Abstract
Background
Immunotherapy has revolutionized colorectal cancer (CRC) treatment, however, predictive biomarkers for mismatch repair proficient (pMMR) tumors remain scarce. The involvement of the SLC9A9 (solute carrier family 9 member A9) gene in the tumor immune microenvironment is poorly understood.
Methods
We analyzed publicly available gene expression datasets to assess SLC9A9 expression levels in CRC patients. The relationships between SLC9A9 expression and lymphocyte infiltration, specifically CD8 + and CD4 + T cells, were examined using correlation and regression analyses.
Results
Expression of SLC9A9 was notably higher in normal CRC tissues compared to the adjacent tumor tissues, particularly among pMMR CRC patients. Moreover, a significant association was found between SLC9A9 expression and lymphocyte infiltration in these pMMR patients. Interestingly, pMMR patients exhibiting high SLC9A9 expression showed enhanced sensitivity to immunotherapy compared to their counterparts with low SLC9A9 expression.
Conclusions
Our study highlights the prognostic value of SLC9A9 in pMMR CRC and its correlation with lymphocyte infiltration, particularly CD8 + and CD4 + T cells. These results provide a foundation for further investigation into the mechanistic link between SLC9A9 expression and tumor immunity, potentially facilitating the creation of innovative therapeutic approaches for pMMR CRC patients. Collectively, SLC9A9 may serve as a novel immune checkpoint to assess the efficacy of immunotherapy and predict therapeutic outcomes in pMMR CRC patients.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12935-025-04139-5.
Keywords: SLC9A9, Immunotherapy, Colorectal cancer, Biomarker, Immune checkpoint
Introduction
Immunotherapy has shown promise as a therapeutic approach for various cancer types, yielding long-term sustained responses in breast cancer [1], lung cancer and melanoma [2, 3]. However, the application of immunotherapy in CRC patients continue to face substantial challenges. CRC can be divided into two subtypes: MMR-deficient, dMMR (microsatellite instability) and pMMR (microsatellite stability). Programmed death 1 (PD-1) blockade has emerged as a groundbreaking treatment for dMMR CRC, yet its effectiveness in pMMR CRC remains limited [4–6]. dMMR tumors exhibit a high tumor mutational burden (TMB) and marked infiltration of activated CD8 + cytotoxic T lymphocytes (CTLs) and Th1 cells producing IFN-γ [7], which renders dMMR tumors suitable targets for immunotherapy [8]. Conversely, pMMR CRC has long been considered unresponsive to immune checkpoint inhibitors due to its low TMB and lack of immune infiltration, leading to an “immune-resistant” phenotype [9]. Intriguingly, approximately 45% of pMMR tumors exhibited a high immunoscore, suggesting that a subset of pMMR tumors may indeed be responsive to immunotherapy [10]. In line with this observation, it has been shown that 27% (4 out of 15) of pMMR patients exhibited responses following immunotherapy, suggesting that immunotherapy could indeed be effective in a subset of pMMR patients [6]. Moreover, immune “hot” tumors, characterized by comprehensive lymphocyte infiltration, demonstrated enhanced responses to chemotherapy [11, 12]. These findings are further supported by recent clinical trial evidence. In the AtezoTRIBE study, the addition of the anti–PD-L1 antibody atezolizumab to standard FOLFOXIRI plus bevacizumab significantly improved progression-free survival in metastatic CRC patients, including those with pMMR tumors [13]. Updated data from this trial demonstrated a favorable trend in overall survival and response even within the pMMR subgroup, although statistical significance was not reached [14]. Nevertheless, the current knowledge base is inadequate for routinely implementing predictive biomarkers in pMMR patients. Given that over 9 out of 10 CRC cases are pMMR, the identification of novel immune checkpoints for improved prognostic prediction holds considerable urgency.
The tumor immune contexture has been increasingly shown to directly impact the clinical outcomes of cancer patients [15–18]. SLC9A9, also denoted as NHE9, pertains to the Na+/H + exchanger superfamily and serves as a late recycling endosome-localized member. It aids in preserving cation and volume homeostasis by enabling the electroneutral exchange of protons for Na + across membranes. Additionally, previous research has indicated that disorder of functional mutations in SLC9A9 is correlated with autism [19, 20]. There has been downregulation of SLC9A9 in hormone-sensitive prostate cancer [21], and higher expression levels of SLC9A9 have been correlated with unfavorable outcomes in patients with esophageal squamous cell carcinoma who may undergo aggressive surgical intervention [22], as well as in those with glioblastoma [23]. Moreover, elevated SLC9A9 expression has been implicated in CRC patients, and it has even been linked to an increased likelihood of liver metastasis in CRC patients [24]. Previous research has demonstrated that in hypermutated human colon and rectal cancers, SLC9A9 is among the frequently mutated targets [25]. The potential of SLC9A9 genes as biomarkers in CRC remains unclear. During our investigation, we examined a panel of SLC genes in CRC and identified SLC9A9 as a potential new prognostic biomarker for pMMR CRC. But the role of SLC9A9 in cancer has not been elucidated. We found that the expression of SLC9A9 was high express in noncancerous CRC tissues, displaying a correlation with immune checkpoint genes and lymphocyte infiltration. Notably, our study revealed an association between increased SLC9A9 expression and elevated densities of CD8 + and CD4 + T cell tumor-infiltrating lymphocytes. Crucially, pMMR CRC patients with high SLC9A9 expression derive significantly greater benefits from immunotherapy, emphasising the likelihood of SLC9A9 as a new prognostic biomarker in pMMR CRC.
Methods
Patients
In our study, we analyzed data from six public cohorts, including The Cancer Genome Atlas (TCGA) CRC cohort and datasets GSE39582, GSE146771, GSE132465, GSE132257, and GSE144735. These datasets were sourced from the UCSC Xena platform and the Gene Expression Omnibus (GEO) database. The selected cohorts provided extensive gene expression data from CRC samples, integrating both bulk and single-cell RNA sequencing techniques, with three instances of each. The study included a specific focus on 31 CRC patients treated at the Sixth Affiliated Hospital, Sun Yat-Sen University in Guangzhou, China. Additionally, the research examined the outcomes of eight patients with pMMR CRC, who received a combination of immune checkpoint inhibitors (ICI). Criteria for participation excluded individuals with infectious diseases, autoimmune disorders, or multiple primary cancers. Conducted in compliance with the ethical standards of the Declaration of Helsinki, the study also received approval from the Institutional Review Board (IRB) of the Sixth Affiliated Hospital, Sun Yat-sen University.
IHC staining
Immunohistochemical analyses and Hematoxylin and Eosin (H&E) staining were meticulously performed on 5 μm thick sections of CRC tissue from resected recurrent tumors. The study utilized standard immunoperoxidase staining techniques, employing specific antibodies: anti-SLC9A9 (PROTEINTECH, 13718-1-AP, 1:200), anti-CD4 (ZSGB-Bio, ZA-0519), and anti-CD8 (ZSGB-Bio, ZA-0508). The staining process was conducted using a DAKO Autostainer Link 48 slide stainer (Agilent Technologies). The procedure for preparing the tissue sections included deparaffinization with xylene and heat-mediated antigen retrieval in sodium citrate buffer. Subsequently, sections were counterstained with hematoxylin, dehydrated, and mounted under coverslips. Diaminobenzidine staining facilitated visualization. A officially accredited pathologist digitally evaluated the staining to confirm the quality of the tumor tissue. IHC staining was quantitatively assessed following a previously established semi-quantitative protocol [26]. The assessment involved assigning an intensity score to each slide and categorizing the proportion of tumor cells showing positive staining on a scale of 1 to 4. IHC scores were calculated by multiplying the intensity score with the corresponding proportion of the area score. The final score was determined by averaging values from duplicate samples.
Quantification and statistical analysis
In the study, graphical representations displayed the data as mean ± standard deviation (SD), derived from three distinct experiments. Statistical analysis involved the use of a two-tailed Student’s t-test for comparing two groups. For evaluations involving multiple groups, a two-way Analysis of Variance (ANOVA) was applied. The correlation between variables was assessed using either Spearman’s rank correlation coefficient or Pearson’s correlation coefficient, depending on the data distribution. Statistical significance was established at a P-value threshold of less than 0.05.
Results
SLC9A9 demonstrates a strong association with T cell infiltration in CRC, highlighting its potential as a biomarker of immune activity. In our investigation, we aimed to find potential biomarkers for evaluating lymphocyte infiltration and predicting responses to immunotherapy within the tumor immune microenvironment. This is a critical factor in determining the effectiveness of clinical treatments for cancer. Our approach involved analyzing the relation between the expression of all genes and CD8 + T cell infiltration in pMMR CRC patients from TCGA CRC cohort. We employed the TIMER and Xcell algorithms for this analysis.
Our focus was specifically on the top 3500 genes in pMMR CRC, ranked based on the magnitude of their correlation with CD8 + T cell. Through a process of intersecting these genes, SLC9A9 was identified as a particularly notable candidate. This finding underscores the potential significance of SLC9A9 in the context of the tumor immune microenvironment and its implications for immunotherapy in CRC (Fig. 1A, n = 249). In our analysis of the TCGA datasets, we found that while several members of the solute carrier (SLC) gene family showed differential expression between tumor and normal tissues, SLC9A9 was selected as the primary candidate based on its strong and consistent correlation with CD4⁺ and CD8⁺ T cell infiltration in pMMR CRC, as demonstrated in multiple datasets (Fig. 1B, n = 249), highlighting a context-dependent immunoregulatory role. In addition, a moderate correlation with macrophage infiltration was also observed, which may reflect broader immune activation within the tumor microenvironment.
Fig. 1.
A strong correlation between high SLC9A9 expression and increased lymphocyte infiltration in CRC patients. (A) In the TCGA database, a comprehensive analysis of the correlation between all genes and CD8 + T cell infiltration in pMMR colorectal cancer (n = 249) was performed using TIMER and Xcell algorithms. Among the top 3,500 genes, SLC9A9 emerged as a highly ranked candidate. Several other members of the SLC gene family were also present within these 3,500 genes, highlighting their potential significance in the context of CD8 + T cell infiltration in pMMR colorectal cancer. (B) Correlation between SLC family gene expression and CD8⁺ T cell infiltration in pMMR CRC patients from the TCGA cohort (n = 249), based on TIMER analysis. Notably, among the SLC gene family members analyzed, only SLC9A9 demonstrated a robust and significant positive correlation with CD8⁺ T cell infiltration, indicating its potential immune-related regulatory function. (C-F) Expression of SLC9A9 from the TCGA (C, n = 434; D, n = 280) and GSE39582 (E, n = 585; F, n = 459) database. D, F correspond to pMMR CRC samples, and show consistent downregulation of SLC9A9 in tumors compared to normal tissues. (G) Correlation between SLC9A9 expression and CD8⁺ T cell infiltration across multiple cancer types using TCGA pan-cancer data (TIMER analysis). Significant correlations were observed in several cancer histotypes, including CRC, LUAD, LIHC, and KIRC, among others. Abbreviations: BLCA (Bladder urothelial carcinoma), BRCA (Breast invasive carcinoma), CESC (Cervical squamous cell carcinoma and endocervical adenocarcinoma), CHOL (Cholangiocarcinoma), COAD (Colon adenocarcinoma), DLBC (Diffuse large B-cell lymphoma), ESCA (Esophageal carcinoma), GBM (Glioblastoma multiforme), HNSC (Head and neck squamous cell carcinoma), KICH (Kidney chromophobe), KIRC (Kidney renal clear cell carcinoma), KIRP (Kidney renal papillary cell carcinoma), LAML (Acute myeloid leukemia), LGG (Lower grade glioma), LIHC (Liver hepatocellular carcinoma), LUAD (Lung adenocarcinoma), LUSC (Lung squamous cell carcinoma), MESO (Mesothelioma), OV (Ovarian serous cystadenocarcinoma), PAAD (Pancreatic adenocarcinoma), PCPG (Pheochromocytoma and paraganglioma), PRAD (Prostate adenocarcinoma), READ (Rectum adenocarcinoma), SARC (Sarcoma), SKCM (Skin cutaneous melanoma), STAD (Stomach adenocarcinoma), TGCT (Testicular germ cell tumors), THCA (Thyroid carcinoma), THYM (Thymoma), UCEC (Uterine corpus endometrial carcinoma), UCS (Uterine carcinosarcoma), UVM (Uveal melanoma). (H) Correlation between immune check points and SLC9A9 expression in pMMR patients by Spearman’s correlation coefficient. n=249. (I, J) Unsupervised hierarchical clustering of colorectal tumors using ssGSEA scores for immune signatures identifies increasing levels of immune infiltrates. I from TCGA database, n=367, J from GSE39582 database, n=519
We further compared the expression levels of selected SLC genes in tumor tissues and adjacent normal tissues of CRC using TCGA and GSE39582 databases. Notably, SLC9A9 consistently demonstrated significant up-regulation in normal colorectal tissue versus CRC tumor tissue across both databases (Fig. 1C, n = 434; Fig. 1E, n = 585), particularly in pMMR CRC patients (Fig. 1D, n = 280; Fig. 1F, n = 459). While several other SLC family members also showed differential expression between normal and tumor tissues, only SLC9A9 demonstrated both consistent downregulation in tumors and a robust positive correlation with CD8⁺ T cell infiltration in pMMR CRC samples (Fig. 1B, D, F). This immune-relevant expression pattern distinguishes SLC9A9 from other family members and supports its potential as an immunotherapy biomarker. Considering the crucial role of SLC genes in mediating cell-cell recognition via their function as cell surface receptors, our results implicate SLC9A9 as a potential neoantigen generated during carcinogenesis, which may contribute to lymphocyte infiltration. We thus investigated the potential of SLC9A9 as a marker for lymphocyte infiltration by examining its association with tumor-infiltrating lymphocytes in the TCGA dataset. Our findings revealed a substantial positive relation between SLC9A9 expression and CD8⁺ T cell infiltration across diverse cancer types, including but not limited to CRC, where the correlation was among the most pronounced (Fig. 1G).
Additionally, we identified a noteworthy positive association between the expression of SLC9A9 and immune checkpoints, including programmed cell death protein 1 (PD-1), programmed cell death ligand 1 (PD-L1), cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), and T cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domains (TIGIT) in pMMR CRC specimens (Fig. 1H, n = 249). These evidence supports the possible role of SLC9A9 as an emerging biomarker that may augment the effectiveness of established immunotherapeutic approaches. To validate the relation between SLC9A9 expression and tumor-infiltrating lymphocytes, we also analyzed the GSE39582 dataset (Supplementary Fig. 1, n = 444), reinforcing its potential as an immunotherapy biomarker. Moreover, we delved deeper into the association between SLC9A9 expression and the tumor immune microenvironment. Using single-sample gene set enrichment analysis (ssGSEA), we calculated scores for sample based on established colorectal cancer immune signatures. Given the unique immunological biological characteristics of dMMR tumors, we isolated these tumors into a separate cohort. We then conducted unsupervised hierarchical clustering on pMMR tumors (Fig. 1I, n = 367). Our findings uncovered that high SLC9A9 expression was strongly correlated with extensive immune infiltration in these tumors. This association was further confirmed in another CRC dataset (Fig. 1J, n = 519).
To further compare the immunological context of dMMR tumors with pMMR counterparts, we performed a complementary analysis based on the TCGA and GSE39582 datasets (Supplementary Fig. 2).
In dMMR CRC patients, several SLC family members—including SLC9A9—also exhibited a moderate correlation with CD8⁺ T cell infiltration, albeit weaker than that observed in pMMR tumors (Supplementary Fig. 2 A, n = 118; Supplementary Fig. 2B, n = 75). SLC9A9 expression in dMMR tumors is generally lower than in normal tissues, although the magnitude of change is less consistent compared to pMMR (Supplementary Fig. 2 C, n = 132; Supplementary Fig. 2D, n = 77). The correlation between SLC9A9 and immune checkpoint proteins (PD-1, PD-L1, CTLA-4, and TIGIT) in dMMR CRC was also presented (Supplementary Fig. 2E, n = 118; Supplementary Fig. 2 F, n = 75). The correlation coefficients were lower than those in pMMR CRC, suggesting potential differences in checkpoint regulation.
These results highlight fundamental immunological distinctions between dMMR and pMMR tumors. While pMMR tumors with high SLC9A9 expression are associated with increased infiltration of CD4⁺ and CD8⁺ T cells and elevated checkpoint gene engagement, dMMR tumors display a unique immune profile characterized by enhanced presence of mast cells, naïve B cells, and NK cell activity. This divergence reinforces the subtype-specific role of SLC9A9 and supports its relevance primarily in the pMMR immunological context.
Interestingly, although SLC9A9 expression is overall lower in CRC tumor tissues compared to adjacent normal tissues (Fig. 1C, E), further stratified analysis revealed that within pMMR CRCs, SLC9A9 expression is significantly enriched in immune-inflamed tumor subtypes. These subtypes, often termed “immune-hot” tumors, are characterized by dense infiltration of CD4⁺ and CD8⁺ T cells (Figs. 1I and J and 3C and D). This pattern suggests a potential immunoregulatory role for SLC9A9 specifically within the tumor immune microenvironment.
Fig. 3.
SLC9A9 is highly correlated with immune infiltration in CRC tissues. (A) Correlation between SLC9A9 expression and immune-related genes in the TCGA pMMR-CRC cohort. (B) Representative IHC images of SLC9A9, CD4 + T cell and CD8 + T cell expression in CRC tissues from the patients. Red dashed lines indicate tumor regions. (C) Expression of SLC9A9. (D, E) Correlation between lymphocyte infiltration and SLC9A9 in CRC patients by Spearman’s correlation coefficient, n = 60
In our subsequent exploration, we employed single-cell RNA sequencing (scRNA-Seq) to delve into the transcriptional intricacies of individual cells. This approach enabled us to unravel the transcriptional patterns present in malignant colorectal lesions (Fig. 2A). The cluster identities were meticulously established, guided by the expression profiles of well-established marker genes. This rigorous approach allowed for the clear segregation of cells into eight distinct lineages. Based on marker gene expression, we identified eight major populations, including B cells, CD4⁺ T cells, CD8⁺ T cells, innate lymphoid cells (ILCs), myeloid cells, epithelial cells, fibroblasts, and other stromal components, reflecting the diverse immune and stromal composition of the CRC tumor microenvironment (Fig. 2A). Notably, SLC9A9 demonstrates heightened expression in lymphocyte, particularly in CD4 + T cells (Fig. 2B). We further explored this observation using three independent single-cell RNA-seq datasets: GSE132465, GSE144735, and GSE132257. Consistent with our initial findings, GSE132465 and GSE144735 revealed clear enrichment of SLC9A9 in CD4⁺ T cells (Fig. 3C-D). In GSE132257, a comparable trend was observed, with SLC9A9 showing relatively high expression in the overall T cell population (Fig. 2E). However, this dataset lacks sufficient annotation to distinguish CD4⁺ and CD8⁺ T cell subsets, limiting more granular immune profiling. Additionally, none of the three validation datasets (GSE132465, GSE144735, and GSE132257) provided UMAP coordinate data in their supplementary materials, which made it difficult to reconstruct annotated UMAP visualizations with consistent cell-type labels, as we successfully did using the GSE146771 dataset from supplementary file.
Fig. 2.
SLC9A9 is highly expressed in lymphocyte. (A) t-SNE plot showing clustering of eight cell lineages in CRC single-cell RNA-seq data (GSE146771), including B cells, CD4⁺ T cells, CD8⁺ T cells, innate lymphoid cells (ILCs), myeloid cells, epithelial cells, fibroblasts, and stromal cells (left), and the expression of SLC9A9 across these cell types (right). (B-E) Expression of SLC9A9 (TPM) in cell type subpopulations from GSE146771-cohort (B), GSE132465-cohort (C), GSE144735-cohort (D) and GSE132257-cohort (E). (E) GSE132257 lacks T cell subset-level metadata; therefore, only total T cell expression is shown. Notably, none of the three datasets (GSE132465, GSE144735, GSE132257) provided UMAP coordinates data according to the provided cell types, except annotated UMAP plots with GSE146771
Building on the above observations, we next examined whether the relationship between SLC9A9 and immune activity could also be captured at the bulk RNA level. Across the TCGA CRC and GSE39582 transcriptomic dataset, SLC9A9 expression showed a significant positive correlation with key T-cell–associated genes, including CD8A, CD8B, and CD4, reinforcing its linkage to lymphocyte abundance and activation (Fig. 3A; Supplementary Fig. 1 C). These RNA-level correlations, together with our scRNA-seq findings, consistently highlight SLC9A9 as a gene preferentially enriched within T-cell compartments and closely associated with immune infiltration in colorectal cancer.
To corroborate our findings obtained from the public database, we assessed the expression of SLC9A9 in a cohort of 60 CRC patients’ sample (Table 1). These patients were randomly selected from our institutional biobank to illustrate the general expression pattern of SLC9A9 in CRC tissues, without stratification based on MMR status. Remarkably, the expression of SLC9A9 were significantly elevated in the normal tissues compared to the corresponding tumor tissues (p < 0.0001, Fig. 2B-C). To provide a visual illustration of this pattern, we selected two representative cases that exhibited contrasting levels of SLC9A9 expression and T cell infiltration (Fig. 2B).
Table 1.
Patient characteristics (n = 60)
| Patient ID | Stage | Primary tumor site |
|---|---|---|
| 1 | T1N0M0 | Colon |
| 2 | T2N0M0 | Colon |
| 3 | T1N0M0 | Rectum |
| 4 | T2N0M0 | Colon |
| 5 | T1N0M0 | Colon |
| 6 | T2N0M0 | Colon |
| 7 | T3N0M0 | Colon |
| 8 | T4aN0M0 | Colon |
| 9 | T4bN0M0 | Rectum |
| 10 | T3N0M0 | Colon |
| 11 | T4aN0M0 | Colon |
| 12 | T4bN0M0 | Colon |
| 13 | T3N0M0 | Colon |
| 14 | T4aN0M0 | Colon |
| 15 | T4bN0M0 | Colon |
| 16 | T3N0M0 | Colon |
| 17 | T4aN0M0 | Rectum |
| 18 | T4bN0M0 | Colon |
| 19 | T3N0M0 | Rectum |
| 20 | T4aN0M0 | Rectum |
| 21 | T4bN0M0 | Colon |
| 22 | T3N0M0 | Colon |
| 23 | T4aN0M0 | Rectum |
| 24 | T4bN0M0 | Rectum |
| 25 | T2N1M0 | Rectum |
| 26 | T3N1aM0 | Colon |
| 27 | T3N1bM0 | Colon |
| 28 | T3N2aM0 | Rectum |
| 29 | T3N2bM0 | Colon |
| 30 | T4aN1M0 | Colon |
| 31 | T4aN2M0 | Rectum |
| 32 | T2N1M0 | Colon |
| 33 | T3N1aM0 | Rectum |
| 34 | T3N1bM0 | Rectum |
| 35 | T3N2aM0 | Rectum |
| 36 | T3N2bM0 | Colon |
| 37 | T4aN1M0 | Rectum |
| 38 | T4aN2M0 | Colon |
| 39 | T2N1M0 | Rectum |
| 40 | T3N1aM0 | Colon |
| 41 | T3N1bM0 | Rectum |
| 42 | T3N2aM0 | Rectum |
| 43 | T3N2bM0 | Colon |
| 44 | T4aN1M0 | Colon |
| 45 | T4aN2M0 | Colon |
| 46 | T2N1M0 | Colon |
| 47 | T3N1aM0 | Rectum |
| 48 | T3N1bM0 | Colon |
| 49 | T3N0M0 | Colon |
| 50 | T3N1M0 | Colon |
| 51 | T3N2M0 | Rectum |
| 52 | T4aN1M0 | Rectum |
| 53 | T4bN2M0 | Rectum |
| 54 | T3N0M0 | Rectum |
| 55 | T3N1M0 | Colon |
| 56 | T3N2M0 | Rectum |
| 57 | T4aN1M0 | Rectum |
| 58 | T4bN2M0 | Colon |
| 59 | T3N0M0 | Rectum |
| 60 | T3N1M0 | Colon |
In light of our analysis of public databases, we postulated that SLC9A9 may be implicated in the infiltration of CD4 + and CD8 + T cells within CRC. To test this hypothesis further, we furthermore performed IHC staining for CD4 + T cells, and CD8 + T cells on CRC sample obtained from the same cohort of CRC patients.
As reported in previous research [6], the infiltration of CD4 + and CD8 + T lymphocytes was observed to vary extensively across various CRC tissue samples. We illustrate this variability through representative cases, highlighting the heterogeneous spatial distribution of T cells within tumor regions (Fig. 3A). Intriguingly, our IHC results revealed a robust positive association between the expression of SLC9A9 and the infiltration of tumor-infiltrating lymphocytes, encompassing both CD4 + and CD8 + T cells, in CRC tumors (Fig. 3C, D).
Elevated SLC9A9 expression correlates with enhanced effect of immunotherapy
Considering the robust correlation between SLC9A9 expression and immune infiltration in CRC tumors, we explored the potential of SLC9A9 as a biomarker to forecast therapeutic response in CRC patients. We analyzed a cohort of 8 pMMR CRC patients who underwent immunotherapy treatment followed by complete surgical resection of the primary tumor. The administered immune checkpoint inhibitors included sintilimab, camrelizumab, nivolumab, and pembrolizumab, in combination with chemotherapy regimens such as FOLFOX or XELOX (Table 2). In our pathological analysis of resected tumors pre- and post-immune checkpoint inhibitor (ICI) therapy, we found that in patients who responded to immunotherapy (immuno-sensitive), surgical specimens exhibited a marked reduction in tumor burden, characterized by macroscopic shrinkage of the tumor mass. Furthermore, extensive necrotic tissue was observed within the tumor bed, as confirmed by pathological examination, indicating a strong therapeutic response at the tissue level (Fig. 4A, Ⅰ-Ⅺ). In contrast, immuno-resistant tumors displayed no response or even a negative response to ICI treatment (Fig. 4A, a-l). We subsequently performed IHC to assess SLC9A9 expression, CD4 + T cells, and CD8 + T cells in the pathological tumor samples. Intriguingly, high expression of the SLC9A9 gene was noted in resected tumor specimens demonstrating immune sensitivity, which was concurrently associated with substantial infiltration of both CD4 + and CD8 + T lymphocytes (Fig. 4A). The effective infiltration of these lymphocyte into tumors contributed to the successful outcomes of most immunotherapies [27]. Furthermore, SLC9A9 levels were significantly higher in immuno-sensitive tumors compared to immuno-resistant tumors before ICI treatment (Fig. 4B-D). These findings suggest that SLC9A9 may serve as a promising biomarker for predicting response to immunotherapy in pMMR CRC patients.
Table 2.
Patient Characteristics of 8 MMR-proficient CRC patients treat with ICI
| Characteristics | Patient 1 | Patient 2 | Patient 3 | Patient 4 | Patient 5 | Patient 6 | Patient 7 | Patient 8 | |
|---|---|---|---|---|---|---|---|---|---|
| Primary tumor site | rectum | Rectum | Rectum | Rectum | Rectum | Colon | Colon | Rectum | |
| Stage | T2N0M0 | T3N0M0 | T3N0M0 | T3N1M0 | T4aN1aM0 | T3N0M0 | T4N2M0 | T4N2M0 | |
| Metastaic site | NO | NO | NO | NO | NO | NO | NO | NO | |
| Treatment before ICI | |||||||||
| Systematic (response ■) | none | FOLFOX + radiotherapy (PR) | FOLFOX (PR) | none | FOLFOX (PD) | XELOX (PD) | none | FOLFOX (PR) | |
| Surgery | none | none | none | none | hartmann | right hemicolectomy | none | none | |
| ICI combined treatment | |||||||||
| Regimen | sintilimab + Avastin + FOLFOX | FOLFOX + sintilimab | FOLFOX + sintilimab | sintilimab + FOLFOX | camrelizumab | nivolumab | FOLFOX + pembrolizumab | FOLFOX + sintilimab | |
| Systematic (response■) | PR | PR | PR | PD | PD | PD | PD | PR | |
| Radiological response | PR | PR | PR | PD | ★ | ★ | PD | PR | |
| Surgical treatment (after ICI) | Dixon | TaTME | Dixon | Dixon | ★ | ★ | hartmann | Dixon | |
| Pathological response | |||||||||
| Primary tumor | PR | PR | PR | PD | ★ | ★ | PD | PR | |
| Reginal lymph nodes | PR | PR | PR | PD | ★ | ★ | PD | PR | |
| Postoperative treatment | NONE | NONE | FOLFOX | FOLFOX | camrelizumab | nivolumab | pembrolizumab | sintilimab | |
| TRG score (NCCN Guidelines) | 1 | 1 | 1 | 2 | ★ | ★ | 2 | 1 |
■ Assessed by the Response Evaluation Criteria in Solid Tumors 1.1 criteria
★ Colectomy was conducted before ICI combined treatment
FOLFOX, fluorouracil + oxaliplatin; XELOX, capecitabine + oxaliplatin; ICI, immune checkpoint inhibitor; pMMR, mismatch repair (MMR) proficient; dMMR, mismatch-repair (MMR) deficient; PD, progressive disease; PR, partial response
Fig. 4.
High SLC9A9 expression is benefit from immunotherapy. (A) Radiological and pathological response to FOLFOX plus sintilimab in a patient with stage T4N2M0 (Case 1) and FOLFOX plus pembrolizumab in a patient with stage T4N2M0 (Case 2). Radiographic imaging shows the tumor in rectum (Ⅰ, a) at initial diagnoses. A notable tumor regression could be seen in primary tumor from Case 1 (Ⅱ). But there was no response in Case 2 after ICI (b). Primary tumor was observed using colonoscopy (Ⅲ, c) at initial diagnosis and after ICI treatment (d). H&E staining shows primary tumor at initial diagnosis (Ⅳ, e) and pathological response after ICI treatment (V, f). Fibrosis and an infiltration with viable density of many lymphocytes (V, arrowheads) can be found, which cannot be found in case 2 (f). IHC staining showed SLC9A9, CD4 + T cells and CD8 + T cells expression with pretreatment tumor samples (Ⅵ, Ⅷ, Ⅹ for Case 1; g, i, k for Case 2) and posttreatment tumor tissues (Ⅶ, Ⅸ, Ⅺ for Case 1; h, j, l for Case 2). Gross and histological changes in CRC patients following immune checkpoint inhibitor treatment. “Marked reduction in tumor burden” refers to visible tumor shrinkage in resected specimens. “Extensive necrotic tissue” refers to large necrotic areas within the tumor bed, confirmed by pathological assessment. These features were more frequently observed in patients who responded to treatment (B- D) Quantification of the score for CD4 + T cells, CD8 + T cells and SL9A9 staining before treatment in CRC tissue from immunotherapy sensitive versus immunotherapy resistance assessed by IHC assay, n = 8
Discussion
CRC ranks among the most common cancer types globally and is a major contributor to cancer-related mortality [28]. Immunotherapy has proven effective in treating various cancers; however, its use in CRC has primarily been restricted to patients with dMMR tumors. Intriguingly, a substantial fraction of pMMR tumors displayed elevated levels of tumor-infiltrating lymphocytes (TILs) within the inter-tumoral region, suggesting potential sensitivity to immunotherapy [11, 12]. Although clinical practice employs biomarkers such as carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA199), and carbohydrate antigen 125 (CA125) to evaluate tumor cell proliferation and recurrence, there remains a dearth of reliable biomarkers capable of predicting lymphocyte infiltration. Consequently, the identification of new predictive markers for pMMR tumors, which could potentially benefit from immunotherapy, is of critical importance.
Previous research has highlighted the significance of the tumor immune contexture in predicting the prognosis of CRC patients [15–18]. Immunotherapy targeting immune checkpoints, such as the programmed cell death protein 1 (PD-1) and its ligand PD-L1, has emerged as an established treatment option for CRC patients exhibiting dMMR or high microsatellite instability (MSI-H) [29]. These therapies are now being utilized to treat an increasingly broad spectrum of malignancies [30–32]. Consequently, there is a growing interest in exploring alternative immunotherapeutic strategies, such as cancer vaccines and adoptive cell transfer [31]. Recent investigations have revealed a significant association between dMMR status and immune infiltration in CRC. Specifically, a high immunoscore is observed in approximately 45% of pMMR and 65% of dMMR CRC cases, while a low immunoscore is detected in 55% of pMMR and 35% of dMMR CRC cases. These data indicate that a proportion of dMMR CRC may not successfully initiate antitumor immune responses, whereas certain pMMR CRC cases might conversely exhibit robust immune activation [10]. Recent studies have provided further support for the potential efficacy of immunotherapy in pMMR CRC [13, 14]. For instance, the utilization of a neoadjuvant treatment approach consisting of ipilimumab and nivolumab demonstrated a pathological response rate of 27% in patients with early-stage mismatch repair pMMR (CRC). This evidence suggests that pMMR CRC is not an immunologically inert landscape and may be amenable to immunotherapeutic interventions [6]. In addition to dMMR and pMMR, we hypothesize that the SLC9A9 may serve as another possible biomarker for predicting responsiveness to immunotherapy, given its strongly positive relationship with lymphocyte. Our study suggests that SLC9A9 could be a promising novel biomarker for promoting the expression of lymphocyte, particularly CD4 + and CD8 + T cells, which are linked to improved survival rates in pMMR CRC patients [32, 33]. As such, SLC9A9 may represent a valuable indicator for predicting immunotherapy response, contributing to enhanced immune function. The complex interactions between tumor and lymphocyte within the tumor microenvironment (TME) ultimately determine clinical outcomes in CRC patients. While information specific to CRC are lacking, studies have shown that increased infiltration of T cells (TCI) in several other tumor types is correlated with a higher probability of response to immunotherapy, underscoring the importance of lymphocyte infiltration in predicting therapeutic outcomes [27, 34].
Over the past few decades, numerous inhibitory immunoreceptors, including PD-1, PD-L1, CTLA-4, LAG3, HAVCR2, TIGIT, CD69, and CD40, have been identified and investigated in the context of cancer. Often referred to as “immune checkpoints,” these receptors serve as regulators of immune responses. Co-evolving with stimulatory immunoreceptors throughout evolution, immune checkpoints have origins traceable back to fish [35]. The signaling motifs of immunoreceptors, which employ monotyrosine, such as the immunoreceptor tyrosine-based inhibitory motif (ITIM) and immunoreceptor tyrosine-based switch motif (ITSM), play a crucial part in transmitting inhibitory signals. Antibodies that impede ligand-receptor interactions can effectively target these surface molecules. Anti-PD-1/PD-L1 treatment, representing the most potent form of immune checkpoint blockade therapy and a class of anticancer immunotherapies, has received regulatory approval for managing many cancer types including blood, skin, lung, liver, bladder, and kidney malignancies [36]. The efficacy of immune checkpoint blockade therapy (ICBT) has been demonstrated to yield more durable responses compared with chemotherapy or targeted therapies, potentially due to the immunological memory capabilities. However, emerging clinical data reveal limitations and adverse effects associated with ICBT. A primary constraint is the low response rate observed across most cancer types, with rates ranging between 10% and 30% [36]. In particular, pMMR CRC exhibits minimal to reduced efficacy with anti-PD-1/PD-L1 treatment [37]. In our research, we revealed the pivotal role of SLC9A9 in modulating the immune microenvironment of CRC. Although SLC9A9 expression was elevated in adjacent normal tissues, high expression within tumors was associated with a more favorable immune landscape, potentially enhancing CD4⁺ and CD8⁺ T cell–mediated tumor elimination. Interestingly, our analysis also identified SLC25A10, another solute carrier family member, as exhibiting an opposing infiltration profile—being more enriched in immune-cold tumors with reduced T cell infiltration. SLC25A10 encodes a mitochondrial dicarboxylate transporter involved in redox and metabolic regulation, and may contribute to an immunosuppressive tumor milieu via altered mitochondrial metabolism or reactive oxygen species (ROS) modulation [38, 39].
Our findings suggest that SLC9A9 may serve as a novel immunologically relevant biomarker in CRC, particularly in the pMMR subgroup. Previous studies have implicated SLC9A9 in endosomal pH regulation, which can influence antigen presentation and T cell activation. Its enrichment in immune-inflamed tumors and correlation with T cell infiltration highlight its role in promoting a permissive immune microenvironment [40, 41].
These observations warrant further mechanistic investigation and support the potential of SLC9A9 as part of multi-marker panels for immune subtyping or treatment stratification in CRC. SLC9A9 may also contribute to the development of next-generation immunotherapies or the optimization of current immunotherapeutic protocols. However, further validation in independent cohorts and functional studies will be necessary before clinical application.
Here, we demonstrated that high SLC9A9 expression facilitated an increase in lymphocyte within CRC, thereby promoting immune-mediated tumor elimination. SLC9A9 served as an independent predictor of improved prognosis, offering insights into potential therapeutic strategies for PMMR CRC. Consequently, SLC9A9 emerges as a promising prognostic biomarker and target for immunotherapies in pMMR CRC.
Supplementary Information
Acknowledgements
Supported by National Key Clinical Discipline.
Author contributions
Yu-cheng Xu, De-zheng Lin, Yi-ming Shi and Yu-fang Liang contributed to study concept and design, acquisition, analysis, interpretation of data and drafting of the manuscript. Chong Wang, Ri-yun Liu, Huan-miao Zhan and Xiao-chuan Chen contributed to data collections and manuscript review. Yi-yun Xu and Xi Chen supervised the study. All authors read and approved the final manuscript.
Funding
This work was supported by Guangdong Basic and Applied Basic Research Foundation (2022A1515111094) and the Fundamental Research Funds for the Central Universities, Sun Yat-sen University.
Data availability
The Cancer Genome Atlas (TCGA) pan-cancer data were obtained from UCSC Xena (https://xenabrowser.net/datapages/?cohort=TCGA%20Pan-Cancer%20(PANCAN)), including gene expression RNAseq (https://xenabrowser.net/datapages/?dataset=tcga_RSEM_gene_fpkm&host=https%3 A%2 F%2Ftoil.xenahubs.net&removeHub=https%3 A%2 F%2Fxena.treehouse.gi.ucsc.edu%3A443),phenotype - sample type and primary disease (https://xenabrowser.net/datapages/?dataset=TCGA_phenotype_denseDataOnlyDownload.tsv&host=https%3 A%2 F%2Fpancanatlas.xenahubs.net&removeHub=https%3 A%2 F%2Fxena.treehouse.gi.ucsc.edu%3A443) and phenotype - Curated clinical data(https://xenabrowser.net/datapages/?dataset=Survival_SupplementalTable_S1_20171025_xena_sp&host=https%3 A%2 F%2Fpancanatlas.xenahubs.net&removeHub=https%3 A%2 F%2Fxena.treehouse.gi.ucsc.edu%3A443).TMB data in Colorectal Adenocarcinoma (TCGA, PanCancer Atlas) were obtained from cBioPortal (https://www.cbioportal.org/study/clinicalData? id=coadread_tcga).MSI status data of The Cancer Genome Atlas (TCGA)-COAD and TCGA-READ datasets from the Genomic Data Commons were obtained from using the R package “TCGAbiolinks”.Other RNA-seq data reported in this article has been deposited in NCBI’s Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE39582.Single cell RNA sequencing (scRNA-seq) data of colorectal cancer reported in this article has been deposited in NCBI’s Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE132465, GSE146771, GSE132257, GSE144735.
Declarations
Ethics approval
This study was approved by the ethics committees of the Sixth Affiliated Hospital of Sun Yat-sen University.
Consent for publication
All authors approve to publish this paper.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yu-cheng Xu, Xiao-chuan Chen and Huan-miao Zhan contributed equally to this work.
Contributor Information
Yi-ming Shi, Email: shiym5@mail.sysu.edu.cn.
Yi-yun Xu, Email: xuyy@gdmu.edu.cn.
Xi Chen, Email: key_cx@163.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The Cancer Genome Atlas (TCGA) pan-cancer data were obtained from UCSC Xena (https://xenabrowser.net/datapages/?cohort=TCGA%20Pan-Cancer%20(PANCAN)), including gene expression RNAseq (https://xenabrowser.net/datapages/?dataset=tcga_RSEM_gene_fpkm&host=https%3 A%2 F%2Ftoil.xenahubs.net&removeHub=https%3 A%2 F%2Fxena.treehouse.gi.ucsc.edu%3A443),phenotype - sample type and primary disease (https://xenabrowser.net/datapages/?dataset=TCGA_phenotype_denseDataOnlyDownload.tsv&host=https%3 A%2 F%2Fpancanatlas.xenahubs.net&removeHub=https%3 A%2 F%2Fxena.treehouse.gi.ucsc.edu%3A443) and phenotype - Curated clinical data(https://xenabrowser.net/datapages/?dataset=Survival_SupplementalTable_S1_20171025_xena_sp&host=https%3 A%2 F%2Fpancanatlas.xenahubs.net&removeHub=https%3 A%2 F%2Fxena.treehouse.gi.ucsc.edu%3A443).TMB data in Colorectal Adenocarcinoma (TCGA, PanCancer Atlas) were obtained from cBioPortal (https://www.cbioportal.org/study/clinicalData? id=coadread_tcga).MSI status data of The Cancer Genome Atlas (TCGA)-COAD and TCGA-READ datasets from the Genomic Data Commons were obtained from using the R package “TCGAbiolinks”.Other RNA-seq data reported in this article has been deposited in NCBI’s Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE39582.Single cell RNA sequencing (scRNA-seq) data of colorectal cancer reported in this article has been deposited in NCBI’s Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE132465, GSE146771, GSE132257, GSE144735.