Abstract

   Kidney clear cell renal cell carcinoma (KIRC) is also the most lethal
   subtype among all kidney cancer subtypes, posing a severe threat to
   public health. Therefore, it is crucial to identify new, reliable
   biomarkers in KIRC. Therefore, it is crucial to identify novel,
   reliable biomarkers associated with KIRC. We analyzed RNA sequence
   results from TCGA and several GEO datasets. The commonly deregulated
   gene, ALDOB, was found in multiple data and confirmed its important
   prognostic value. Subsequently, we explored the specific mechanism by
   which ALDOB regulates anti-tumor immunity through in vivo and in vitro
   experiments. We found that ALDOB may play a role in regulating tumor
   growth by regulating CD8^+ T cell infiltration. This is consistent with
   the results of our immune infiltration-related analysis. In addition,
   we have also discovered the effect of ALDOB in previous studies on
   other cancer types. Finally, we concluded that ALDOB may have potential
   reference value for immunotherapy and can also be used as an
   independent predictor of prognosis in KIRC.

1. Introduction

   Renal cell carcinoma (RCC) is a commonly occurring tumor within the
   urinary system. Among its various subtypes, kidney clear cell renal
   cell carcinoma (KIRC) is the most prevalent, constituting approximately
   80 %–90 % of all cases [[33][1], [34][2], [35][3]]. Unfortunately, KIRC
   is also the most lethal subtype among all kidney cancer subtypes
   [[36]4]. Traditional treatments such as radiotherapy and chemotherapy
   have shown limited efficacy, making surgical resection the primary
   treatment option [[37]5,[38]6]. However, a significant number of
   patients experience recurrence after surgery, with around 30 % of
   patients developing metastasis. Early diagnosis of KIRC is crucial for
   patient treatment and prognosis [[39]7]. Therefore, the identification
   of new and reliable biomarkers associated with KIRC is of utmost
   importance.

   One area of interest in cancer research is the gene expression of
   metabolic enzymes in tumors. These enzymes play crucial roles in
   various cellular processes such as cell cycle regulation, DNA damage
   repair, cell proliferation, apoptosis, and tumor microenvironment
   regulation. Thus, they can influence the progression of cancer and
   serve as potential prognostic markers. For instance, FASN, a key enzyme
   in lipid metabolism, has been found to be overexpressed in colon cancer
   tissue compared to adjacent tissue [[40]8]. Moreover, the concentration
   of FASN in serum has been positively correlated with the clinical stage
   of colon cancer [[41]9]. Additionally, mutations in metabolic enzymes
   can directly contribute to tumorigenesis. The IDH1/2 metabolic genes,
   for example, are frequently mutated in various human cancers
   [[42]10,[43]11]. These mutations disrupt cell metabolism and epigenetic
   regulation, thereby promoting tumorigenesis. IDH1, in particular, is an
   important molecular marker in patients with glioma and acute myeloid
   leukemia [[44]10,[45]12].

   With advancements in molecular biology and genetic molecular biology,
   numerous studies have confirmed the association between the occurrence
   and progression of KIRC and differential gene expression [[46][13],
   [47][14], [48][15]]. In this study, we utilized RNA data obtained from
   GEO and TCGA databases to identify differentially expressed genes
   associated with disease progression. Various bioinformatics algorithms
   and software were employed to explore functional pathways related to
   these genes. To further validate our findings, a key candidate
   biomarker was identified and analyzed for its clinical significance.
   Finally, we investigated the molecular mechanisms by which this
   biomarker exerts its effects in KIRC.

   By understanding the molecular mechanisms and identifying potential
   biomarkers associated with KIRC, we aim to contribute to the
   development of effective diagnostic tools, treatment strategies, and
   improved patient outcomes.

2. Methods

2.1. Data preprocessing

   We collected gene expression data from a total of 608 KIRC patients and
   their corresponding clinical information from the TCGA database.
   Additionally, we retrieved three independent gene expression profiles
   along with clinical data from the GEO database. These included
   [49]GSE100666, which consisted of three KIRC samples and three normal
   renal samples, [50]GSE53757, which included 72 KIRC samples and 72
   normal renal samples, and [51]GSE12606, which contained three KIRC
   samples and three normal renal samples. To ensure the reliability of
   our analysis, we performed various preprocessing steps on the obtained
   gene expression profiles. This involved quality control measures, data
   filtering to eliminate any irrelevant or unreliable data, background
   correction, and conversion of the ensemble ID numbers to the
   corresponding gene names.

2.2. Differentially expressed gene identification

   The expression levels of KIRC samples and normal renal samples were
   compared with the “limma” R package [[52]16]. We identified the
   different expression genes (DEGs) with the standard: adj. P < 0.01 and
   |Log2FC| >2. The volcano plot depicting Differentially Expressed Genes
   (DEGs) and the Venn diagram illustrating their overlap were generated
   using the “ggplot2” and “VennDiagram” packages, respectively.

2.3. Immune infiltration analysis

   We first used the Timer Web server [[53]17] to conduct a series of
   analyses on the expression of ALDOB and the infiltration abundance of
   immune cells in KIRC through the gene module. For more detailed
   characterization of immune cell types, we employed the “CIBERSORT”
   [[54]18] R package, which utilizes gene expression data to estimate the
   abundance of individual cell types within a heterogeneous cell
   population. This approach was utilized to investigate potential
   correlations between ALDOB expression and immune cell populations.

2.4. Cell lines and reagent

   The KIRC cell lines 786-O and ACHN were obtained from the Shanghai
   Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences
   in Shanghai, China. The 786-O cells were cultured in RPMI-1640 medium,
   while the ACHN cells were cultured in DMEM medium. Both media were
   supplemented with 10 % fetal bovine serum (FBS) and 1 % Pen/Strep. The
   RCC4 cells were cultured in McCOY's 5A medium supplemented with 10 %
   FBS and 1 % Pen/Strep. The Renca cells, on the other hand, were
   cultured in 1640 medium supplemented with 10 % FBS, 1 % Pen/Strep,
   non-essential amino acids (NEAA) at an extra concentration of 0.1 mM,
   sodium pyruvate at an extra concentration of 1 mM, and l-glutamine at
   an extra concentration of 2 mM. All cell lines were maintained in a
   controlled environment at 37 °C with a CO[2] concentration of 5 %.
   Regular assessments for mycoplasma contamination were carried out using
   PCR techniques.

2.5. Overexpression and depletion of genes in cell lines

   Stable overexpression of ALDOB was achieved in 786-O, ACHN, RCC4, and
   Renca cells by infecting them with Flag-ALDOB lentiviral particles
   (Miaoling Biology). Cells were then incubated for 48 h and subsequently
   selected in a medium containing 5 μg/ml puromycin for 7 days to enhance
   ALDOB expression. To generate ALDOB knockdown (KD) cells, human ALDOB
   shRNA plasmid and mouse ALDOB shRNA plasmid (Miaoling Biology) were
   transfected into the aforementioned cell lines. Following transfection,
   cells were sorted based on green fluorescent protein (GFP) fluorescence
   to isolate successful ALDOB knockdown cells. Subsequently, PCR and
   Western blot analyses were conducted to assess the efficiency of ALDOB
   overexpression or depletion.

2.6. Western blot analysis

   Cells were lysed in ice-cold RIPA lysis buffer containing 1 mM
   phenylmethanesulfonyl fluoride to extract proteins. Protein
   concentration in the lysates was determined using the BCA protein assay
   kit. Equal amounts of total protein were separated on 10 % SDS-PAGE and
   transferred onto a PVDF membrane. The membrane was then incubated with
   specific antibodies against ALDOB and β-actin. After washing with TBST,
   the membranes were exposed to HRP-conjugated secondary antibodies and
   visualized using a ChemiScopeTouch imaging system. The intensity of
   immunoblot bands was quantified using ImageJ 1.8.0 software.

2.7. Colony formation assay

   Cells from various experimental groups were plated at a density of
   5000 cells per well in 6-well plates and incubated for 10–14 days with
   regular medium changes every two days to support colony growth. After
   the incubation period, colonies were rinsed with PBS, fixed with 4 %
   formaldehyde for 30 min, and stained with 0.5 % crystal violet in 10 %
   methanol for another 30 min. Subsequently, the plates were washed,
   air-dried, and examined for colony visualization.

2.8. Cell growth assay

   For cell growth assessment, cells from different experimental groups
   were seeded in 6-well plates at a density of 10000 cells per well. The
   cell numbers were subsequently quantified on days 2, 4, and 6
   post-seeding to compare growth rates.

2.9. In vitro wound healing assay

   For in vitro wound healing assays, cells were cultured in complete
   medium in 6-well plates until reaching approximately 100 % confluence.
   Subsequently, the cells were serum-deprived for 12 h at 37 °C and 5 %
   CO[2]. A scratch was created using a 1000 μl pipette tip, followed by
   washing the wells with phosphate-buffered saline and replacing the
   medium with serum-free medium. Images were captured at 0 h and 24 h
   post-scratching. The width of the scratch at both time points was
   measured, and wound closure percentage was calculated using the
   formula: % wound closure = ((0 h wound area – 24 h wound
   area) × 100)/0 h wound area.

2.10. Transwell assay

   For the cell migration assay utilizing transwell filter inserts with
   8 μm pore size, 5 × 10^4 cells (786-O) or 2 × 104 cells (Renca) in
   serum-free medium were added to the upper chamber of each insert. The
   inserts were then placed in a 24-well plate containing 500 μl of
   complete medium. Following a 12-h incubation at 37 °C, cells in the
   upper chamber were gently removed, while the migrated cells were fixed
   with methanol and stained with 0.05 % crystal violet solution (100 μl).
   Subsequently, the inserts were examined under an inverted microscope
   (100 × magnification), and images of five fields per insert were
   captured. The migrated cells stained with crystal violet were
   quantified and compared.

2.11. Animal care and use

   The animal study was approved by the Animal Ethics Committee of Quzhou
   People's Hospital. Male 6-week-old Balb/c mice and Nude mice were
   obtained from the Nanjing Biomedical Research Institute of Nanjing
   University. For the study, 8-week-old male BALB/c nude mice or Nude
   mice were subcutaneously injected with 5 × 105 Renca, Renca ALDOB OE,
   or Renca ALDOB KD cells in the right flank. The growth of the
   subcutaneous tumors was monitored by caliper measurements, and tumor
   volume was calculated using the formula: length × width [[55]2] × 0.5.
   The ethical guidelines limited the tumor size to 2000 mm^3 per mouse,
   and this threshold was strictly adhered to throughout the study. Upon
   sacrifice, the tumors were excised, weighed, and preserved in 10 %
   formaldehyde for further analysis. Quzhou People's Hospital Ethics
   Committee approved this study (ID: 20220121003).

2.12. IHC staining

   Immunohistochemistry (IHC) staining was performed to evaluate the
   expression of CD8 in formalin-fixed paraffin-embedded mouse tumor
   tissue sections. The slides, with a thickness of approximately 3–5 μm,
   underwent a heat-induced epitope retrieval process at 68 °C for 60 min.
   Subsequently, the slides were deparaffinized and rehydrated. Following
   preparation, specific primary antibodies targeting CD8 were applied and
   allowed to incubate overnight at 4 °C. This was followed by exposure to
   a biotin-conjugated secondary antibody at room temperature for 30 min.
   Visualization of the proteins was achieved using the Diaminobenzidine
   Chromogen Kit (BDB2004, Biocare). To improve visibility, the slides
   were counterstained with diluted hematoxylin and examined under a light
   microscope. Representative images were captured using ZEN Connect
   software from Zeiss. The IHC staining facilitated the quantification of
   the protein signal by utilizing Image J software.

2.13. Warburg effect assessment

   In order to assess the Warburg effect, tumor cells with different gene
   expression profiles (WT, ALDOB overexpression, and ALDOB knockdown)
   were cultured and subjected to metabolic analysis. Colorimetric assay
   kits for measuring glucose consumption (Sigma, MAK083-1 KT) and lactate
   production (Sigma, MAK065-1 KT) were utilized following the
   manufacturer's instructions. The rates of glucose consumption and
   lactate production were determined using an iMark™ Microplate Reader
   (Bio-Rad, 168–1130). To account for potential variations in cell
   proliferation induced by growth factors, the measured values were
   normalized to the cell number.

2.14. Statistical analyses

   Kaplan-Meier curves generated with the “survival” R package were used
   to display survival-related analysis results. The P value was
   determined by a log-rank test. The receiver operating characteristic
   (ROC) curve over time was used to show the accuracy of the biomarker.
   We did the ROC analysis with the “pROC” R package. In the other
   statistical analysis parts, we used unpaired two-tailed Student's
   t-test. And all these data analyses were performed using the R program
   (R 4.2.1).

3. Results

3.1. Identification of differentially expressed genes

   We conducted a comparative analysis of the gene expression between KIRC
   samples and normal renal samples using data from three GSE
   ([56]GSE100666, [57]GSE53757, and [58]GSE12606) projects. To identify
   differentially expressed genes (DEGs) in KIRC samples relative to
   normal tissues, we implemented stringent filtering criteria.
   Specifically, we required an adjusted p-value of less than 0.01 and a
   fold change greater than 2 (|Log2FC| > 2). Subsequently, we visually
   represented the DEGs using a volcano plot ([59]Fig. 1A). To increase
   the reliability of our findings, we considered genes that displayed
   consistent trends across multiple projects as more likely to be
   associated with the occurrence and progression of the disease. By
   overlapping the DEGs identified in these three projects, we discovered
   12 up-regulated genes and 28 down-regulated genes that were
   consistently dysregulated in KIRC samples compared to normal tissues
   ([60]Fig. 1B). We have meticulously compiled a comprehensive list of
   these genes in [61]Table 1.

Fig. 1.

   [62]Fig. 1
   [63]Open in a new tab

   Identification of the prognosis gene. A The volcano plots of three GSE
   ([64]GSE100666, [65]GSE53757, and [66]GSE12606) project DEGs; B The
   Venn diagram of overlapping DEGs; C The Kaplan-Meier survival curve of
   three candidate prognosis genes (ALDOB, HEPACAM2, and DNAH11); D The
   ROC curve of three candidate prognosis genes (ALDOB, HEPACAM2, and
   DNAH11). Abbreviations: TCGA.

Table 1.

   Lists 12 overlapping up-regulated genes and 28 overlapping
   down-regulated genes.
   DEGs Gene names
   up-regulated DNAH11, CCND1, ANGPT2, HEY1, VWF, TNFAIP6, COL23A1,
   SLC6A3, CA9, ANGPTL4, HILPDA, NDUFA4L2
   down-regulated SOST, C16orf89, TFAP2B, RIMBP2, ELF5, ATP6V1C2, GGACT,
   RHBG, TMEM52B, RHCG, FXYD4, CLCNKA, HEPACAM2, SERPINA5, ATP6V0D2,
   TDGF1, TMEM213, DPEP1, HRG, KCNJ1, UMOD, CTXN3, CLDN8, RALYL, CALB1,
   XPNPEP2, KNG1, ALDOB
   [67]Open in a new tab

3.2. Identification of the prognosis gene

   To gain insights into the potential impact of these 40 genes on the
   development of KIRC, we performed a survival analysis using gene
   expression data and prognostic information obtained from the TCGA
   database. Using a significance threshold of p < 0.01, we identified
   three genes: ALDOB, HEPACAM2, and DNAH11, which showed significant
   associations with the prognosis of KIRC patients ([68]Fig. 1C). To
   determine the most promising prognostic gene among these candidates, we
   conducted a receiver operating characteristic (ROC) analysis.
   Remarkably, ALDOB emerged as the gene with the most remarkable
   prognostic potential, as it demonstrated superior performance in the
   ROC analysis (TCGA, All patients: 1 year AUC = 0.74; 3 years
   AUC = 0.63; 5 years AUC = 0.66) ([69]Fig. 1D). These findings provide
   compelling evidence that ALDOB could serve as a robust prognostic
   marker in KIRC.

3.3. ALDOB-related pathway analysis

   After identifying ALDOB as a potential prognostic biomarker for KIRC,
   we aimed to explore the underlying pathways through which it exerts its
   effects. Utilizing RNA data from 608 KIRC patients sourced from the
   TCGA database, we stratified them into two groups based on the median
   expression of ALDOB: a high-expression ALDOB group and a low-expression
   ALDOB group. Subsequently, we conducted differential gene expression
   analysis between these two groups. The differentially expressed genes
   were then subjected to Gene Ontology (GO) analysis, Kyoto Encyclopedia
   of Genes and Genomes (KEGG) analysis, and Hallmark pathway enrichment
   analysis to elucidate the biological processes and pathways associated
   with ALDOB expression in KIRC ([70]Fig. 2A–C). In the GO pathway
   enrichment analysis, we observed that the high-expression ALDOB group
   exhibited significant associations with pathways related to excretion,
   pressure natriuresis, and acute-phase response ([71]Fig. 2A). These
   findings suggest that patients in the high-expression ALDOB group may
   exhibit stronger clinical responses to treatment. Furthermore, the KEGG
   pathway enrichment analysis revealed that retinol metabolism, tyrosine
   metabolism, bile secretion, and phenylalanine metabolism were enriched
   in the high-expression ALDOB group, all of which are related to the
   upstream and downstream pathways of glycolysis ([72]Fig. 2B).
   Consistently, the Hallmark pathway enrichment analysis also revealed a
   significant association between ALDOB expression and bile acid
   metabolism ([73]Fig. 2C). Collectively, these results indicate that the
   role of the ALDOB gene in renal cancer is closely intertwined with
   glycolysis. Overall, our findings suggest that ALDOB may exert its
   prognostic potential in KIRC through its involvement in key pathways,
   primarily those related to glycolysis. These insights shed light on the
   underlying mechanisms and lay the groundwork for further exploration of
   the functional role of ALDOB in KIRC.

Fig. 2.

   [74]Fig. 2
   [75]Open in a new tab

   ALDOB-related pathway analysis and immune cell infiltration analysis in
   KIRC. A GO pathway enrichment analysis; B KEGG pathway enrichment
   analysis; C Hallmark pathway enrichment analysis; D The association
   between immune cells and ALDOB expression in TIMER website; E
   Relationship between ALDOB expression and immune cells in the
   “CIBERSORT” database.

3.4. The immune cell infiltration landscape in KIRC

   To delve deeper into the relationship between ALDOB expression and the
   immune landscape of KIRC, we performed immune infiltration analysis
   using the TIMER website and the “CIBERSORT” R package ([76]Fig. 2D and
   E). [77]Fig. 2D illustrates the relationship between ALDOB expression
   levels and the infiltration of various immune cell types in KIRC. It is
   apparent from the analysis that ALDOB expression in KIRC exhibits a
   significant association with the infiltration of B cells
   (p = 2.22e-02), CD8^+ T cells (p = 1.23e-01), and neutrophil cells
   (p = 3.05e-01). Notably, the infiltration of B cells and CD8^+ T cells
   showed a positive correlation with ALDOB expression, whereas the
   infiltration of neutrophil cells displayed a negative correlation
   ([78]Fig. 2D). Additionally, we utilized the “CIBERSORT” package for
   immune infiltration analysis, which provides a comprehensive assessment
   of immune cell categories using linear support vector regression for
   deconvolution analysis. The results revealed significant associations
   between ALDOB expression and the infiltration of resting dendritic
   cells (p = 5.9e-05), M0-macrophages (p = 0.00057), M1-macrophages
   (p = 0.00206), activated NK cells (p = 0.01052), plasma cells
   (p = 0.02763), and CD8^+ T cells (p = 0.02724) in KIRC ([79]Fig. 2E).
   Both analysis tools consistently demonstrated a significant correlation
   between ALDOB expression and the infiltration of CD8^+ T cells in KIRC.
   In conclusion, our findings indicate that ALDOB expression is
   associated with the infiltration of various immune cell types,
   suggesting its potential value in immunotherapy for KIRC. The positive
   correlation with CD8^+ T cell infiltration further highlights the
   potential immunotherapeutic implications of ALDOB in KIRC.

3.5. ALDOB inhibits the growth and migration of RCC cells in vitro

   To investigate the biological function of ALDOB in tumor progression
   and its association with renal cell carcinoma (RCC), we conducted ALDOB
   overexpression and knockdown experiments in human renal cancer cell
   lines (786-O, RCC4, ACHN) and a mouse renal cancer cell line (Renca).
   The efficacy of ALDOB overexpression and knockdown was confirmed
   through Western blot experiments ([80]Supplementary Fig. 2). In colony
   formation assays, we observed that overexpression of ALDOB
   significantly suppressed the formation of tumor cell colonies, whereas
   ALDOB knockdown notably promoted colony formation ([81]Fig. 3A,
   [82]Supplementary Fig. 3A). These results suggest that ALDOB plays a
   pivotal role in regulating the clonal growth ability of renal cancer
   cells. In line with these findings, cell growth experiments
   demonstrated that ALDOB overexpression markedly suppressed the growth
   rate of renal cancer cell lines, whereas ALDOB knockdown conversely
   accelerated the growth rate of renal cancer cells ([83]Fig. 3B,
   [84]Supplementary Fig. 3B). These findings suggest that ALDOB may serve
   as a potential regulator of cellular proliferation in RCC. Furthermore,
   wound healing assays were conducted to assess the migratory ability of
   cells. Overexpression of ALDOB significantly inhibited the migration
   ability of 786-O and Renca cells, whereas ALDOB knockdown notably
   promoted their migration ability ([85]Fig. 3C and D). In addition, the
   cell transwell assay demonstrated that ALDOB expression significantly
   decreased the number of migrating cells in both 786-O and Renca cells
   ([86]Supplementary Fig. 4). Overall, these experimental results
   indicate that ALDOB exerts a significant impact on tumor cell colony
   formation, cell growth, and cell migration in RCC. The outcomes of
   ALDOB overexpression and knockdown experiments collectively support its
   potential role as a key factor in the regulation of RCC progression.

Fig. 3.

   [87]Fig. 3
   [88]Open in a new tab

   ALDOB expression decreased growth and migration abilities of 786-O and
   Renca cells. Colony formation assays(A) and cell growth curves(B) were
   performed to assess the proliferation of ALDOB KD or ALDOB OE
   transduced 786-O and Renca cells.Cell wound healing assays in 786-O(C)
   and Renca(D) cells with or without ALDOB OE or KD. The width of cell
   wound healing was measured (n = 5/group). WT, wild type; ALDOB OE,ALDOB
   overexpression; ALDOB KD, ALDOB knockdown. Data were presented as means
   ± standard deviations. P values were determined by the one-way ANOVA,
   **P < 0.01, ***P < 0.001, ****P < 0.0001.

   We further explored the impact of ALDOB on tumor cell metabolism
   following its implication in glycolysis based on RNA-seq analysis. Our
   investigation revealed that ALDOB overexpression (OE) led to a decrease
   in medium acidification in renal cancer cell lines, indicative of
   suppressed cancer cell metabolism ([89]Supplementary Fig. 5A).
   Conversely, ALDOB knockdown (KD) accelerated medium acidification,
   suggesting enhanced cancer cell metabolism ([90]Supplementary Fig. 5A).
   Furthermore, ALDOB KD promoted glucose consumption and lactate
   production, characteristic features of the Warburg effect, while ALDOB
   OE significantly inhibited these metabolic processes ([91]Supplementary
   Fig. 5B).

3.6. Regulation of anti-tumor immunity by ALDOB

   To further confirm the impact of ALDOB on tumor growth and its role in
   anti-tumor immunity, we conducted in vivo experiments by injecting
   ALDOB WT and ALDOB OE Renca cells into nude mice and Balb/c mice
   subcutaneously ([92]Fig. 4A–D). Interestingly, we found that
   overexpression of ALDOB effectively inhibited the growth rate of renal
   cancer tumors in mice. However, this inhibitory effect was more
   pronounced in immune-intact Balb/c mice ([93]Fig. 4B–C, Fig. 4E and F).
   Additionally, we observed an increase in infiltrated CD8^+ T cells in
   the ALDOB OE Renca group ([94]Supplementary Fig. 6A). These findings
   suggest that ALDOB overexpression may activate anti-tumor immunity
   within the tumors, contributing to the suppression of tumor growth.
   Furthermore, we performed similar experiments by injecting ALDOB WT and
   ALDOB KD Renca cells into nude mice and Balb/c mice subcutaneously
   ([95]Fig. 4G–J). Strikingly, ALDOB knockdown significantly increased
   the growth rate of renal cancer tumors in mice, particularly in
   immune-intact Balb/c mice ([96]Fig. 4H- I, [97]Fig. 4K-L). Moreover, we
   observed a decrease in infiltrated CD8^+ T cells in the ALDOB KD Renca
   group ([98]Supplementary Fig. 6B). Taken together, these in vivo
   experiments provide further validation that ALDOB plays a crucial role
   in tumor growth regulation. The differential effects of ALDOB
   overexpression and knockdown on tumor growth in immune-intact mice
   suggest its involvement in modulating anti-tumor immune responses,
   particularly through the regulation of CD8^+ T cell infiltration. These
   findings highlight the significance of ALDOB in the interplay between
   tumor growth and anti-tumor immunity in renal cancer.

Fig. 4.

   [99]Fig. 4
   [100]Open in a new tab

   ALDOB inhibits growth of Renca cell in vivo. WT nude mice (n = 5) were
   orthotopically inoculated with WT Renca cells and ALDOB OE Renca
   cells(A). Quantification of tumor growth (B) and tumor weight (C) of
   tumor. WT balb/c mice (n = 5) were orthotopically inoculated with WT
   Renca cells and ALDOB OE Renca cells(D). Quantification of tumor growth
   (E) and tumor weight (F) of tumor. WT nude mice (n = 5) were
   orthotopically inoculated with WT Renca cells and ALDOB KD Renca
   cells(G). Quantification of tumor growth (H) and tumor weight (I) of
   tumor. WT balb/c mice (n = 5) were orthotopically inoculated with WT
   Renca cells and ALDOB KD Renca cells(J). Quantification of tumor growth
   (K) and tumor weight (L) of tumor. P values were determined by the
   unpaired Student's t-test, *P < 0.05, **P < 0.01, ***P < 0.001,
   ****P < 0.0001.

4. Discussion

   Kidney Renal Clear Cell Carcinoma (KIRC) is a prevalent and highly
   dangerous disease among kidney disorders. Therefore, the identification
   of effective biomarkers for early disease diagnosis is of utmost
   significance. Through the analysis of multiple public datasets
   ([101]GSE100666, [102]GSE79973, and [103]GSE12606), we identified a
   total of 40 commonly deregulated genes, among which 12 were
   up-regulated and 24 were down-regulated. Using the standard of
   significant threshold of p < 0.01 in the Kaplan-Meier survival analysis
   results, we narrowed the range of prognostic biomarkers studied mainly
   to ALDOB, HEPACAM2, and DNAH11. Then, the ROC analysis results showed
   that ALDOB had better performance in the one-year, three-year and
   five-year periods. Ultimately, we focused on ALDOB for further
   research.

   Aldolase B, a protein encoded by the ALDOB gene, demonstrates
   predominant expression in the adult liver, kidneys, and intestines
   [[104]19]. Previous investigations have elucidated the crucial role of
   ALDOB in metabolism and glycolysis [[105]20]. Primarily, ALDOB
   functions as a catalyst, facilitating the conversion of fructose
   1-phosphate to dihydroxyacetone phosphate and glyceraldehyde [[106]19].
   Encouragingly, our pathway analysis aligns with these findings as we
   observe the enrichment of multiple upstream and downstream products and
   pathways related to glycolysis in the subgroup exhibiting high ALDOB
   expression. This discrepancy challenges our conventional understanding.
   Previous studies have convincingly demonstrated that cancer cells under
   aerobic conditions readily consume significant quantities of glucose
   via an “inefficient” form of glycolysis, also known as aerobic
   glycolysis or the Warburg effect, to satisfy their energy requirements
   for growth and proliferation [[107]21,[108]22]. However, in the case of
   KIRC, we observe lower ALDOB expression in tumors compared to adjacent
   tissues. While it would be premature to equate ALDOB expression solely
   with the occurrence of glycolysis, this intriguing observation
   highlights the need for further exploration. Moreover, we report a
   compelling correlation between the expression of ALDOB and the
   infiltration of diverse immune cell types, underscoring its potential
   as a predictive marker in the context of immunotherapy.

   Furthermore, we discovered through additional studies that ALDOB serves
   as a significant prognostic marker in various cancer types
   [[109]20,[110][23], [111][24], [112][25]]. Expanding upon these
   findings, we utilized the TIMER platform to analyze the expression
   patterns of ALDOB in numerous cancer types. Strikingly, in
   kidney-related cancers, the expression of ALDOB in tumor tissues
   exhibited a notable decrease compared to that in normal kidney tissues.
   Similarly, this trend was observed in breast cancer, intrahepatic
   cholangiocarcinoma, liver cancer, and other cancer types
   ([113]Supplementary Fig. S1). These promising observations highlight
   the potential of ALDOB as a valuable asset in early cancer screening
   initiatives. Further investigation is warranted to fully explore its
   diagnostic and prognostic capabilities.

   Despite the significant findings and potential implications of our
   research, it is important to acknowledge the limitations and gaps that
   remain in our study. Primarily, we recognize that we have not delved
   deeply enough into the underlying mechanisms by which ALDOB exerts its
   protective effect in KIRC. While we have established a correlation
   between ALDOB expression and various outcomes, we have yet to
   thoroughly investigate the specific molecular pathways and interactions
   through which this gene influences the development and progression of
   KIRC. Additionally, our analysis is primarily based on bioinformatics
   data and lacks direct experimental validation. Though bioinformatics
   approaches are valuable in generating hypotheses and identifying
   potential markers, further experimental validation is essential to
   confirm and expand upon our findings. Furthermore, our study focuses on
   the association between ALDOB expression and cancer-related outcomes
   without considering other confounding factors, such as patient
   characteristics and treatment regimens. Incorporating these factors
   would provide a more comprehensive understanding of ALDOB's role in
   cancer biology.

   In conclusion, while our research sheds light on the potential
   significance of ALDOB expression as a prognostic marker and its
   association with immune cell infiltration in various cancers, more
   comprehensive and mechanistic investigations are needed to fully
   elucidate the underlying molecular mechanisms. Furthermore,
   experimental validation and consideration of confounding factors are
   essential for strengthening the clinical utility and broadening the
   scope of our findings.

Data availability statement

   The datasets supporting the results and conclusions of this study were
   downloaded from the Cancer Genome Atlas (TCGA)
   ([114]http://tcga-data.nci.nih.gov/tcga/) and Gene Expression Omnibus
   (GEO) ([115]http://www.ncbi.nlm.nih.gov/geo/), and we thank TCGA and
   GEO for providing transcriptomics and clinicopathological data. The
   accession number are [116]GSE100666
   , [117]GSE5375, and [118]GSE12606. Further inquiries can be directed to
   the corresponding authors.

Ethics statement

   All animal experiments were approved by Research ethic committee of the
   Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou
   People's Hospital, ID of the approval: 20220121003.

CRediT authorship contribution statement

   Xiao-yang Li: Writing – original draft. You-yao Xu: Writing – review &
   editing. Sen-yan Wu: Formal analysis. Xi-xi Zeng: Conceptualization.
   Yan Zhou: Formal analysis, Conceptualization. Guo-bin Cheng:
   Supervision.

Declaration of competing interest

   The authors declare that they have no known competing financial
   interests or personal relationships that could have appeared to
   influence the work reported in this paper.

Footnotes

   ^Appendix A
   Supplementary data to this article can be found online at
   [119]https://doi.org/10.1016/j.heliyon.2024.e29368.
   List of abbreviations
   Abbreviation Full name
   KIRC         Kidney clear cell renal cell carcinoma
   RCC          Renal cell carcinoma
   DEGs         Different expression genes
   FBS          Fetal bovine serum
   GO           Gene Ontology
   KEGG         Kyoto Encyclopedia of Genes and Genomes
   IHC          Immunohistochemistry
   ROC          receiver operating characteristic
   TCGA         The Cancer Genome Atlas
   GEO          Gene Expression Omnibus
   IDH1/2       Isocitrate dehydrogenase 1/2
   SPF          specific-pathogen-free
   [120]Open in a new tab

Contributor Information

   You-yao Xu, Email: xuyouyaoya@126.com.

   Guo-bin Cheng, Email: 745798889@qq.com.

Appendix ASupplementary data

   The following are the Supplementary data to this article:
   Multimedia component 1
   [121]mmc1.pdf^ (20.1MB, pdf)
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   [122]mmc2.pdf^ (224.4KB, pdf)
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   [129]mmc9.xlsx^ (3.2MB, xlsx)

References