Analysis of SLC genes alternative splicing identifies the SLC7A6 RI isoform as a therapeutic target for colorectal cancer

Abstract

Alternative splicing (AS), a crucial mechanism in post‐transcriptional regulation, has been implicated in diverse cancer processes. Several splicing variants of solute carrier (SLC) transporters reportedly play pivotal roles in tumorigenesis and tumor development. However, an in‐depth analysis of AS landscapes of SLCs in colon adenocarcinoma (COAD) is lacking. Herein, we analyzed data from The Cancer Genome Atlas and identified 1215 AS events across 243 SLC genes, including 109 differentially expressed AS (DEAS) events involving 62 SLC genes in COAD. Differentially spliced SLCs were enriched in biological processes, including transmembrane transporter activity, transporter activity, ferroptosis, and choline metabolism. In patients with COAD, tumor tissues exhibited higher expression of longer mitochondrial carrier SLC25A16 isoforms than adjacent normal tissues, consistent with bioinformatics analysis. Protein‐coding sequences and transmembrane helices of survival‐related DEAS were predicted, revealing that shifts in splicing sites altered the number and structure of their transmembrane proteins. We developed a prognostic risk model based on the screened 6‐SLC‐AS (SLC7A6_RI_37208 (SLC7A6‐RI), SLC11A2_AP_21724, SLC2A8_ES_87631, SLC35B1_AA_42317, SLC39A11_AD_43204, and SLC7A8_AP_26712). Knockdown of the intronic region of SLC7A6‐RI isoform enhanced colon cancer cell proliferation. In vivo, knockdown of the intronic region of SLC7A6‐RI isoform enhanced tumor growth in colon cancer. Mechanistically, si‐SLC7A6‐RI isoform exerted oncogenic effects by activating the PI3K‐Akt–mTOR signaling pathway and promoting cell proliferation, evidenced by increased expression of key regulators Phosphorylated Mammalian Target of Rapamycin (p‐mTOR) and a cell proliferation marker Proliferating Cell Nuclear Antigen (PCNA) using western blotting. Our study elucidated SLC‐AS in COAD, highlighting its potential as a prognostic and therapeutic target and emphasizing the suppressive influence of SLC7A6‐RI in colon cancer progression.

INTRODUCTION

Alternative splicing (AS) is a vital mechanism for regulating gene expression after transcription and plays a crucial role in over 90% of multi-exon genes in humans.^1,2 AS has the potential to generate diversity at both RNA and protein levels, markedly enriching the cellular protein reservoir and contributing to the temporal and spatial diversification of biological functions.^3,4 It is widely recognized that abnormal AS is a characteristic feature of cancer. This phenomenon affects several crucial aspects, including the cell cycle, apoptosis, proliferation, metabolism, genomic stability, motility, invasion, epithelial-mesenchymal transition, angiogenesis, immune environment, and drug resistance in tumors.^5–7

Colorectal cancer (CRC), one of the most commonly diagnosed cancers of the digestive tract, is the third leading cause of cancer-related deaths worldwide.⁸ Growing evidence suggests the occurrence of alterations in AS in CRC. For example, Chen et al.⁹ reported that the serine and arginine rich splicing factor 1 (SRSF1) can prevent DNA damage and promote tumorigenesis by regulating DBF4B-CDC7 kinase regulatory subunit (DBF4B) pre-mRNA splicing in colon cancer. Zhou et al.¹⁰ identified that knockdown of the BCL2 associated transcription factor 1 (BCLAF1) protein isoform resulting from exon5a inclusion could suppress human colon cancer cell growth and that its overexpression enhanced tumorigenic potential. Sakuma et al.¹¹ showed that knockdown of heterogeneous nuclear ribonucleoprotein L like (HNRNPLL) could modulate AS of CD44, which is responsible for the enhanced invasion activity of CRC cells.

Currently, there is growing interest in understanding the role of membrane transporters in both benign and malignant diseases in humans. Solute carriers (SLCs) are a group of transporters that facilitate the movement of inorganic and organic solutes across cellular membranes.¹² The SLC superfamily comprises 65 subfamilies, with more than 400 SLC transporters identified to date.¹³ More than 80 SLC transporters have been implicated in a range of complex, multifactorial diseases, including Alzheimer's disease, epilepsy, schizophrenia, and various carcinomas.^14–17 Recently, SLC25A22 was found to play a role in promoting the proliferation and metastasis of gallbladder cancer cells by activating the MAPK/ERK pathway.¹⁸ In addition, Cheng et al.¹⁹ demonstrated that the zinc transporter SLC39A13/ZIP13 facilitates human ovarian cancer cell metastasis by activating the Src/FAK signaling pathway. According to Jiang, SLC3A1 promotes cysteine uptake and determines the cellular response to the antioxidant N-acetylcysteine, indicating that SLC3A1 is a potential therapeutic target for breast cancer.²⁰ SLC22A18 inhibition reportedly reduces free fatty acid release from lipid droplets, hindering the lysosomal/autophagy degradation pathway and decreasing the invasive behavior of HepG2 cells by lowering IGFBP-1 expression.²¹

Over the years, alternatively spliced variants of genes encoding SLC transporters have been increasingly identified to influence tumor growth and progression. For example, SLC39A14 was found to have two splice isoforms, SLC39A14-4A and SLC39A14-4B, which comprise a mutually exclusive exon 4; the expression of SLC39A14-4B mRNA was markedly increased in colonic adenoma and CRC tissues when compared with that of SLC39A14-4A mRNA.²² Furthermore, Sveen et al.²³ reported that SLC39A14-4B could be used as a marker to distinguish CRC from other colon-related pathological conditions.

Although there has been some progress in investigations on disordered AS of SLC genes in CRC, the role of abnormal splicing events related to SLC family genes in CRC development and progression remains unclear. A comprehensive investigation into the AS of the SLC superfamily could lead to the discovery of valuable biomarkers for CRC. In the present study, we conducted a comprehensive and in-depth analysis of the AS landscape of SLC genes in colon adenocarcinoma (COAD) using The Cancer Genome Atlas (TCGA). Importantly, we demonstrated that SLC7A6 intron-retained (SLC7A6-RI) knockdown exerted oncogenic effects by activating the PI3K-Akt–mTOR signaling pathway and promoting HCT116 cell proliferation. Taken together, our in-depth analysis of the AS Landscape in SLC genes identified SLC7A6-RI as a promising therapeutic target for CRC.

MATERIALS AND METHODS

Data acquisition

We obtained comprehensive transcriptome data and survival information for COAD from TCGA, including 41 normal and 473 tumor samples. The percentage spliced-in (PSI) values, indicating the probability of alternative splicing events, were calculated using SpliceSeq. These values can be downloaded from the TCGA SpliceSeq website.²⁴ PSI is determined by the formula PSI = IR/(IR + ER), where IR and ER represent inclusion and exclusion reads, respectively.^25,26

Identification of DEGs

Gene expression data of SLCs were included in the differentially expressed gene (DEG) analysis. DEGs were screened using the edgeR package based on a threshold (|logFC| >1, P value <0.05). The heatmap and volcano plot represent the DEG expression.

UpSet plot

An UpSet plot was created using the UpSet R package. This plot effectively demonstrates the intersections and distributions of all AS event types, offering a clearer quantitative representation than traditional Venn diagrams.

Identification of DEAS events

To investigate differentially expressed AS (DEAS) of SLC genes between COAD and adjacent normal tissues, we identified SLC-AS events with significant differential expression (P < 0.05, ΔPSI ≥0.1) from the OncoSplicing database.^27,28 The expression profiles of these events are shown using heat maps and box plots.

Prognosis-associated AS events

We conducted multiple survival analyses using the OncoSplicing database, including overall survival (OS), progression-free survival (PFI), disease-free survival (DFI), and disease-specific survival (DSS). Cox PH regression evaluated the hazard ratio between two groups by dichotomizing PSI values, while the log-rank test assessed significance. AS events required a sample size >30, survival events >5, and group sizes >10. An AS event was considered a significant SASE if the log-rank test P value was <0.05.

Forecast model analysis establishment

We used least absolute shrinkage and selection operator (LASSO) regression with the glmnet R package to select important prognosis-associated AS markers. These selected AS events were then included in a multivariate Cox regression analysis. The coefficients from this analysis were used as coefficients in the risk forecast model. Risk scores were calculated using the following formula:$$$ \mathrm{risk}\ \mathrm{score}=\sum \limits_{i=1}^n{\mathrm{Coef}}_i\times \mathrm{PSI} $$$

Kaplan–Meier (KM) curves were generated to assess the prognostic models' ability to distinguish between patients with longer and shorter OS. The “survivalROC” package was used to evaluate the AS signature's specificity and sensitivity through Receiver Operating Characteristic (ROC) curves, calculating the Area Under the Curve (AUC) for each sample. Additionally, risk score distribution, survival status, and PSI value heat maps were used to measure the AS model's predictive effectiveness.

Evaluation of the independence and correlation with clinical features

Cox regression analyses were used to assess if the risk-prognostic signature independently predicted COAD patient outcomes. The limma and ggpubr R packages analyzed and visualized correlations between risk score and clinical features (age, sex, stage, T, N, and M stages). P values <0.05 were considered significant.

GSEA for AS events

Using the median cutoff risk score of the AS signature, TCGA COAD patients were divided into low- and high-risk subgroups. Gene set enrichment analysis (GSEA) was conducted to explore pathways in the AS event-based predictive model, comparing high- and low-risk groups using the JAVA program with annotated gene sets from “Hallmark gene set,” “C2: KEGG pathways,” and “C6: oncogenic signature gene sets” from MsigDB3. Gene sets with nominal P values <0.05 after 1000 permutations were considered significantly enriched.

Validation in human samples

Four pairs of tumors and normal tissues from COAD patients were obtained with informed consent at Shenzhen People's Hospital. Tissues were stored in liquid nitrogen until RNA extraction using RNeasy Kits. Total RNA was treated with DNase I and reverse-transcribed to cDNA. Reverse transcription-quantitative PCR was performed with 2× Green PCR Mix and splicing-specific primers (Table S1). Splicing-specific products were identified using agarose gel electrophoresis.

Cell viability assay

HCT116 cells were seeded in 96-well plates at 6000 cells per well. After 24 h, cells were treated with si-NC, si-SLC7A6-RI-1, and si-SLC7A6-RI-2 for 48 h. Cell viability was then assessed using a CCK8 kit. Cells were rinsed with Phosphate-Buffered Saline (PBS), incubated in serum-free Dulbecco's Modified Eagle's Medium (DMEM) with 10% CCK8 for 2 h, and absorbance was measured at 450 nm using a microplate reader.

Detection of colony formation

In brief, HCT116 cells were seeded in a six-well plate at 1000 cells/well, and 3 mL of cell culture medium was added to each well. Three duplicate wells were analyzed for each sample. The cells were cultured in a cell culture incubator for 14 days. Subsequently, the cells were washed with PBS three times and fixed with paraformaldehyde for 30 min. The cells were stained with crystal violet for 30 min and rinsed with PBS three times for 5 min each. The number of colonies formed by the cells was counted.

Cell proliferation assay

For the cell proliferation assay, apoptosis was measured using the EdU Cell Proliferation Assay Kit (RiBo Bio). Cells were cultured with 5 μM EdU for 2 h, washed with PBS, fixed with 4% paraformaldehyde, and permeabilized with 0.5% Triton-100. After washing, cells were incubated with 1× Apollo® reaction cocktail for 30 min and stained with Hoechst 33342 for 30 min. EdU-positive cells were counted under a fluorescence microscope.

Establishment of tumor-bearing nude mice

Briefly, cells were cultured until 80% confluency, then digested and centrifuged. They were washed with PBS, resuspended in serum-free medium at a density of 4 × 10⁷/mL, and mixed with an equal volume of Matrigel on ice. For tumor formation, 200 μL of this cell suspension was injected into the disinfected subcutaneous armpit of nude mice. Mice were kept under strict conditions and given siRNA every 3 days. Tumor size was measured, and the experiment lasted 2 weeks. Tumor volume was calculated using the formula: (width)² × length/2. Based on the measurement results, the relative tumor volume (RTV) was calculated using the formula RTV = Vt/V0. Here, V0 is the tumor volume measured at the time of initial dosing (i.e., on day 0), and Vt is the tumor volume measured at the last measurement. The relative tumor growth rate T/C (%) is calculated using the formula: T/C = TRTV/CRTV × 100% (TRTV, RTV of the treatment group; CRTV, RTV of the control group).

Western blot assay

Whole-cell lysates were prepared by lysing cells on ice with RIPA buffer containing a mixture of protease and phosphatase inhibitors. Protein concentrations were assayed using a BCA protein quantification kit, adhering to the protocol outlined by the manufacturer. For electrophoresis, 30 μg of protein per well was loaded onto Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis (SDS-PAGE) gels, transferred to Polyvinylidene Fluoride membranes, and subjected to incubation with primary and secondary antibodies. The immunoreactive bands were detected and captured utilizing the Invitrogen iBright CL1000 imaging system (Thermo Fisher Scientific). Band grayscale values were quantified employing ImageJ software ().

Statistical analysis

Data analyses were performed using R 4.1.2 and GraphPad Prism 9. Two-tailed P values <0.05 were considered significant. Data are shown as mean ± standard deviation from at least three experiments, analyzed with a paired Student's t-test and two-way ANOVA. Pearson's correlation test created a scatter diagram in R. Significance was set at |Cor| > 0.5.

RESULTS

Overview of the AS event profile of the SLC gene family in COAD

Herein, we analyzed 443 patients and identified 1215 AS events associated with 243 SLC family genes. These AS events were categorized into different types as follows: 538 exon skipping (ES)-type events involving 174 SLC genes, 260 AP-type events involving 105 SLC genes, 182 AT-type events involving 79 SLC genes, 92 AA-type events involving 68 SLC genes, 74 retained intron (RI)-type events involving 48 SLC genes, 64 AD-type events involving 44 SLC genes, and five mutually exclusive exon (ME)-type events involving five SLC genes (Figure 1A,B and Table S2). Notably, a single gene can exhibit multiple AS patterns. An UpSet plot was used to visualize detailed information regarding the specific AS types of genes. Among these, ES events were the most prevalent, accounting for nearly half of all AS events (Figure 1A).

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Subsequently, the KEGG analysis identified 1215 SLCs with AS events mainly involved in ferroptosis, choline metabolism in cancer, insulin resistance, protein digestion, mineral absorption, bile secretion, and vitamin digestion (Figure 1C). GO analysis was performed on 1215 SLCs with AS events. The representative biological processes included transmembrane transporter activity, transporter activity, and ion transporters (Figure 1D).

DEAS events of SLC family genes in COAD

In COAD, 109 DEAS events involving 62 SLC genes were identified: 30 ES events (28 genes), 46 AP events (25 genes), 14 AT events (seven genes), five AA events (four genes), four RI events (four genes), nine AD events (seven genes), and one ME event (SLC39A14) (Figure 2A and Table S3). Among these, 61 were upregulated, and 48 were downregulated. Notably, SLC17A4 and SLCO4A1 had three AS types between tumor and normal tissues (Figure 2A). Table 1 listed the top 20 upregulated and downregulated AS events.

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TABLE 1 The detailed information of the top 40 most different AS events of SLCs.

Splice_event	Gene_Symbol	PSI_Tumor	PSI_Normal	PSI_Difference	P value_Difference
Upregulated
SLC23A3_AD_57600	SLC23A3	1	0.0656	0.9344	4.23E-20
SLC19A1_AP_60878	SLC19A1	0.993	0.46405	0.529	7.14E-25
SLC39A14_AP_83005	SLC39A14	0.8945	0.4623	0.4322	1.28E-25
SLCO2B1_ES_17825	SLCO2B1	1	0.5685	0.4315	0.00371
SLC8A1_AP_53341	SLC8A1	1	0.5928	0.4072	1.12E-07
SLC14A1_AP_45332	SLC14A1	0.7384	0.3545	0.3839	0.0056
SLC13A3_AT_59696	SLC13A3	0.3797	0	0.3797	1.98E-16
SLC17A4_AD_75550	SLC17A4	0.7288	0.3771	0.3517	7.23E-19
SLC23A3_AD_57608	SLC23A3	0.44	0.1128	0.3272	1.69E-10
SLC14A2_AD_45331	SLC14A2	1	0.7177	0.2823	0.000142
SLC44A1_ES_87116	SLC44A1	0.46705	0.1898	0.2772	1.21E-05
SLC9B2_AP_70169	SLC9B2	0.6152	0.3399	0.2753	1.01E-10
SLCO4A1_AA_102832	SLCO4A1	0.5337	0.26	0.2737	0.00954
SLC25A45_AP_16821	SLC25A45	0.4749	0.213	0.2619	7.03E-16
SLC14A2_AT_45329	SLC14A2	1	0.7593	0.2407	1.50E-07
SLC25A16_ES_11952	SLC25A16	0.4691	0.2294	0.2397	3.61E-09
SLC16A5_AP_43350	SLC16A5	0.7244	0.4985	0.2259	1.92E-13
SLC27A2_ES_30568	SLC27A2	0.4077	0.1828	0.2249	0.0421
SLC29A1_AP_76356	SLC29A1	0.5184	0.2968	0.2216	1.43E-12
SLC26A6_AP_64720	SLC26A6	0.33395	0.1179	0.216	3.30E-15
Downregulated
SLC44A3_AP_3817	SLC44A3	0.5863	0.7923	−0.206	9.94E-17
SLC27A5_AT_52475	SLC27A5	0.2604	0.4738	−0.2134	8.96E-14
SLC26A6_AP_64721	SLC26A6	0.66605	0.8821	−0.216	3.30E-15
SLC29A1_AP_76359	SLC29A1	0.47	0.6959	−0.2259	3.82E-12
SLC3A2_ES_16468	SLC3A2	0.56	0.78755	−0.2275	0.00341
SLC16A5_AP_43347	SLC16A5	0.1947	0.4238	−0.2291	1.38E-15
SLC38A5_AP_88961	SLC38A5	0.28255	0.51375	−0.2312	0.000322
SLC17A4_ES_216384	SLC17A4	0.52185	0.75705	−0.2352	1.01E-09
SLC4A8_AT_21855	SLC4A8	0.38695	0.6249	−0.238	5.69E-10
SLC25A40_ES_80341	SLC25A40	0.76155	1	−0.2385	0.0173
SLC14A2_AT_45330	SLC14A2	0	0.2407	−0.2407	1.50E-07
SLC37A2_ES_19256	SLC37A2	0.0761	0.3423	−0.2662	0.0294
SLC22A18_AP_13936	SLC22A18	0.2294	0.509	−0.2796	2.18E-20
SLC9B2_AP_70167	SLC9B2	0.339	0.6601	−0.3211	2.59E-13
SLC4A7_ES_63783	SLC4A7	0.0266	0.4059	−0.3793	5.16E-09
SLC8A1_AP_53342	SLC8A1	0	0.4072	−0.4072	1.12E-07
SLC39A14_AP_83006	SLC39A14	0.1055	0.5377	−0.4322	1.28E-25
SLC13A3_AT_59695	SLC13A3	0.5263	1	−0.4737	1.39E-15
SLC39A14_ME_140283	SLC39A14	0.1053	0.6149	−0.5096	1.63E-25
SLC19A1_AP_60877	SLC19A1	0	0.5269	−0.5269	5.04E-27

We also identified 157 differentially expressed solute carrier genes (DESLCs) in cancer and paracancerous tissues. Comparing DEAS and DESLCs revealed both similarities and significant differences (Figure 2B). Abnormal AS events, particularly AP and AT, may directly impact parental RNA expression. Notably, 73.9% of DEAS in DESLCs were AP (53.6%) and AT (20.3%), highlighting the value of AS event analysis. We confirmed mRNA expression levels in ES events for SLC25A16 in non-tumor and tumor tissues, consistent with bioinformatic analysis (Figure 2C,D).

Identification of survival-related SLC-AS events in COAD

We identified 284 SLC-AS events that were significantly associated with at least one of the four types of survival outcomes (OS, PFI, DFI, and DSS) with log-rank P < 0.05, representing 23% of all SLC-AS events in COAD (Table S4). Among these, 66 events were associated with PFI, 64 with OS, 92 with DFI, and 62 with DSS (Figure 3A). Notably, six SLC-AS events were significant across PFI, OS, and DSS indicators: SLC22A23_ES_75197, SLCO2B1_ES_17829, SLC35B3_AT_75286, SLC25A16_ES_11953, SLC8B1_AP_24633 and SLCO2B1_ES_17824. (Figure 3A). Additionally, SLC37A3_AA_81987 correlated with PFI, OS, and DFI, whereas SLC25A17_AP_62357 was significantly associated with OS, DSS, and DFI (Figure 3A).

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In addition, 22 DEAS events with survival values were detected, with the AS types mainly including the following events: 13 AP, three AT, three ES, two AD, and one RI (Figure 3B,C). Two AP events involving multiple SLC genes were detected, namely SLC9B2, SLC44A3, SLC43A1, SLC3A1, and SLC26A6. Notably, these genes exhibited different expression patterns and survival outcomes for each type of AP event, in contrast to the prognostic implications of different AP events within the same gene.

Predicting protein-coding sequences and TMHs

To better understand SLC splicing events, we used the UCSC database to find alternative coding sequences and TMHMM software to predict transmembrane helices (TMHs) (Table 2). We found significant differences in transcript length, translation length, and the number of predicted TMHs in SLC9B2's two AP splicing events, while SLC43A1's AP types differed only in transcript length.

TABLE 2 Predicting protein-coding sequences and TMHs (transmembrane helices).

Splice_Event	Exons	Coding exons	Transcript length (bps)	Translation length (residues)	Number of predicted TMHs
SLC9B2_AP_70167	6	5	578	112	0
SLC9B2_AP_70169	11	10	2237	454	9
SLC43A1_AP_15846	15	14	2598	559	12
SLC43A1_AP_15844	15	14	2358	559	12
SLC3A1_AP_53420	10	10	2989	685	1
SLC44A3_AP_3817	14	14	2094	617	8
SLC44A3_AP_3818	14	13	2061	605	7
SLC16A3_AP_44300	5	4	2054	465	12

Two choline transporter SLC44A3 AP events (SLC44A3_AP_3817 and SLC44A3_AP_3818) were identified, with splice sites in the initial and second exons, respectively (Figure 3D,E,F,G). This led to amino acid sequence variations, producing proteins of 617 and 605 amino acids (Table 2). SLC44A3_AP_3817 was predicted to have eight TMHs, while SLC44A3_AP_3818 had seven (Figure 3H,I).

Establishment of the forecast model for COAD

We constructed a COAD forecast model based on prognosis-associated AS events, avoiding overfitting with LASSO (Figure 4A) and lambda plots (Figure 4B). Twelve AS events underwent multivariate Cox regression analysis, resulting in six selected events for the model. Risk scores for patients were calculated using these coefficients.

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ROC analysis showed the model's robust performance, with AUCs of 0.64, 0.66, and 0.72 at 1, 3, and 5 years, respectively, surpassing the TNM stage's AUC of 0.69 (Figure 4C,D). According to the KM curve, low-risk patients had higher survival probabilities than high-risk patients (P < 0.001) (Figure 4E). Risk scores were distributed in ascending order, dividing patients into high- and low-risk groups at the median point (Figure 4F). Overall, high-risk patients had shorter survival times. Finally, a heat map illustrated the PSI expression patterns of the six AS events between the groups (Figure 4G).

Correlation between risk score and clinical features

To confirm the independence of the risk score, both univariate and multivariate Cox regression analyses were applied to age, sex, TNM stage, stage (T, N, and M stages), and risk score. In the two analyses, the P values of the risk score were less than 0.001 and the hazard ratios were 1.573 (95% confidence interval [CI] 1.345–1.839) and 1.354 (95% CI 1.145–1.602), suggesting that the risk score was a good predictor (Figure 5A,B). We then explored the correlation between the risk score and clinical features (TNM stage, T, N, and M stage) and found that the risk score could significantly distinguish between patients with stages I and IV, stage II and IV, stage III and IV (Figure 5C), T1 and 2, T2 and 4, T3 and 4 (Figure 5D), N0 and 2 (Figure 5E), and M0 and 1 (Figure 5F).

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Functional enrichment analysis

GSEA was performed to explore the specific pathways associated with AS signatures. By using GSEA with mRNA expression between low- and high-risk groups according to the risk score of the AS signature, we found that several oncogenic pathways were significantly enriched in the low-risk group, including the P53, inflammatory response, BCAT, EGFR, AKT, NOD-like receptor, glycolysis, apoptosis, and protein secretion pathways (Figure 6A–D), indicating their involvement in COAD tumorigenesis and proliferation.

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SLC7A6-RI knockdown promotes colon cancer cell proliferation in vitro

Among the six AS events identified in our risk model, we focused on RI events for functional validation in COAD. We evaluated the prognostic value of SLC7A6 intron retention, finding higher PSI expression correlated with better survival (Figure 7A). Two small interfering RNAs (siRNAs), siSLC7A6-RI-1 and siSLC7A6-RI-2, are strategically positioned to target distinct intronic regions, effectively reducing the expression of the SLC7A6-RI isoform as depicted in Figure 7B. Knockdown efficiency was confirmed using specific primers, 61.7% and 64.7%, respectively, showing significant downregulation of retained introns (Figure 7C). Colony formation assays showed increased colonies in siSLC7A6-RI-1 and siSLC7A6-RI-2 groups compared to si-NC (Figure 7D,E). CCK8 assays indicated increased HCT116 cell viability, and EdU staining revealed enhanced proliferation after SLC7A6-RI knockdown (Figure 7F–H).

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GSEA pathway enrichment analysis indicated SLC7A6-RI involvement in PI3K_AKT_MTOR_SIGNALING (Figure 7I), MITOTIC_SPINDLE, and TNFA_SIGNALING_VIA_NFKB etc (Table 3). Western blotting showed increased levels of p-mTOR and PCNA after SLC7A6-RI knockdown (Figure 7J).

TABLE 3 GSEA analysis of SLC7A6-RI.

Term	Normalized Enrichment Score	P value
HALLMARK_TGF_BETA_SIGNALING	2.18	0
HALLMARK_ANDROGEN_RESPONSE	2.13	0
HALLMARK_PROTEIN_SECRETION	2.09	0.002
HALLMARK_UV_RESPONSE_DN	2.06	0.002
HALLMARK_HEME_METABOLISM	1.93	0
HALLMARK_PI3K_AKT_MTOR_SIGNALING	1.91	0.002
HALLMARK_MITOTIC_SPINDLE	1.84	0.02
HALLMARK_ESTROGEN_RESPONSE_EARLY	1.83	0.002
HALLMARK_APOPTOSIS	1.77	0.02
HALLMARK_TNFA_SIGNALING_VIA_NFKB	1.71	0.055

SLC7A6-RI isoform knockdown promotes human colon cancer xenograft growth in nude mice

Regarding in vivo experiments utilizing nude mouse-transplanted tumors, siSLC7A6-RI-2 was injected into the tumors at 2-day intervals (Figure 8A). Following the fourth siRNA injection, a substantial increase in tumor size was observed in the siSLC7A6-RI-2 injection group when compared with that in the si-NC group (Figure 8B). At the end of the experimental protocol, the siSLC7A6-RI-2 group exhibited a significantly higher tumor volume and weight than the si-NC group. Specifically, after a 14-day siRNA treatment period, the tumor weight in the si-SLC7A6-RI-2 group was 1.61 times greater than that in the si-NC group (Figure 8C,D). Calculations of the RTV indicated that the si-SLC7A6-RI-2 group had a T/C of 148.32% relative to the si-NC group (Figure 8C,D). Collectively, these findings suggested that inhibition of the SLC7A6-RI isoform promotes colon cancer cell proliferation.

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DISCUSSION

The SLC superfamily comprises over 400 transporters; however, several family members remain unexplored in the context of cancer pathogenesis.²⁹ Our research investigated the AS landscape of SLCs by utilizing RNA-sequencing data from a comprehensive cohort of patients with COAD from TCGA. We highlighted this analysis using SLC family splicing event data from TCGA SpliceSeq and UncoSplicing databases. To the best of our knowledge, our study, for the first time, revealed that knocking down the expression of the SLC7A6-RI isoform can activate the PI3K-Akt–mTOR signaling pathway to promote COAD progression.

In total, 1215 AS events were identified in 243 SLC genes, with the ES type representing 44.2% and the ME type accounting for 0.41%, which was the least frequent. Consistent with previous studies, ES was predominant across various tumor types.³⁰ Notably, our findings revealed that AS events of SLCs differed significantly between patients with colon cancer and normal individuals, underscoring their potential and offering valuable insights into the molecular mechanisms driving tumorigenesis. The differential ME type of SLC39A14 comprises nine exons and produces two splicing isoforms: SLC39A14-4A and SLC39A14-4B. The higher expression of SLC39A14-4B mRNA than of SLC39A14-4A mRNA has been reported in colon adenoma and CRC tissues.²² Moreover, SLC39A14-4B has been proposed as a marker to distinguish CRC from other colon pathologies.²³ Likewise, AS events have been observed in SLC2A8 mRNA within tumors and normal tissues, with studies indicating that aberrant splicing may contribute to reduced functionality in tumors.³¹ Conversely, SLC22A18 is a tumor suppressor gene, primarily based on its genomic location on chromosome 11p15.5, and the presence of missense mutations and abnormal splicing patterns have been detected in various cancer types.^32,33 Further investigations into the functional consequences of these splicing isoforms are crucial to deepen our current understanding of their precise roles in cancer progression and identify potential therapeutic targets.

In recent years, mounting evidence has indicated that splicing defects and the generation of specific isoforms play pivotal roles in cancer.^34,35 SLC2A11 comprises 12 exons, and cDNA cloning has revealed the presence of three types of transcript variations. ES and premature termination can cause frameshifts, resulting in the production of truncated SLC2A11 transcripts.³⁶ Similarly, we detected differences in transcript length (bp), translation length (residues), and number of predicted TMHs in survival-related DEAS. For example, SLC44A3 has two AP events, among which SLC44A3_AP_3817 encodes a 617-amino acid protein and eight TMHs, whereas SLC44A3_AP_3817 encodes a 605-amino acid protein and seven TMHs. These characteristics may explain the differences in the transport activity of SLC44A3 during different splicing events, thereby impacting its role in intestinal cancer progression.

Based on univariate Cox regression and LASSO regression analyses, we developed a prognostic model and identified six prognostic factors (SLC39A11_AD_43204, SLC7A8_AP_26712, SLC11A2_AP_21724, SLC2A8_ES_87631, SLC35B1_AA_42317, SLC7A6-RI) in the high-risk group, which differed significantly from low-risk patients with CRC, thereby indicating the strong discrimination ability. Knockdown of SLC39A11 expression reportedly suppresses cell proliferation, promotes apoptosis, delays the cell division cycle, and reduces cell invasion, thereby highlighting the key role of SLC39A11 in lung adenocarcinoma occurrence and development.³⁷ Similarly, AD events with a low PSI expression of SLC39A11 (SLC39A11_AD_43204) were associated with a lower COAD risk, indicating that SLC39A11 may play a key role in COAD development. Furthermore, high PSI expression in SLC7A8_AP_26712 was associated with an increased COAD risk. Higher SLC7A8 expression was found to inhibit lung adenocarcinoma growth and metastasis, and was associated with poor prognosis in patients with lung adenocarcinoma.³⁸

Intron retention is frequently observed in a wide range of cancer transcriptomes when compared with normal tissues.³⁹ Likewise, in the present study, we identified 74 RI events in SLC family genes in CRC, which remain poorly examined. Intron-retention-derived tumor neoantigens have been associated with good survival in patients with pancreatic cancer, highlighting the diagnostic potential of intron retention.⁴⁰ Interestingly, our study found that SLC7A6-RI was the only survival-related RI event and that higher PSI expression correlated with better patient survival. Moreover, North et al.⁴¹ used intron-splicing events generated by SF3B1 mutations in patients with tumors to design and synthesize introns for the targeted elimination of tumor cells. Therefore, the commonly present SLC7A6-RI in patients may also be a promising candidate to design therapeutic synthetic introns. We experimentally confirmed the prognostic value of SLC7A6-RI in patients with COAD. Specifically, the knockdown of this intron could promote cancer cell proliferation by activating the PI3K-Akt–mTOR signaling pathway. These insightful results also indicate potential novel therapeutic strategies against COAD and that SLC7A6-RI may serve as an accurate and potent biomarker.

AUTHOR CONTRIBUTIONS

Chao Sun: Data curation; funding acquisition; investigation; methodology; software; validation; writing – original draft; writing – review and editing. Boning Zeng: Data curation; funding acquisition; investigation; methodology; writing – original draft; writing – review and editing. Jilong Zhou: Data curation; methodology; software; validation. Nan Li: Data curation; investigation; software; writing – original draft. Mingwei Li: Funding acquisition; investigation; methodology. Chaowei Zhu: Investigation; methodology. Shouxia Xie: Writing – review and editing. Yifei Wang: Writing – review and editing. Shaoxiang Wang: Conceptualization; data curation; funding acquisition; investigation; supervision; writing – original draft; writing – review and editing. Xiao Wang: Conceptualization; data curation; funding acquisition; investigation; supervision; writing – original draft; writing – review and editing.

ACKNOWLEDGMENTS

We would like to thank Editage (www.editage.cn) for English language editing.

FUNDING INFORMATION

This research was funded by the Shenzhen Key Laboratory (ZDSYS20200811142804014), the National Natural Science Foundation of China (82073937 and 82373126), the Shenzhen Science and Technology Program (JCYJ20210324093602007, JCYJ20220531101016035, JCYJ20220818102605011), the Postdoctoral Research Foundation of China (2022M722213 and 2022M712192), the Shenzhen Key Medical Discipline Construction Fund (SZXK059), and the GuangDong Basic and Applied Basic Research Foundation (2023A1515110415). [Correction added on 28 October 2024, after first online publication. The Funding information was changed.]

CONFLICT OF INTEREST STATEMENT

The authors have no conflict of interest.

ETHICS STATEMENT

Approval of the research protocol by an Institutional Reviewer Board: This study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Shenzhen People's Hospital (Project identification code: LL-KT-201801126), and the approval date was February 5, 2018.

Informed Consent: All human specimens were collected with the informed consent of guardians.

Registry and the Registration No. of the study/trial: N/A.

Animal Studies: All procedures regarding animals in this study were approved by the Institutional Animal Care and Use Committee of Shenzhen People's Hospital.

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