2.1 The mRNA expression of SMC1A

RNAseq data were downloaded from UCSC XENA (https://xenabrowser.net/datapages/) in transcripts per million reads (TPM) format for TCGA and The Genotype-Tissue Expression (GTEx) and unified via the Toil process.¹⁸ The RNAseq data in TPM format were then log2 transformed. The ggplot2 package was used to visualize the expression of SMC1A in cancer and normal tissues, and the Mann–Whitney U test was used for statistical analysis. The pan-cancer data of TCGA include 33 cancer types, and they are ACC (Adrenocortical carcinoma), BLCA (Bladder urothelial carcinoma), BRCA (Breast invasive carcinoma), CESC (Cervical squamous cell carcinoma), CHOL (Cholangiocarcinima), COAD (Colon adenocarcinoma), DLBC (Lymphoid Neoplasm 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 (Brain 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), and UVM (Uveal Melanoma).

2.2 Validation of protein expression of SMC1A

Protein levels of SMC1A in normal and CRC tumor tissues were verified by immunohistochemical (IHC) assays using the Human Protein Atlas database (HPA, https://www.proteinatlas.org/), which is a protein expression profiling IHC-based database.^19-21

2.3 Validation of the Cancer Cell Line Encyclopedia (CCLE) database

The CCLE database (https://sites.broadinstitute.org/ccle/) provides data on the genomes of over 1100 cell lines from different tumors, including a total of 56 intestinal cancer cell lines such as SW480, SW620, HCT116, etc.²² The CCLE database data are mainly obtained by high-throughput sequencing, which contains five main dataset types, they are copy number, mRNA expression (Affymetrix), inverse phase protein array, reduced representation bisulfite sequencing, and mRNA expression (RNA sequencing). Using CCLE database, the genes associated with SMC1A were analyzed by Pearson and the final genes were filtered by Pearson correlation coefficient r > 0.5 and p value <0.001.

2.4 Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis

Relevant genes obtained from the CCLE database were used for subsequent GO and KEEG pathway analysis, the org.Hs.eg.db R package was utilized for ID conversion, and the clusterProfiler R package was applied for GO and KEEG enrichment analysis.²³ p values were adjusted using the BH method.

The differentially expressed genes in the high- and low-risk groups of SMC1A of COAD were analyzed from the TCGA database. GO and KEGG enrichment analysis was performed using the clusterProfiler R package to obtain significantly enriched functions and pathways.²³

2.5 SMC1A analysis at single-cells levels

The Tumor Immune Single Cell Center (TISCH) database (http://tisch.comp-genomics.org/) is a database focused on single-cell RNA (scRNA) sequencing in the tumor microenvironment (TME).²⁴ This database is used to study the expression levels of SMC1A in different immune cell types under single-cell conditions in CRC. It was also analyzed for the expression of SMC1A in single cells in different TNM stages.

2.6 Immune cell infiltration analysis

Immune cell infiltration in COAD was detected by GSVA R package for the expression levels of SMC1A.²⁵ The ssGSEA method was used to calculate immune cell infiltration. Spearman's method was used for correlation analysis, and a p value <0.05 was considered statistically significant.

2.7 Correlation of mutation and somatic cell copy number variation (SCNV) of SMC1A and immune cell infiltration

Tumor Immune Estimation Resource (TIMER) (https://cistrome.shinyapps.io/timer/) is a database for the systematic analysis of immune infiltration in different types of cancer.^{26, 27} The database is estimated by multiple methods of immune infiltration abundance for a comprehensive exploration of tumor immunological, clinical, and genomic features. The correlation of SMC1A mutation and SCNA status with immune cell infiltration in COAD was examined using the TIMER database. The two-sided Wilcoxon rank-sum test was used for statistical analysis.

2.8 Association of SMC1A expression and tumor stem cell scores

Tumor stem cell features were extracted from the transcriptome of COAD samples from TCGA.²⁸ The correlation between SMC1A expression levels and tumor stemness scores was statistically analyzed by the Spearman test.

2.9 Mice and MC38 tumor model

A total of 10 female C57BL/6 mice, aged 6 weeks, were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. The murine colon cancer cell line MC38 was maintained in DMEM (Gibco) supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin/streptomycin, and cultured at 37°C with 5% CO2. Then, the control and SMC1A overexpressing MC38 cell lines were established according to our previous publication.¹⁵ Mice were inoculated subcutaneously in the flank with 5 × 10⁵ control or MC38 overexpressing cells. The mice were euthanized and tumors harvested when the maximum tumor diameter reached around 1.0 cm. All animal experiments were performed in accordance with the guidelines of the Institutional Animal Care and Use Committee of The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University (IACUC-1810008).

2.10 Immunohistochemical assay

Tumor tissues were fixed in 4% paraformaldehyde and embedded in paraffin. The slides (4 μm) were deparaffinized in xylene and hydrated using a graded alcohol series. Antigen retrieval was conducted in antigen unmasking solution at 100°C for 20 min. The sections were then incubated with 0.3% H₂O₂ for 20 min and blocked with 5% BSA (Sigma-Aldrich) for 1 h at room temperature. The slides were incubated with primary antibodies (anti-CD45, 1:50 dilution, BD Biosciences, 550,539) overnight at 4°C, followed by incubating with HRP-conjugated IHC detection reagent for 2 h at 37°C. Immunoreactive cells were visualized using DAB, and nuclei were stained with hematoxylin for 15 s at room temperature. Cells were counted using an IX70 inverted fluorescence microscope.

2.11 Flow cytometry assay

Tumor samples are cut into small pieces and macerated through 70 μm cell strainer, then digested into single-cell suspension by using digestive solution (1.5 mg/mL collagenase, 1.5 mg/mL hyaluronidase and 20 μg/mL DNase diluted by HBSS) at 37°C for 30 min. Cell suspensions were collected and lysed with RBC lysis buffer, then pretreated with Fc-block CD16/CD32 antibody (BD Biosciences, 553,142) at 4°C for 15 min. For extracellular staining, cells were resuspended by FACS buffer (1% FBS 0.05% sodium azide in PBS), incubating with CD3 (FITC Hamster anti-mouse, BD Biosciences, 561,827; 1:100 dilution) and CD4 (APC-H7 Rat anti-mouse, BD Biosciences, 560,246; 1:100 dilution) antibodies at 4°C for 20 min. For intracellular staining, cells were fixed in Fixation/Permeabilization Solution (eBioscience) for 15 min and stained with FoxP3 (PerCP-Cy5.5 Rat anti-Mouse, BD Biosciences, 563,902; 1:100 dilution) and Ki67 (PE Rat anti-mouse, Biolegend, 16A8; 1:200 dilution) antibodies at 4°C for 20 min after washing. For cytokine expression, cells were resuspended using DMEM medium containing 10% FBS and 1% penicillin/streptomycin and activated with Leukocyte Activation Cocktail (BD Biosciences, 554,656; 1:100 dilution) at 4°C for 6 h, then stained with IL4 antibody (BV421 Rat anti-mouse, Biosciences, 562,915). Cells were analyzed using a flow cytometer (BD Biosciences), and the data were analyzed using FlowJo software.

2.12 Patients and human CRC tissues

The samples of 37 CRC patients were collected from January 2019 to May 2021 at the Affiliated Huaian No.1 People's Hospital of Nanjing Medical University. Patients' tumors and adjacent normal paracancerous tissues were included in this study. The CRC diagnosis was confirmed by at least two pathologists. Collected CRC sample tissues (tumor tissues and paraneoplastic tissues) were preserved in RNA Stabilizing Tissue Preservation Solution (Beyotime Biotechnology, R0118) and stored at −80°C. All samples used in this project were de-identified and assigned a study number. None of these 37 patients had received any preoperative anticancer therapies. All procedures involving human material in this study were approved by the committee of The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University (Ethical number: YX-2021-106-01).

2.13 RNA isolation and real-time quantitative PCR (RT-qPCR)

Total RNA of colon cancer samples was extracted with TRIzol (Invitrogen). To check the expression of SMC1A, 500 ng RNA was reverse transcribed to cDNA (Takara Biotechnology Co., Ltd) and then qPCR assays were performed. The primer sequences were as follows: SMC1A Forward: 5’-GGAGCAGCAGCAGATTGAG-3′, Reverse: 5’-TCTCTTCTTCCATCCGTTCTTC-3′; GAPDH Forward: 5’-TGACTTCAACAGCGACACCCA-3′, Reverse: 5’-ACCCTGTTGCTGTAGCCAAA-3′. The expression of SMC1A was normalized by GAPDH. To check the expression of has-miR-23b-3p, 500 ng RNA was reverse transcribed to cDNA (QIAGEN; USA), then qPCR assays were performed using miRCURY LNA SYBR Green PCR kit (QIAGEN). The hsa-miR-23b-3p (QIAGEN) and the normalization with U6 (QIAGEN) were used for primers of qPCR. The expression of has-miR-23b-3p was normalized by U6. The fold changes of SMC1A and hsa-miR-23b-3p were calculated using 2−^ΔΔCt method.

2.14 Statistical analysis

All statistical analyses were performed using R language 4.0.5 or SPSS 16.0 software. The expression of SMC1A in cancer and normal tissues of pan-cancer was analyzed by the Wilcoxon rank-sum test. The genes associated with SMC1A in CCLE database were analyzed by the Pearson's test. p values of GO and KEGG analysis were adjusted using the BH method. The expression of SMC1A in different TNM stages at single-cell levels from TISCH database was analyzed by the Kruskal–Wallis test. The immune cell infiltration analysis was performed by the Spearman's test. The correlation of SMC1A mutation and SCNA status with immune cell infiltration in COAD was examined by the two-sided Wilcoxon rank-sum test. The correlation between SMC1A expression and the expression of immune checkpoint genes (CD274, CTLA4, and PDCD1) or the expression of stem cell indicators (CD133, CD29, CD166, CD44, Lgr5, and Oct4) was analyzed by the Spearman's test. Two-tailed t-tests were used to compare data between two groups in qRT-PCR, Immunohistochemistry, and flow cytometry assay. p < 0.05 was considered to be statistically significant.

3.1 SMC1A is highly expressed in colon adenocarcinoma

To examine the involvement of SMC1A in clinical cancer patients, we first investigated the mRNA levels of SMC1A in all tumor samples and normal tissues in the TCGA and GTEx databases. Our findings demonstrated that SMC1A is an oncogene that is highly expressed in numerous clinical cancer tissues compared with normal tissues, including COAD (Figure 1A). Similarly, SMC1A protein expression was increased in colon cancer tissues in the mass spectrometry-based proteomics of the CPTAC database (Figure 1B). To further identify the protein levels of SMC1A, we next examined the protein expression of SMC1A by IHC using the Human Protein Atlas database (HPA, https://www.proteinatlas.org/). SMC1A was mainly expressed in the nucleus (Figure 1B). Similar to the results of TCGA and CPTAC databases, the protein expression levels of SMC1A were significantly higher in tumor tissues of CRC than in normal tissues (Figure 1C). These results further confirmed that SMC1A may be a potential biomarker for COAD.

3.2 The functions and pathways associated with SMC1A

Next, we researched the relevant functions and signaling pathways of SMC1A using gene ontology (GO) and KEEG pathways. First, we used the Cancer Cell Line Encyclopedia (CCLE) database (https://sites.broadinstitute.org/ccle/), which has sequencing results for 56 large intestine cell lines. We found SMC1A-associated genes in 56 cell lines (by correlation coefficient >0.5, p value <0.001), and we showed the most positive and negative correlations with SMC1A in 20 genes, respectively (Figure 2A). GO and KEEG analysis showed that the most related to the expression of SMC1A was DNA activity, including DNA replication, DNA helicase activity, cell cycle, and DNA replication (Figure 2B). Moreover, we examined the differential genes of SMC1A in the TCGA database and analyzed the GO and KEGG pathways, which similarly showed that SMC1A was associated with DNA activity (Figure 2C).

3.3 SMC1A is expressed in a variety of immune cells

T cells are one of the most important components of the immune system and assume an essential function in the immune response to tumors. Recent studies have shown that chromatin structure-mediated proliferation of T cells is one of the key mechanisms of immune regulation, specifically, resting T cells possess condensed chromatin, while proliferating T cells possess a more open chromatin structure.²⁹ Chromosome structure maintenance complexes (SMCs) were found to play roles in the regulation of chromatin structure during cell division; however, they are now found to function in T-cell development and function as well.²⁹

Therefore, we next explored whether SMC1A serves significant functions in the immune microenvironment. To examine how SMC1A modulates the immune microenvironment in COAD, we first investigated the expression of SMC1A at the single-cell levels in different cell types using the TISCH (Tumor Immune Single-cell Hub) (http://tisch.comp-genomics.org/home/; http://tisch.comp-genomics.org/home/) database, and we found that SMC1A was highly expressed in many types of immune cells among several datasets (Figure 3A). Further, we explored the expression levels of SMC1A in different immune cell types in a GSE database CRC_GSE146771_Smartseq2, and the results showed that SMC1A was significantly expressed in a variety of immune cells (Figure 3B). In particular, among all types of immune cell types, SMC1A expression showed extremely high in proliferating T cells (Figure 3A,B). Interestingly, the SMC1A levels of multiple types of immune cells were associated with TNM staging, including conventional CD4 cells, CD8 T cells, exhausted CD8 T cells, mast cells, monocytes or macrophages, and natural killer cells (Figure 3C). However, the expression levels of SMC1A in proliferating T cells were not associated with TNM staging (Figure 3C).

3.4 Correlation between SMC1A and immune cell infiltration

To better understand the influences of SMC1A expression on immune cell infiltration, we performed the ssGSEA method in the TCGA database using the GSVA package to calculate the correlation coefficient between SMC1A expression and immune cells. Our results showed that high expression of SMC1A was positively correlated with immune infiltration (Figure 4A,B). We further verified the effect of SMC1A expression levels on immune cell infiltration in the mice model. Similar to the results of TCGA database, immunohistochemical data showed that in contrast to the control group, the CD45 cell infiltration was significantly increased in the SMC1A overexpression group (Figure 4C), indicating that SMC1A was positively associated with immune infiltration in the mice model. SMC1A expression showed positively associated with Th2 cells in TCGA samples (Figure 4A). Th1 and Th2 are two subtypes of CD4 T cells. Th1 cells suppress malignant tumor proliferation via inducing an immune response to tumors by mainly secreting interferon-γ (IFN-γ) and tumor necrosis factor-α (TNF-α); while Th2 cells mainly secrete interleukin-4 (IL4), IL-10 leading to tumor immunosuppression.³⁰ Furthermore, CD4(+) regulatory T cells (Tregs) play a crucial position in immune homeostasis and contribute to tumor progression in malignancies by suppressing effective tumor immunity.³¹ Interestingly, the high expression of SMC1A was significantly linked to the expression of Tregs in TCGA database (Figure 4A). To further confirm these results, we developed the MC38 mouse model and examined the expression of Th2 and Treg cells by flow cytometry assay. Similarly, in vivo experiments also showed that the percentage of IL4⁺CD4⁺ T cells (Th2) and FoxP3⁺CD4⁺ T cells (Tregs) was significantly higher in the SMC1A overexpression group (Figure 4D,E). In conclusion, these data suggested that high SMC1A levels may be associated with tumor immunosuppression.

In addition, single-cell database showed that SMC1A was highly expressed in proliferating T cells (Figure 3A), we therefore inquired whether SMC1A expression impacted T-cell proliferation. Intriguingly, in vivo experiment showed that the frequency of Ki67-positive CD3⁺ T cells was higher in SMC1A overexpressing group than in control (Figure 4F), suggesting that SMC1A expression may affect the proliferation of T cells.

In addition to gene expression, mutations in the SMC1 gene also perform key effects in the development of CRC.¹⁴ Next, we explored whether mutations of SMC1A were also involved in immune infiltration. The results showed that mutated SMC1A was elevated in CD8 T cells and CD8 central memory T cells (Figure 4G). Similarly, the mutated SMC1A group was significantly higher in CD4 T cells, CD4 T-cell Th1 and Th2 than in SMC1A wild-type group (Figure 4H). Furthermore, we also examined the correlation between immune cell infiltration and the somatic cell copy number variation (SCNV) of SMC1A. Our data revealed that B-cell, neutrophil, and dendritic cell infiltration and the SCNV of SMC1A showed statistically significant differences; however, there was no correlation between CD8 T cells, CD4 T cells, and macrophages and CNV of SMC1A (Figure 4I).

3.5 SMC1A may be a predictive marker for immune checkpoint inhibitor (ICI) therapy

A large number of studies have been conducted to identify predictors of response to ICI (antiprogrammed cell death 1 (PD-1)/programmed cell death-ligand 1 (PD-L1)) immunotherapy. Currently, the characteristics that predict immunotherapy response are tumor mutational burden (TMB), the expression of immune checkpoints (i.e., PD-L1 expression), and “hot” T-cell inflammatory microenvironment.^32-35 We have known that high SMC1A expression was positively correlated with immune cell infiltration, especially T-cell immune cell infiltration, that is, SMC1A was in the “hot” T-cell inflammatory microenvironment in colon cancer (Figure 4A,B). In addition, SMC1A was positively linked to the immune checkpoint genes CD274, CTLA4, and PDCD1 in COAD (Figure 5A–C). Therefore, we considered that SMC1A is a possible biomarker for ICI treatment prediction.

3.6 SMC1A may mediate tumor stem cell in COAD

Cancer stem cells (CSCs) have been reported to show a very strong DNA damage response (DDR).^{36, 37} Our previous results showed that SMC1A played important roles in DNA activity (Figure 2B,C), therefore, we speculated whether SMC1A could mediate CSCs. To validate the involvement of SMC1A in the tumor stemness of COAD, we measured the mRNA levels of SMC1A with tumor stemness scores from TCGA database. We used two methods to evaluate tumor cell stemness, which were RNAss based on mRNA expression (RNA stemness score) and DNAss based on gene DNA methylation (DNA stemness score). The results showed that the expression of SMC1A and RNAss were positively correlated with COAD, which means that the higher the levels of SMC1A expression the stronger the tumor stemness (Figure 6A). However, the expression of SMC1A was not related to DNAss (Figure 6A).

Currently, the main CSC markers found to be associated with CRC are CD133, CD29, CD166, CD44, Lgr5, oct4, Nanog, etc.^38-41 These stem cell markers are involved in proliferation, invasive metastasis, and drug resistance of CRC via multiple molecular mechanisms.^{38, 39} Our association analysis indicated that the mRNA levels of SMC1A were strongly correlated with the expression levels of stem cell indicators CD133, CD29, CD166, CD44, Lgr5, and Oct4 (Figure 6B).

3.7 SMC1A binds to miR-23b-3p

The ceRNA mechanism of SMC1A was explored next to understand the cause of SMC1A overexpression in COAD. We investigated the binding miRNAs of SMC1A using the Encyclopedia of RNA Interactomes (ENCORI) database (https://starbase.sysu.edu.cn/). The ENCORI database contains data from CLIP-seq, which predicts miRNA targets with Ago protein binding sites by intersections to demonstrate miRNA-target interactions, this database provides results from seven prediction programs (PITA, RNA22, miRmap, DIANA-MicroT, miRanda, PicTar, and TargetScan). Our results exhibited that the predicted results of binding miRNAs of SMC1A appeared in 2 and more programs (Figure 7A). We further validated the predicted miRNAs in the COAD samples from TCGA database. Among them, only miR-23b-3p was significantly downregulated in colon cancer tissues compared with normal tissues (Figure 7B), and miR-23b-3p and SMC1A showed a significant negative correlation in COAD samples (Figure 7C).

Further, we collected 37 cases of colon cancer and paired paraneoplastic tissues and performed RT-qPCR assays for the expression levels of SMC1A and miR-23b-3p in the tissues. Our results showed that the mRNA levels of SMC1A were significantly increased in colon cancer tissues compared with paraneoplastic tissues (Figure 7D). However, the expression of miR-23-3p was significantly downregulated in 37 clinical samples (Figure 7E).