCGA/GEO dataset and validation of gene expression and survival differences confirmed the prognostic significance of SERPINC1 and CES1 in ccRCC. SERPINC1 (serpin peptidase inhibitor clade C member 1), also referred to as antithrombin III (ATIII) , regulates coagulation by inhibiting various factors and also has anti-inflammatory effects on epithelial cells . Previous studies have reported that SERPINC1 expression is upregulated in nasopharyngeal carcinoma tissue , bladder cancer tissue , endometrial and exosome cancer tissue  compared to adjacent normal tissues. SERPINC1 expression was also strongly associated with the development occurrence and progression of certain tumor types [39, 40, 41] and has been identified as an immune-related gene. SERPINC1 expression is prognostic for the survival of lung adenocarcinoma41, uveal melanoma  and hepatocellular carcinoma  patients. CES1, also referred to as serine esterase 1, is protective against xenobiotics and is primarily expressed in the epithelia of metabolic organs including the liver, lungs and bladder [42, 43]. The restrain of CES1 in mononuclear cells display a diminished ability to lyse cancer cells . Deficient CES1 enzyme activity was also frequently observed in non-Hodgkin lymphoma and B-cell chronic lymphocytic leukemia [42, 43], suggesting a possible cancer-cell-killing or cancer monitoring function for CES1.
To our knowledge, this is the first report of potential prognostic value for AIF1L, SERPINC1, and CES1 expression in ccRCC patients. The prognostic index model based on the three genes revealed a better performance than each gene alone. The performance of this model for the prediction of first-year survival in the TCGA dataset reached 0.671 using AUC analysis, indicating that it can predict the prognosis of ccRCC patients. One limitation of this study is that the model’s risk score was not compared with other clinical parameters (age, histological grade, and pathological stage) for the prediction of overall survival. Furthermore, this research was conducted using retrospective data available from public databases. Further verification will require prospective clinical trials.
AIF1L expression is markedly decreased in ccRCC tissues compared to normal kidney tissues. The expression level of AIF1L decreases with increasing tumor stage. A novel prognostic index model based on AIF1L, SERPINC1 and CES1 expression can predict the prognosis of ccRCC patients. This study provides additional insight into the potential role of AIF1L in the development and progression of ccRCC. Our proposed prognostic index model may help physicians in assessing the prognosis of ccRCC patients.
RCC, renal cell carcinoma; ccRCC, cell renal cell carcinoma; PRCC, papillary RCC; ChRCC, chromophobe RCC; VEGF, vascular endothelial growth factor; VEGFRs, vascular endothelial growth factor receptors; PD-1, programmed death 1; EFh, EF-hand; AIF1, allograft inflammatory factor 1; AIF1L, allograft inflammatory factor 1 like; FPKM, Fragments Per Kilobase Million; TPM, Transcripts Per Kilobase Million; GEO, Gene Expression Omnibus; DEGs, differentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; K-M, Kaplan-Meier; AUC, area under the ROC curve; HR, hazard ratio; CI, confidence intervals; ECM, extracellular matrix; SERPINC1, serpin peptidase inhibitor clade C member 1; ATIII, antithrombin III.
Dataset downloading and analyses—SZ, ZC, JC, MS, YC. Conception and Design—AY, RL, SZ, JL, FL. Manuscript writing—YW, YX, JZ, WC, HW. Manuscript revision—HW, JL, FF, ZW, CW, BX. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript.
This work was supported by grants from the Natural Science Foundation of Zhejiang Province (LQ20H160007), the Natural Science Foundation of Fujian Province (2019J01153), the fourth batch of key discipline construction Project fund of Fujian Medical University Union Hospital, and Startup Fund for scientific research, Fujian Medical University (2019QH1053).
The authors declare no conflict of interest.
The datasets supporting the conclusions of this article are included in this article.