Abstract

Background

   Since the pathophysiology of clear cell renal cell carcinoma (ccRCC)
   was still unknown, finding novel therapeutic targets to support current
   treatment approaches was crucial.

Methods

   We conducted an RNA-Sequencing analysis using data from the TCGA-KIRC
   dataset, with our own clinical data serving as validation. Kaplan–Meier
   survival curves, univariate and multivariate studies of overall
   survival, and differential expression analysis were all used.
   Furthermore, we explored the correlation between LGALS1 (Galectin-1)
   and clinical characteristics in ccRCC patients by leveraging proteomics
   data from the CPTAC analysis. Additionally, the Single-cell
   RNA-Sequencing (scRNA-Seq) analysis elucidated the cellular landscape
   of LGALS1 using the [32]GSE159115 database, complemented by pathway
   enrichment analysis via GO and KEGG datasets. Lastly, we performed an
   immunohistochemistry (IHC) analysis on ccRCC tissue microarrays and
   scored the staining intensity. While p < 0.05 was used for other
   analyses, p < 0.1 was deemed significant in multivariate analysis.

Results

   As shown in TCGA and confirmed in our clinical data, higher LGALS1
   expression in ccRCC was associated with a bad prognosis. Proteomics
   analysis in the CPTAC dataset revealed that LGALS1 levels were
   statistically significant across different grade and stage groups of
   patients. The high expression was confirmed on the IHC chip, with tumor
   tissue displaying a higher staining intensity score. Additionally,
   scRNA-Seq analysis identified that the differential expression of
   LGALS1 primarily occurred in endothelial and epithelial cells, along
   with associated signaling pathways.

Conclusions

   We performed a multi-omics analysis using transcriptomics, proteomics,
   single-cell omics, and our clinical data, confirming LGALS1 as a
   possible target for ccRCC.

   Keywords: LGALS1, ccRCC, Multi-omics analysis, Single-cell RNA-Seq

Introduction

   According to the American Cancer Society, in 2025, renal carcinoma is
   expected to cause 80,980 new cases and 14,510 fatalities in the United
   States [[33]1]. Clear cell renal cell carcinoma (ccRCC) was the most
   common histological subtype of renal carcinoma, accounting for
   approximately 85% of cases [[34]2]. Often exhibiting no symptoms,
   patients with ccRCC were discovered by chance during regular imaging
   tests [[35]3]. The deletion of the Von Hippel-Lindau (VHL) gene, a key
   molecular hallmark of ccRCC, played a pivotal role in its pathogenesis
   [[36]4]. The primary treatment modality for localized ccRCC was
   surgical resection. Surgical excision was the main therapy option for
   localized ccRCC. A number of variables, such as tumor size, renal
   pelvic involvement, and other pertinent considerations, influenced the
   decision between partial and radical nephrectomy [[37]5]. Furthermore,
   chemotherapy and radiation therapy did not usually work in ccRCC.
   Tyrosine kinase inhibitors (TKIs), such axitinib, and immune checkpoint
   inhibitors that target the PD-1/PD-L1 pathway, like pembrolizumab, were
   the most widely used adjuvant treatments [[38]6]. Patients with
   metastatic ccRCC often experienced poor outcomes. For instance, those
   treated with nivolumab typically had a median progression-free survival
   of approximately 11.3 months [[39]7].

   A member of the beta-galactoside-binding protein family, Galectin-1
   (LGALS1) played a role in regulating interactions between cells and
   between cells and matrices [[40]8]. It was identified as being closely
   associated with inflammation, fibrosis, immunity, and other biological
   processes, particularly in carcinomas [[41]9]. Researchers found that
   atherosclerosis and abdominal aortic aneurysms are caused by
   pathological vascular remodeling when the LGALS1 gene is silenced
   [[42]10]. In a hypertonic glucose environment, LGALS1 was found to
   interact with and potentiate the effects of the receptor for activated
   protein C kinase 1, thereby promoting inflammation in diabetic
   nephropathy. At the same time, Li and colleagues identified a compound
   called Bruceine A that targets this gene, which had the potential to
   inhibit the aforementioned pathological biological processes [[43]11].
   By improving STING protein stability, LGALS1 was discovered to mediate
   the migration and accumulation of myeloid-derived suppressor cells,
   preserving the NF-κB signaling pathway in tumor cells [[44]12].
   Additionally, earlier research found that LGALS1 was significantly
   expressed and linked to a poor prognosis acute myeloid leukemia
   [[45]13] and glioblastoma [[46]14].

   We performed a comprehensive clinical data analysis, integrating
   information from multiple databases, with the objective of identifying
   potential risk factors for ccRCC. The ultimate objective of this effort
   was to identify potential treatment targets for this illness. The
   expression levels of the target gene were investigated, and prognostic
   and survival analyses were performed simultaneously. Furthermore, the
   expression of the gene was validated in ccRCC tissue samples. Utilizing
   single-cell sequencing technology, we elucidated the cytological
   landscape of the selected gene and its associated signaling pathways.

Method and materials

RNA-Seq analysis of LGALS1 in ccRCC

   TCGA-KIRC RNA-Seq data and clinical information were obtained from The
   Cancer Genome Atlas (TCGA) database, containing 72 controls and 539
   cancer samples. All tumor specimens were histopathologically confirmed
   as ccRCC. The follow-up spanned 3,668 days, during which 367 deaths
   occurred. The transcripts per million (TPM) approach was used to
   standardize the expression levels of LGALS1. The “limma” package in R
   was used to do differential expression analysis, with a p-value cutoff
   of less than 0.05. Log-rank testing was used to assess the association
   between LGALS1 expression and patients’ overall survival (OS) as
   determined by Kaplan-Meier survival curves. The predictive value of
   LGALS1 expression was assessed using univariate and multivariate Cox
   regression analyses of OS, controlling for clinical variables such age,
   gender, tumor stage, grade, and race. These investigations shed light
   on the possible significance of LGALS1’s RNA expression in ccRCC.

Proteomics analysis of LGALS1 in ccRCC

   We accessed the UALCAN website
   ([47]http://ualcan.path.uab.edu/index.html) and navigated to the
   “Proteomics” section to explore the protein expression of LGALS1 in
   ccRCC [[48]15]. We selected the ccRCC dataset and used boxplots and
   t-tests or Wilcoxon rank-sum tests to analyze the protein expression
   levels of LGALS1 across tumor and normal tissues. Furthermore, we
   investigated associations between clinical characteristics such tumor
   stage and grade and LGALS1 expression. These analyses provided insights
   into the potential protein expression significance of LGALS1 in ccRCC.

Single-cell RNA-Seq (scRNA-Seq) analysis of LGALS1 in ccRCC

   ScRNA-Seq analyses of the expression of LGALS1 in ccRCC were conducted
   by analyzing the [49]GSE159115 dataset from the Gene Expression Omnibus
   (GEO) database ([50]https://www.ncbi.nlm.nih.gov/geo/). The data were
   preprocessed and normalized using the “Seurat” R package. Cells were
   clustered and annotated based on known markers, as the previous article
   described [[51]16]. The TSNE and violin plots were used to visualize
   the results of a differential expression study that compared the
   expression of LGALS1 in different cells between tumor and normal
   samples. The LGALS1-associated pathways were obtained using the
   “irGSEA” R package, and the physiological process connected to LGALS1
   in specific cells of ccRCC was obtained using the GO enrichment
   approach.

Immunohistochemistry (IHC) chip

   After excluding samples with missing or erroneous data, 93 tumor
   specimens and 87 matched peritumoral tissues were included; all were
   obtained surgically. Histopathologic review confirmed ccRCC in every
   tumor sample. The IHC analysis of LGALS1 in ccRCC was conducted using
   tissue microarrays by SHANGHAI OUTDO BIOTECH CO. LTD. The LGALS1
   antibody was purchased from Proteintech (Cat. No. 11858-1-AP; Wuhan,
   China). The expression of LGALS1 in the cytoplasm of cancer and
   peritumoral tissues was evaluated using the IHC analysis of LGALS1 in
   ccRCC tissue microarrays. The percentage of positive cells was assessed
   on a range of 0–100%, while the staining intensity was rated as 0
   (negative), 1 (1+), 2 (2+), or 3 (3+). The staining intensity score and
   the percentage of positive cells score were multiplied to determine the
   final score. Nonparametric tests were used to analyze the expression
   levels of LGALS1 in ccRCC and peritumoral tissues. The median
   postoperative follow-up of these patients was 106 days, during which 31
   deaths occurred. Kaplan-Meier survival analysis and log-rank tests were
   employed to evaluate the correlation between LGALS1 expression levels
   and OS in ccRCC patients. Besides, the clinical data for the cohort
   were analyzed using the Cox proportional-hazards model to assess the
   influence of various factors on survival. In addition, the variables
   that exhibited statistical significance in the univariate analysis were
   incorporated into the subsequent multivariate analysis. Specifically,
   the variates included LGALS1 expression, sex (male vs. female), age
   (≤ 60 vs. > 60 years), tumor size (≤ 5 cm vs. > 5 cm), and TNM stage:
   stage I–II (T1–2 N0 M0) vs. stage III–IV (all other TNM stages).

Statistical analysis

   All statistical analyses were performed using R software (version
   4.3.2), and a significance level of P < 0.05 was applied. Differential
   expression analysis was performed via t-tests or Wilcoxon rank-sum
   tests. Kaplan-Meier survival analysis and log-rank tests were used to
   analyze the OS prognostic value in ccRCC.

Results

The mRNA expression and prognosis correlation of LGALS1 in ccRCC

   Our analysis of TCGA-KIRC data revealed that LGALS1 expression in tumor
   tissues was significantly higher than that in normal tissues
   (p < 2.22 × 10⁻¹⁶), as shown in Fig. [52]1A. Moreover, as illustrated
   in Fig. [53]1B, we conducted paired analyses that further confirmed the
   statistically significant difference (p < 7.7 × 10⁻¹²). The survival
   analysis depicted in Fig. [54]1C revealed that patients with high
   LGALS1 expression had poorer survival outcomes. Furthermore, we carried
   out univariate analysis (Fig. [55]1D) and multivariate analysis
   (Fig. [56]1E) using these data from TCGA. And we came to the conclusion
   that, although the T and M stages were only significant in univariate
   analysis, age, grade, and stage clearly affected the survival and
   prognosis of patients with ccRCC. Surprisingly, LGALS1 was found to be
   a risk factor for ccRCC; both analyses supported this finding. This
   implied that LGALS1 might be a valuable therapeutic target and
   predictive biomarker. We set a more permissive p-value threshold to
   enhance the sensitivity of our analysis. A p-value of less than 0.1 was
   considered statistically significant in multiple factor analysis.

Fig. 1.

   [57]Fig. 1
   [58]Open in a new tab

   Analysis results from transcriptomics using RNA-Seq data from the TCGA
   database: A and B: Expression levels of LGALS1 in normal and tumor
   groups (A: unpaired analysis; B: paired analysis); C: Kaplan-Meier
   survival curves; D: Univariate Cox regression analysis of OS; E:
   Multivariate Cox regression analysis of OS

The protein expression and prognosis correlation of LGALS1 in ccRCC

   With an emphasis on the gene and its phosphorylated version, we
   investigated the protein-level expression of LGALS1 and its correlation
   with clinical outcomes in the CPTAC study. The expression level of
   LGALS1 was significantly higher in tumor tissues (LGALS1: Fig. [59]2A;
   phosphorylated form: Fig. [60]2D). Additionally, we assessed the
   expression levels of the gene in subgroups of ccRCC patients at
   different stages and grades. The expression level of LGALS1 was
   elevated across all tumor grades compared to normal tissues, as shown
   in Fig. [61]2B, and the same trend was observed for its phosphorylated
   form in Fig. [62]2E. The similar conclusion for the stage group was
   shown by Fig. [63]2C and F for LGALS1 and its phosphorylated version,
   respectively: tumor tissues expressed more LGALS1 than normal tissues.
   All p-value was less than 0.05.

Fig. 2.

   [64]Fig. 2
   [65]Open in a new tab

   Analysis results from proteomics utilizing the CPTAC database: A, B,
   and C: Expression levels of LGALS1 in different patient groups
   categorized by clinical features (A: normal and ccRCC groups; B:
   different grade groups; C: various stage groups); D, E, and F:
   Expression levels of phosphorylated LGALS1 in the same groups as above

Validation the expression and clinical correlation of LGALS1 in ccRCC samples

   IHC tissue chips were conducted using tumor and surrounding normal
   tissues from patients with ccRCC, which allowed for an intuitive
   display of the variations in LGALS1 levels within the same patient. The
   expression of LGALS1 was greater in the tumor tissue, as seen in
   Fig. [66]3A. The IHC score, as integrated in Fig. [67]3B, revealed that
   tumors exhibited a higher score than in adjacent non-tumor tissue
   (p < 0.001). Specifically, the median IHC score was 1.013 for tumor
   tissue and 0.264 for matched normal tissue. The clinical data of the
   same patients was then examined, and the results were in agreement with
   the two databases mentioned above. The group with high LGALS1 levels
   exhibited worse OS, as shown in Fig. [68]3C (p < 0.0001). Additionally,
   Fig. [69]3D revealed that the area under the ROC curve was 0.9388, with
   the p-value being less than 0.0001. Age, TNM stages, and LGALS1 were
   found to be statistically significant factors affecting the outcomes of
   ccRCC patients in both univariate and multivariate analyses
   (Fig. [70]3E).

Fig. 3.

   [71]Fig. 3
   [72]Open in a new tab

   IHC analysis and survival analysis of LGALS1: A IHC staining of tissue
   microarrays targeting LGALS1; B IHC scores comparing cancerous and
   peritumoral tissues; C Kaplan-Meier survival analysis of OS; D ROC
   curve analysis distinguishing tumor from normal tissue; E Univariate
   and multivariate analyses of OS. LGALS1 was also known as GAL-1

The scRNA-Seq analysis in ccRCC

   As presented in Fig. [73]4A, we intuitively depicted the expression of
   LGALS1 across various cellular subtypes in ccRCC. A deeper shade of red
   in the UMAP graphic denoted higher expression levels. Moreover,
   Fig. [74]4B showed the levels of LGALS1 in various cell types. Besides,
   we found that endothelial and epithelial cells showed especially high
   expression levels. The expression levels of LGALS1 in endothelial and
   epithelial cells differed significantly from those in other cell types,
   as shown by the violin plot in Fig. [75]4C. When matched with the
   normal group, the endothelial and epithelial cells in the tumor group
   were found to have higher expression levels of LGALS1 (Fig. [76]4D).

Fig. 4.

   [77]Fig. 4
   [78]Open in a new tab

   Single-cell omics analysis results using the [79]GSE159115 dataset: A
   Expression of LGALS1 in ccRCC tissue at a cytological field of view; B
   Expression levels of LGALS1 in various cell subtypes within ccRCC; C
   Violin plot showing differential expression of LGALS1 among different
   cell types; D Differential expression analysis between tumor and normal
   groups across cell subtypes

LGALS1 in ccRCC endothelial cells: expression and pathways

   Figure [80]5A displayed the violin plot of LGALS1 expression in
   endothelial cells, which showed that tumor endothelial cells had a
   substantially greater level of LGALS1 than did normal endothelium
   cells. Furthermore, we separated the cells into two groups based on the
   LGALS1 median level. As shown in Fig. [81]5B, tumor endothelial cells
   were more likely to be part of the group with high LGALS1 expression.
   Furthermore, the UMAP plot in Fig. [82]5C intuitively illustrated the
   expression of LGALS1 in endothelial cells. The KEGG and GO databases
   were used for the ensuing pathway enrichment study. The results of the
   former investigation indicated that endothelial cells with increased
   expression of LGALS1 may be associated with the epithelial-mesenchymal
   transition (EMT), fatty acid metabolism, oxidative phosphorylation, and
   other pathways (Fig. [83]5D). While in Fig. [84]5E, data from the GO
   dataset revealed that these cells might be related to functions such as
   growth factor binding, cytokine binding, and others.

Fig. 5.

   [85]Fig. 5
   [86]Open in a new tab

   Specific scRNA-Seq analysis results of LGALS1 in endothelial cells: A
   Violin plot of LGALS1 expression levels in normal and tumor endothelial
   cells; B Boxplots of LGALS1 expression grouped by median level; C
   LGALS1 expression levels in tumor endothelial cells; D Pathway
   enrichment analysis using the KEGG database; E Pathway enrichment
   analysis using the GO database

LGALS1 in ccRCC epithelial cells: expression and pathways

   Normal and malignant cells differed statistically significantly in
   their expression of LGALS1 within their epithelial cells (Fig. [87]6A).
   Furthermore, tumor epithelial cells showed a noticeably greater
   percentage of strong LGALS1 expression, as seen in Fig. [88]6B. The
   overall expression of LGALS1 in epithelial cells was intuitively
   depicted among all epithelial cells in Fig. [89]6C. The pathway
   enrichment analysis using the KEGG database in Fig. [90]6D revealed
   that epithelial cells with high LGALS1 levels tended to activate
   pathways such as MTORC1 signaling, interferon-gamma response, and
   TNF-alpha signaling via NFκB, among others. As Fig. [91]6E showed,
   electron transfer activity, NADH dehydrogenase activity, and other
   associated processes may be important in epithelial cells with high
   LGALS1 expression, according to the GO dataset’s later analysis.

Fig. 6.

   [92]Fig. 6
   [93]Open in a new tab

   The distinctive results of the scRNA-Seq analysis of LGALS1 in
   epithelial cells: A LGALS1 level in a violin plot of normal and
   malignant epithelial cells; B LGALS1 expression boxplots arranged by
   median level; C LGALS1 level in malignant epithelial cells; D and E
   pathway enrichment analysis (D: KEGG database; E GO database)

Discussion

   Several studies highlighted the relationship between LGALS1 and ccRCC
   [[94]17]. Previous study concluded that LGALS1, which was overexpressed
   in ccRCC, was associated with the TNF-alpha signaling pathway, as
   evidenced by RNA-Seq data from the GEO database [[95]18]. Fang et al.
   identified LGALS1 as a prognostic biomarker of ccRCC by building a gene
   co-expression network using data from the TCGA and GEO databases
   [[96]19]. Transcriptomics, proteomics, single-cell omics, and other
   multi-omics data were combined for ccRCC in our work. As a result,
   LGALS1 was identified as a possible candidate biomarker deserving of
   additional research. Specifically, we identified that LGALS1 was highly
   expressed at both the transcriptional and translational levels, and
   that its high expression was associated with worse OS, which served as
   an independent prognostic factor for ccRCC. Subsequently, publicly
   available scRNA-Seq data revealed that the high expression of LGALS1
   predominantly occurred in endothelial and epithelial cells within ccRCC
   tissue.

   The primary cytological features observed in ccRCC were the absence of
   renal-specific endothelial cells and epithelial cells, coupled with
   extensive immune cell infiltration [[97]20]. The upregulation of
   endothelial cells in ccRCC tumor thrombus likely promoted cancer
   progression through cancer-related vasculature formation and cell
   proliferation [[98]21]. Our knowledge of the highly vascularized
   features of ccRCC was improved when it was demonstrated that CD105 was
   elevated in both tumor and vascular endothelium cells [[99]22].
   Targeting the function of cancer-associated endothelial cells was
   currently a first-line treatment strategy in ccRCC [[100]23].
   Anti-angiogenic drugs, such as axitinib and bevacizumab, functioned by
   inhibiting the proliferation and function of endothelial cells, thereby
   impeding tumor-associated angiogenesis [[101]24]. Targeting the HIF2α
   axis might represent a promising therapeutic strategy to inhibit the
   malignant behavior of endothelial cells, thereby suppressing tumor
   progression [[102]25]. We identified LGALS1 as a potential gene that
   was particularly enriched in the ccRCC endothelial cells.
   Mechanistically, it could affect how these cells behaved in the
   signaling pathways for EMT, oxidative phosphorylation, and apoptosis
   signaling pathways, which warranted further investigation.

   Notably, ccRCC is fundamentally a metabolic disorder, driven by VHL
   inactivation and characterized by marked lipid accumulation and
   mitochondrial dysfunction [[103]26]. Specifically, lipid metabolism in
   ccRCC was typified by augmented uptake from both endogenous synthesis
   and exogenous acquisition, coupled with diminished β-oxidation
   [[104]27]. Consequently, signaling cascades such as mTOR–SREBP and
   PI3K–Akt were hyper-activated, collectively fueling tumor progression
   [[105]28]. Glucose metabolism is reprogrammed, with carbon flux
   shifting from glycolysis to the pentose phosphate pathway [[106]29].
   Previous studies delineated a spectrum of key signaling cascades and
   master regulators governing glucose and lipid metabolism in ccRCC
   progression. Bombelli et al. demonstrated that HIF-1 alpha repressed
   the 36-kDa Annexin A3, thereby diminishing lipid endocytosis and
   driving intracellular lipid accumulation in ccRCC [[107]30]. Moreover,
   MUC1 was discovered to be cooperate with HIF-1 alpha to
   transcriptionally up-regulate LDH-A while repressing LDH-B, which
   stalled glycolytic flux, leading to the intracellular accumulation of
   glycolytic intermediates and a consequent surge in carbon flow through
   the pentose phosphate pathway [[108]31]. Our scRNA-seq profiling
   further implicates LGALS1 in ccRCC metabolic reprogramming, revealing
   that endothelial cells with high LGALS1 exhibit marked up-regulation of
   cholesterol homeostasis, fatty-acid metabolism, and oxidative
   phosphorylation pathways. The role of LGALS1 in ccRCC appeared to hinge
   partly on glucose and lipid metabolism, yet the precise upstream
   triggers and downstream effectors remain to be elucidated.

   Previous research demonstrated that epithelial cells were of critical
   importance in the tumorigenesis and progression of ccRCC. Researchers
   discovered that epithelial cells facilitate the infiltration of immune
   cells and the enrichment of associated inflammatory factors [[109]16].
   According to Xu et al., a high-fat diet and exposure to polystyrene
   microplastics may cause renal epithelial cells’ extracellular matrix
   architecture to be disrupted, which could lead to ROS-mediated ccRCC
   carcinogenesis [[110]32]. A total of 219 malignant epithelial
   cell-related genes (MECRGs) were identified, which enabled the
   establishment of a prognostic model associated with their signatures
   [[111]33]. Following that, the prognostic model was assessed for its
   high accuracy in predicting tumor growth and metastasis as well as
   various biological traits, such as necrotic apoptosis, cuproptosis,
   immune cell infiltration, and immunotherapy response [[112]33].
   Furthermore, the propensity for tumor initiation and metastasis was
   intimately linked to the EMT pathways [[113]34]. Wu and colleagues
   identified that tumor epithelial cells in ccRCC often exhibited a
   mesenchymal transition tendency, which might be mediated by eight
   associated genes [[114]35]. Our study investigated the expression of
   LGALS1 and its related pathways in epithelial cells of ccRCC,
   identifying potential target gene and pathways that modulated the
   malignant behavior of these cells, thereby enhancing our understanding
   of the progression mechanisms of ccRCC.

   The interplay between the immune microenvironment and tumor
   heterogeneity could further influence the efficacy of immunotherapy,
   thereby affecting patient prognosis [[115]36]. A number of cell
   subtypes in ccRCC affected immune cell behavior through a variety of
   biological mechanisms. Endothelial cells, for instance, might be linked
   to immune cell homing, whereas epithelial cells were probably linked to
   immune cell infiltration [[116]16]. Moreover, by up-regulating the
   expression of immune checkpoint molecules like PD-L1 and CTLA-4, the
   EMT signaling pathway might cause immunological suppression and immune
   escape in a variety of malignancies [[117]37]. Furthermore,
   interferon-gamma signaling within cancer cells might concurrently
   enhance PD-L1 expression and decrease TRAILR2 levels. This dual effect
   was identified as possibly leading to T cell exhaustion and
   antagonizing the cytotoxicity of PD-1(+) TRAIL(+) NK/ILC1 cells,
   ultimately impeding the functions of both adaptive and innate immunity
   [[118]38]. Furthermore, the recruitment of CD8(+) cells was reduced in
   the hepatocellular carcinoma model by LGALS1-mediated overexpression of
   PD-L1 in vascular endothelial cells [[119]39]. Previous studies on
   ccRCC also found that the level of LGALS1 was associated with
   predicting the efficacy of immunotherapy targeting PD-L1 [[120]18].
   Future research on the immunoregulatory function of LGALS1 in ccRCC was
   necessary, especially to understand the processes underlying
   immunotherapy based on PD1/PD-L1.

   There were several disadvantages to our work. Firstly, it should be
   noted that the TCGA and UALCAN datasets, which were widely utilized in
   research, predominantly consisted of data from individuals of Caucasian
   descent. On the other hand, the number of patients included in our own
   clinical data, which was centered on the Asian community, was
   relatively limited. In order to more easily identify the correlations
   between LGALS1 levels and tumor grades, stages, and other clinical
   characteristics in the tissues of Chinese ccRCC patients, a large-scale
   observational investigation would be necessary in the future. Moreover,
   the specific mechanisms underlying the elevated LGALS1 levels and the
   potential roles that this gene played in epithelial and endothelial
   cells within ccRCC merited careful selection and in-depth
   investigation. Additionally, particular attention should be paid to its
   relationship with tumor immune behavior and glucose and lipid
   metabolism. This approach could enhance our comprehension of the
   pathogenesis of ccRCC, thereby identifying potential new therapeutic
   targets. Furthermore, the development of potential drugs targeting
   LGALS1 and other key molecules in associated pathways was eagerly
   anticipated, which would provide additional therapeutic strategy
   options to complement current treatments. Lastly, it should be
   emphasized that LGALS1 expression in ccRCC was validated solely by IHC
   chip. therefore, validation in vitro studies were imperative when
   exploring its mechanistic role in future investigations.

Conclusions

   LGALS1 was identified as a risk factor for ccRCC due to its high
   expression in tumors and association with worse survival and poor
   prognosis, supported by TCGA database, CPTAC analysis, and our clinical
   studies. Furthermore, the increased LGALS1 expression in tumors was
   validated by our IHC chip analysis of tumor and paracancerous tissues
   from ccRCC patients. Additionally, scRNA-Seq analysis revealed that
   LGALS1 was predominantly highly expressed in endothelial and epithelial
   cells within tumors, along with the identification of related pathways
   associated with the gene. In summary, LGALS1 was identified as a
   prospective biomarker and therapeutic target for ccRCC.

Acknowledgements