RGCC is a Prognostic Biomarker and Correlates with Immune Infiltrates in Breast Cancer

doi:10.21203/rs.3.rs-2975689/v1

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RGCC is a Prognostic Biomarker and Correlates with Immune Infiltrates in Breast Cancer

https://doi.org/10.21203/rs.3.rs-2975689/v1

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Background

Methods and results

RGCC differential expression analysis was performed based on TCGA, GEO, UALCAN and HPA databases, respectively. Then, KM curve and ROC curve were constructed to evaluate the prognosis and diagnostic value of RGCC. In addition, Immune Infiltration Analysis was performed by ssGSEA. scTIME and cancerSEA databases were used to illustrate the relationship between RGCC and tumor immune cells at the single-cell level. Subsequently, the clinical relevance of RGCC was discussed and Nomogram and calibration curves were constructed. Finally, R package clusterProfiler was used for enrichment of the GO (Gene Oncology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways to explore the biological function of RGCC.

Results

Low expression of RGCC in breast cancer was associated with better overall survival (OS) and Disease Specific Survival (DSS), and ROC curve suggested that RGCC had a good diagnostic value. The expression of RGCC was positively correlated with the invasion levels of iDC, Macrophages, Neutrophils, CD8 T cells, and Th1 cells. RGCC was positively correlated with immunoinhibitors TGFB1 and immunostimulators C10orf54, CD40, CXCL12, CXCR4, IL6, NT5E, TNFRSF4, TNFRSF8 and TNFSF9 in BRCA. Single cell data showed that RGCC was highly expressed in Macrophages. RGCC mRNA expression was significantly correlated with Age, Histological type, T stage, HER2 status and PAM50. Mechanistically, we found that RGCC is closely related to cell adhesion and MHC Class II protein complexes. RGCC was associated with angiogenesis in two single-cell datasets.

Conclusions

RGCC may play an important role in cell proliferation and metastasis, and can be used as a prognostic biomarker to determine the prognosis of BRCA and immune invasion.

RGCC (regulator of cell cycle)

Prognosis

Biomarker

Immune infiltrate

Breast Cancer

Breast cancer is the most common form of cancer worldwide and one of the leading causes of cancer-related deaths in women, and its incidence is rising at an alarming rate. [1]. Most cancers are diagnosed at an advanced stage, limiting the effectiveness of conventional treatment. Consequently, early detection is essential. [2]. The primary obstacles to effective breast cancer prognosis are tumor proliferation, invasion, and metastasis, but the molecular basis of this disease is not yet recognized. [3]. For these reasons, the identification of new biomarkers is urgent and highly demanded in order to enhance early detection of the disease and improve its prognosis.

Immune microenvironment (TME) is a place of immune surveillance and immune escape between tumor cells and the human immune system. Previous studies have shown that BRCA expresses vascular endothelial growth factor, additional molecules that promote additional tumor cell growth and migration, and more tumor infiltrating lymphocytes (TILs) and tumor associated macrophages (TAMs)[4]. TIFs have been shown to play a crucial role in tumor-associated immune responses and can effectively improve the prognosis of a variety of tumors[5–7]. Immunotherapy has garnered increasing interest and produced extraordinary results in a broad range of tumor types as a result of intensive research into the tumor microenvironment. T cells are a crucial component of the microenvironment of a tumor. T-cell infiltration is an essential prognostic indicator in the majority of malignancies.[8]. Immune checkpoint inhibitors are the most promising immunotherapy for breast cancer at this time, but they usually have to be combined with other treatments, particularly for subtypes of the population that have limited benefits. Consequently, a better comprehension of the percolation of different immune cells in breast cancer and the associated pathogenesis, as well as the search for effective biomarkers, are pressing issues that need to be addressed promptly.

RGCC (regulator of cell cycle) is a protein-coding gene, also known as RGC-32(response gene to complement 32 protein), which is an important regulatory gene of cell cycle progression. It is induced by p53 in response to DNA damage, or by sublytic levels of complement system proteins that result in activation of the cell cycle and associate with and increase the kinase activity of cell division cycle 2 protein. RGCC which is expressed in a variety of the organs and tissues, such as brain, placenta, heart, and the liver is related to multiple physiological and pathological processes, including cell proliferation, apoptosis, fibrosis, metabolic disease, and cancer[9]. They have been linked to a number of diseases, but the underlying mechanisms of this association remain obscure. Nonetheless, this gene-breast cancer research is still in its early stages. A significant number of studies have demonstrated the overexpression of RGCC in colon cancer and the progression of Alzheimer's disease. [10–12]. In the present study, RGCC demonstrated significant diagnostic and prognostic relevance.

In this study, we compared the differences in RGCC expression between breast cancer tumor tissues and normal breast tissues using The Cancer Genome Atlas (TCGA) database, as well as the Gene Expression Omnibus database (GEO) and the UALCAN dataset for verification. Evaluation of the prognostic value of RGCC expression in BRCA patients. In addition, GO/KEGG analysis was conducted to determine its potential function. Finally, ssGSEA (single-sample gene collection enrichment analysis) was utilized to analyze the infiltration levels of various types of immune cells in the tumor microenvironment, to investigate the correlation between potential molecular mechanisms and immune cell infiltration, and to provide a certain theoretical basis for breast cancer immunotherapy. The flowchart of this study is shown in Fig. 1.

1. Baseline characteristics of patients

The clinical information of 1083 patients were downloaded from the TCGA database, see Table 1 for details. The expression of RGCC was significantly correlated with Age (P = 0.004), T stage (P = 0.003), Histological type (P < 0.001), PAM50 (P < 0.001), Anatomic neoplasm subdivisions (P = 0.048), radiation therapy (P = 0.035), OS event (P = 0.028). In addition, we also proved by univariate logistic regression analyses that there are clinicopathological differences between the RGCC high expression group and the low expression group, including age (p = 0.003), Histological type (P < 0.001), HER2 status (P = 0.049), PAM50 (P < 0.001), radiation therapy (P = 0.030), Anatomic neoplasm subdivisions (P = 0.042) (Table 2).

Table 1

Clinicopathological characteristics of high- and low-RGCC expression groups.
Characteristic	Low expression of RGCC	High expression of RGCC	p
n	541	542
Age, n (%)			0.004
<=60	276 (25.5%)	325 (30%)
> 60	265 (24.5%)	217 (20%)
T stage, n (%)			0.003
T1	112 (10.4%)	165 (15.3%)
T2	333 (30.8%)	296 (27.4%)
T3	73 (6.8%)	66 (6.1%)
T4	21 (1.9%)	14 (1.3%)
N stage, n (%)			0.292
N0	246 (23.1%)	268 (25.2%)
N1	187 (17.6%)	171 (16.1%)
N2	61 (5.7%)	55 (5.2%)
N3	32 (3%)	44 (4.1%)
M stage, n (%)			0.914
M0	462 (50.1%)	440 (47.7%)
M1	11 (1.2%)	9 (1%)
Pathologic stage, n (%)			0.076
Stage I	74 (7%)	107 (10.1%)
Stage II	318 (30%)	301 (28.4%)
Stage III	126 (11.9%)	116 (10.9%)
Stage IV	9 (0.8%)	9 (0.8%)
Histological type, n (%)			< 0.001
Infiltrating Ductal Carcinoma	420 (43%)	352 (36%)
Infiltrating Lobular Carcinoma	60 (6.1%)	145 (14.8%)
PR status, n (%)			0.567
Negative	178 (17.2%)	164 (15.9%)
Indeterminate	2 (0.2%)	2 (0.2%)
Positive	335 (32.4%)	353 (34.1%)
ER status, n (%)			0.157
Negative	111 (10.7%)	129 (12.5%)
Indeterminate	2 (0.2%)	0 (0%)
Positive	403 (38.9%)	390 (37.7%)
HER2 status, n (%)			0.116
Negative	263 (36.2%)	295 (40.6%)
Indeterminate	7 (1%)	5 (0.7%)
Positive	88 (12.1%)	69 (9.5%)
PAM50, n (%)			< 0.001
Normal	9 (0.8%)	31 (2.9%)
LumA	235 (21.7%)	327 (30.2%)
LumB	161 (14.9%)	43 (4%)
Her2	55 (5.1%)	27 (2.5%)
Basal	81 (7.5%)	114 (10.5%)
Menopause status, n (%)			0.301
Pre	111 (11.4%)	118 (12.1%)
Peri	16 (1.6%)	24 (2.5%)
Post	362 (37.2%)	341 (35.1%)
Anatomic neoplasm subdivisions, n (%)			0.048
Left	298 (27.5%)	265 (24.5%)
Right	243 (22.4%)	277 (25.6%)
Radiation therapy, n (%)			0.035
No	228 (23.1%)	206 (20.9%)
Yes	252 (25.5%)	301 (30.5%)
OS event, n (%)			0.028
Alive	452 (41.7%)	479 (44.2%)
Dead	89 (8.2%)	63 (5.8%)
DSS event, n (%)			0.156
Alive	479 (45.1%)	499 (46.9%)
Dead	49 (4.6%)	36 (3.4%)
PFI event, n (%)			0.210
Alive	460 (42.5%)	476 (44%)
Dead	81 (7.5%)	66 (6.1%)

Table 2

Associations of RGCC expression with clinicopathological characteristics of patients (n = 1083)
Characteristics	Total(N)	Odds Ratio(OR)	P value
T stage (T3&T4 vs. T1&T2)	1,080	0.822 (0.593–1.137)	0.236
N stage (N1&N2&N3 vs. N0)	1,064	0.885 (0.696–1.126)	0.320
M stage (M1 vs. M0)	922	0.859 (0.343–2.095)	0.738
Pathologic stage (Stage III&Stage IV vs. Stage I&Stage II)	1,060	0.890 (0.672–1.177)	0.413
Age (> 60 vs. <=60)	1,083	0.695 (0.546–0.884)	0.003
Histological type (Infiltrating Lobular Carcinoma vs. Infiltrating Ductal Carcinoma)	977	2.884 (2.078–4.044)	< 0.001
PR status (Positive vs. Negative)	1,030	1.144 (0.882–1.483)	0.311
ER status (Positive vs. Negative)	1,033	0.833 (0.623–1.112)	0.215
HER2 status (Positive vs. Negative)	715	0.699 (0.488–0.997)	0.049
PAM50 (LumA&LumB&Her2&Basal vs. Normal)	1,083	0.279 (0.124–0.568)	< 0.001
radiation_therapy (Yes vs. No)	987	1.322 (1.028–1.702)	0.030
Anatomic neoplasm subdivisions (Right vs. Left)	1,083	1.282 (1.010–1.628)	0.042
Menopause status (Post vs. Pre&Peri)	972	0.842 (0.635–1.116)	0.233

2. Low expression of RGCC in BRCA

A total of 33 cancer types were included in the pan-cancer analysis. The results showed that there were significant differences in the expression of RGCC in 23 tumors, of which 12 were up-regulated in tumor tissues and 11 were down-regulated. (Fig. 2A). RGCC is significantly lower expressed in breast cancer tissues compared to normal tissues. (p < 0.001) (Fig. 2B). Meanwhile, the expression of RGCC in 112 pairs of breast cancer tissues was significantly lower than that in adjacent tissues.(p < 0.001) (Fig. 2C). Then we studied the expression of RGCC protein in BRCA in the UALCAN database and compared it with normal breast tissue, and found that the expression level of RGCC protein in BRCA was significantly lower than that in normal breast tissue (p < 0.001) (Fig. 2D) .The above results are verified in GSE35525 (p < 0.001) (Fig. 2E). In addition, IHC staining data from the HPA database indicated that RGCC was moderately expressed in normal breast tissue and lowly expressed in BRCA tissue. (Fig. 2F、G).

3. The correlation between RGCC expression and immune infiltration.

Expression of RGCC was correlated with iDC (Spearman R = 0.424, p < 0.001), Macrophages (Spearman R = 0.404, p < 0.001), Neutrophils (Spearman R = 0.399, p < 0.001), CD8 T cells (Spearman R = 0.384, p < 0.001), and Th1 cells (Spearman R = 0.378, p < 0.001) were significantly positively correlated with immune cell infiltration levels. (Fig. 3A). In addition, the enrichment scores of iDC, Macrophages, Neutrophils, CD8 T cells and Th1 cells in the RGCC high expression group were significantly higher than those in the RGCC low expression group (p < 0.001) (Fig. 3B-K).

4. Correlation between RGCC expression and clinical features.

RGCC mRNA expression was significantly correlated with Age (Fig. 4A), Histological type (Fig. 4B), T stage (Fig. 4C), HER2 status (Fig. 4D) and PAM50 (Fig. 4E). However, no statistically significant correlations were found between RGCC expression levels and N stage (Fig. 4F), PR status (Fig. 4G), ER status (Fig. 4H) and M stage (Fig. 4I). The above results are consistent with the results of univariate logistic regression analyses.

5. Diagnostic value of RGCC expression in BRCA

In order to evaluate the diagnostic value of RGCC, we constructed receiver operating characteristic (ROC) curves, the area under the curve (AUC) was 0.934, which proved that it has high diagnostic value, as shown in Fig. 5A. Next, we constructed the ROC curve for each clinical subgroup, and the result was AUC values of 0.934 for stage I/II (Fig. 5B), 0.952 for stage III/IV (Fig. 5C), 0.942 for M0 (Fig. 5D), 0.937 for T1/T2 (Fig. 5E), 0.948 for T3/T4 (Fig. 5F), 0.930 for N0 (Fig. 5G) and 0.946 for N1-N3 (Fig. 5H), which proved that All have high diagnostic value.

6. Effect of RGCC expression on prognosis in breast cancer patients

Kaplan-Meier survival analysis was used to evaluate the Correlation between RGCC expression and prognosis of breast cancer patients. The results demonstrated that the low RGCC expression group exhibited a worse prognosis than the high RGCC expression Group as OS [HR = 0.60 (0.43–0.83), P = 0.002] and DSS [HR = 0.61 (0.40–0.94), P = 0.026]. (Fig. 6A、B). Univariate Cox analysis indicated that a low RGCC expression was significantly correlated with poor OS (HR = 0.597, 95% CI = 0.432–0.825, p = 0.002) and the different cancer stage categories: T stage(T3&T4 vs. T1&T2, P = 0.012), N stage (N1&N2&N3 vs. N0, P < 0.001), M stage (M1 vs. M0, P < 0.001), Pathologic stage (Stage III&Stage IV vs. Stage I&Stage II, P < 0.001), Age (> 60 vs. <=60, P < 0.001) and Menopause status (Post vs. Pre&Peri, P < 0.001). (Fig. 6C; Table 3). These data suggest that the expression of RGCC affects the prognosis of breast cancer patients with different pathological stages.

Table 3

Univariate and multivariate Cox regression analyses of the clinical characteristics associated with overall survival in BRCA in The Cancer Genome Atlas (TCGA).
Characteristics	Total(N)	Univariate analysis		Multivariate analysis
Characteristics	Total(N)	Hazard ratio (95% CI)	P value	Hazard ratio (95% CI)	P value
T stage (T3&T4 vs. T1&T2)	1079	1.608 (1.110–2.329)	0.012	1.944 (0.861–4.386)	0.109
N stage (N1&N2&N3 vs. N0)	1063	2.239 (1.567–3.199)	< 0.001	1.030 (0.400–2.650)	0.951
M stage (M1 vs. M0)	922	4.254 (2.468–7.334)	< 0.001	1.939 (0.510–7.377)	0.331
RGCC (High vs. Low)	1082	0.597 (0.432–0.825)	0.002	1.175 (0.601–2.296)	0.638
Pathologic stage (Stage III&Stage IV vs. Stage I&Stage II)	1059	2.391 (1.703–3.355)	< 0.001	4.351 (1.501–12.613)	0.007
Age (> 60 vs. <=60)	1082	2.020 (1.465–2.784)	< 0.001	2.606 (1.253–5.417)	0.010
Histological type (Infiltrating Lobular Carcinoma vs. Infiltrating Ductal Carcinoma)	977	0.827 (0.526–1.299)	0.410
PR status (Positive vs. Negative)	1029	0.732 (0.523–1.024)	0.068	0.686 (0.281–1.674)	0.407
ER status (Positive vs. Negative)	1032	0.712 (0.495–1.023)	0.066	0.486 (0.184–1.283)	0.145
HER2 status (Positive vs. Negative)	715	1.593 (0.973–2.609)	0.064	0.710 (0.314–1.605)	0.410
PAM50 (LumA&LumB&Her2&Basal vs. Normal)	1082	0.830 (0.388–1.773)	0.630
Menopause status (Post vs. Pre&Peri)	971	2.348 (1.428–3.860)	< 0.001	2.677 (1.008–7.113)	0.048
Anatomic neoplasm subdivisions (Right vs. Left)	1082	0.766 (0.554–1.057)	0.105
radiation_therapy (Yes vs. No)	986	0.576 (0.394–0.841)	0.004	0.470 (0.240–0.922)	0.028

7. RGCC expression and chemokines and/or chemokine receptors.

To investigate the role of ROCC in the immune microenvironment of breast cancer, the correlation between the expression levels of RGCC and immune cell chemokines and chemokine receptors was analyzed in Pan-cancer via TISIDB. The results indicated that several chemokines and chemokine receptors were significantly associated with the expression of RGCC in BRCA. (Fig. 7A、B). The above results suggest that RGCC may be related to tumor immunity. To further elucidate the relationship between RGCC and immune cell migration, we analyzed the correlation between RGCC expression and chemokine/chemokine receptors. Scatter plot results displaying that RGCC expression was significantly positively correlated with CXCL12 (r = 0.409, p < 2.2e-16), CXCL2 (r = 0.343, p < 2.2e-16), CX3CL1 (r = 0.36, p < 2.2e-16), CCL11 (r = 0.382, p < 2.2e-16), CCL4 (r = 0.323, p < 2.2e-16), CCL2 (r = 0.326, p < 2.2e-16), CXCR4 (r = 0.336, p < 2.2e-16), and CCR10 (r = 0.314, p < 2.2e-16)(Fig. 7C-J).

8. RGCC expression and immunoinhibitors and immunostimulators.

We used the TISIDB database to analyzed the correlation between RGCC and expression of immunoinhibitors and immunostimulators in 30 human cancers. (Fig. 8A、B). The analysis results showed that RGCC was significantly positively correlated with the immunoinhibitors TGFB1 (r = 0.379, p < 2.2e-16) and the immunostimulators C10orf54 (r = 0.52, p < 2.2e-16), CD40 (r = 0.392, p < 2.2e-16), CXCL12 (r = 0.409, p < 2.2e-16), CXCR4 (r = 0.336, p < 2.2e-16), IL6 (r = 0.36, p < 2.2e-16), NT5E (r = 0.345, p < 2.2e-16), TNFRSF4 (r = 0.41, p < 2.2e-16), TNFRSF8 (r = 0.38, p < 2.2e-16) and TNFSF9 (r = 0.341, p < 2.2e-16). (Fig. 8C-L). In conclusion, RGCC may be associated with tumor immunity.

9. Construction and validation of a nomogram based on RGCC expression.

In order to evaluate the prognosis of breast cancer patients, we constructed a prognostic model based on OS as an independent factor according to the expression of RGCC and several other clinical factors. Multivariate analysis was performed by integrating RGCC, Pathologic stage and M stage in the nomogram. A higher the point in the figure, the worse the prognosis. (Fig. 9A). In addition to this, we constructed a calibration curve to evaluate the performance of the RGCC nomogram. The C index of this model is 0.681 (95% CI = 0.653–0.710), suggesting that it has a certain accuracy in predicting OS in breast cancer patients. (Fig. 9B-D).

10. Identification of hub genes and their prognostic value in BRCA.

First, the most important clusters in the PPI network were identified using STRING database (Fig. 11C). We used cytoHubba (ranked by degree) to find 5 hub genes in the above PPI network, which are HLA-DMA, HLA-DRB1, HLA-DPB1, CD74 and HRA-DRA respectively (Fig. 10A). Then, we used Kaplan-Meier Plotter to evaluate the prognostic value of hub gene, and the results showed that high expression of the above 5 bub genes was significantly correlated with better prognosis (Fig. 10B-F).

11. Co-expression Gene Analysis of RGCC in BRCA

In order to explore the biological function of RGCC in BRCA, we used the LinkFinder module of LnkedOmics website to analyze the co-expression of RGCC in BRCA in TCGA database. Set | Spearman cor | >0.35 and adjusted p-value < 0.05, 1352 RGCC related genes were screened. We displayed the top 50 co-expression genes positively or negatively correlated with RGCC expression in BRCA in the heatmap(Fig. 11A、B). A total of 1666 DEGs were acquired with the threshold values of |log2 fold-change (FC)| > 1.5 and adjusted p-value < 0.05, including 97 upregulated genes and 1569 downregulated genes (Fig. 11D).

Then, we performed GO term and KEGG pathway analyses for the above 100 genes, revealing that the primary biological process (BP) contained gland development, regulation of cell-cell adhesion, embryo implantation, and positive regulation of osteoblast proliferation. The cellular component (CC) was mainly enriched in collagen-containing extracellular matrix, extrinsic component of membrane, extrinsic component of plasma membrane and cavella. The molecular function (MF) was primarily involved in insulin-like growth factor binding (Fig. 12B).

In order to screen the genes associated with survival prognosis, 1352 genes with the highest RGCC correlation were crossed with 2691 up-regulated genes associated with survival in BRCA, and 105 genes associated with RGCC and BRCA survival were detected at the crossing points (Fig. 12A). Next, GO functional enrichment and KEGG pathway analysis were performed on these 105 genes, showing that the primary biological process (BP) contained regulation of lymphocyte activation, positive regulation of cell adhesion and positive regulation of cell-substrate adhesion. The cellular component (CC) was mainly enriched in MHC protein complex, MHC class II protein complex and integral component of lumenal side of endoplasmic reticulum membrane. The molecular function (MF) was primarily involved in MHC protein complex binding, MHC class II protein complex binding and peptide antigen binding. The KEGG pathway enrichment was mainly related to Hematopoietic cell lineage, Viral myocarditis, and Asthma (Fig. 12C).

12. The expression of RGCC was detected by scRNA sequence analysis

In this study, three single-cell data sets were introduced and scTIME Portal was used to analyze the expression of RGCC. Figure 13A is the histogram of RGCC expression in each immune cell in GSE114725, Fig. 13B is the UMAP of the subcell population identified by this data set, and Fig. 13C is the corresponding expression of RGCC in each subcell population. The above results suggest that RGCC is highly expressed in Macrophage. Next, we used the CancerSEA website to verify the potential function of RGCC in two single-cell datasets. In GSE75367, the expression of RGCC was significantly positively correlated with Inflammation, Angiogensis and Differentiation, and negatively correlated with DNA repair and DNA damage (Fig. 14A). In the dataset GSE86978, the expression of RGCC has positive correlation with Quiescence and Angiogensis, and negative correlation with Metastasis (Fig. 14B). In addition, T-SNE diagram showed the RGCC expression profile of BRCA at the single-cell level (Fig. 14C、D).

The prognosis and quality of life of patients have improved in recent years as a result of the development of a molecular classification, diagnosis, and treatment model for breast cancer, as well as the further refinement of therapeutic drugs; however, advanced and middle-stage breast cancer with metastases remains the leading cause of death among women.[13]. Therefore, it is imperative that we discover new biomarkers for early diagnosis and treatment, as well as the mechanism to prevent breast cancer lymph node and distant metastasis. As the cancer treatment framework evolves, immunotherapy is progressively recognized as an effective breast cancer treatment strategy.[14].Our study showed significant differences in the expression of RGCC in multiple tumors and normal tissues. Compared with the normal group, the mRNA and protein expression levels of RGCC in the breast cancer group were lower. In addition, BC patients with high RGCC mRNA expression had a better prognosis. we also found that RGCC mRNA expression was significantly correlated with molecular and histological subtypes, but not with N stage, M stage, and PR/ER. ROC curve suggested that RGCC had high diagnostic value. KEGG analysis showed that genes co-expressed with RGCC were involved in cell-cell adhesion.

In this study, two GO/KEGG analyses showed that RGCC was related to cell adhesion, regulation of lymphocyte activity, and formation of MHC protein complex. cell adhesion is a process of similar cells aggregating to form cell mass or tissue on the basis of cell recognition, which plays an important role in mediating many pathophysiological processes such as inflammation, immunity, atherosclerosis, tumor invasion and metastasis, immune response and tissue injury healing. There are cell-cell adhesion and cell-ECM adhesion. RGCC has been reported to reduce the expression of ICAM-1(intercellular adhesion molecule-1) and VCAM-1(vascular cell adhesion molecule-1) in endothelial cells in vivo and in vitro, leading to a decrease in TNF-α(tumor necrosis factor-α) -induced monocyte-endothelial cell interactions[15].At present, there are no reports on the biological process of RGCC in breast cancer cell adhesion mediated metastasis. Combined with the GO analysis results, we speculate that RGCC may mediate the tumor cell adhesion process, and thus reduce the invasion and metastasis of tumor cells. This part will be verified in subsequent basic experiments. As a complement response gene, RGCC and CSb-9 can stimulate a variety of cells to produce oxygen free radical lipids and other inflammatory mediators mediating inflammation, and play an important role in the immune response process[16].Other studies believe that RGCC is a new key factor regulating the classical activation of macrophages, which can improve the phagocytic function of macrophages through PKC pathway. RGCC can interact with NF-κB protein and promote the expression of iOS and IL-1β by binding to promoters[17, 18].The enrichment analysis in this study suggests that RGCC may mediate MHC complex formation. MHC (Major histocompatibility complex) is an important molecule in antigen recognition and plays a key role in antigen presentation and immune signaling. There are two main types of MHC molecules, Class I and Class II. More and more studies have shown that the expression of MHC class I and Class II may be biomarkers to guide the treatment of immune checkpoint inhibitors in a variety of cancer types[19].Studies have shown that abnormal expression of MHC-II pathway in triple negative breast cancer tumor cells can trigger an anti-tumor immune response, which reduces the recurrence rate of patients. Increased expression of MHC-II or related pathway components in tumor cells is associated with better prognosis and enhanced anti-tumor immunity[20].Research in this area is still in its infancy. Existing research on the specific mechanism of RGCC and MHC-related immune response is limited. Combined with the experimental results of this study, we have reason to believe that RGCC is closely related to the formation of MHC. The results provide a basis for further research on RGCC and immunotherapy and drug resistance of breast cancer.

After that, we constructed the interaction network with 105 intersection genes through the STRING database, and finally identified 5 hub genes, namely, HLA-DMA, HLA-DRB1, HLA-DPB1, CD74 and HLA-DRA. Four of them are HLA genes, which are a class of genes on the short arm of chromosome 6, encoding MHC protein. Studies have analyzed 1182 immune-related genes, and the results show that most HLA genes, especially HLA-DMA and HLA-DRA, are negatively correlated with ER-negative breast cancer recurrence[21]. In this study, increased expression of four HLA genes was associated with good prognosis, which was consistent with the findings in the previous literature. CD74 is expressed by breast tumor cells and several immune cells, and is an important part of the functional presentation of MHCII restricted antigens. As a component and cytokine receptor of MHC Class II antigen presentation pathway, CD74 is a key part of anti-tumor immunity[22–24]. Studies have shown that CD74 expression is associated with better prognosis of basal-like subtype invasive breast cancer[25]. This result is consistent with the KM curve in this study. In summary, the above 5 key genes are related to immunity and participate in antigen presentation process, and the functions of the above key genes are consistent with the GO/KEGG enrichment results, providing further evidence for the involvement of RGCC in tumor immunity.

Tumor tissue does not simply contain tumor cells, but is composed of various types of cells, including stromal cells, fibroblasts, and immune cells, which constitute the tumor microenvironment (TME). The heterogeneity of TME profoundly affects the biological behavior of tumor cells and the overall tissue response to anti-tumor drugs[26, 27]. This study is the first to investigate the correlation between the expression of RGCC and the immune infiltration of breast cancer. The results showed that the overexpression of RGCC was positively correlated with iDC, Macrophages, Neutrophils, CD8 T cells and Th1 cells, but negatively correlated with Th2 cells. iDC is closely related to the occurrence and development of tumors. It can monitor and kill tumors by recognizing specific antigen of tumor cells and presenting its signal to T cells with killing effect. Most solid tumors have a good prognosis if more iDC infiltrates in them[28]. It has been documented that macrophages are directly or indirectly involved in several key features of malignant tumors, including angiogenesis, invasion, metastasis, and the regulation of tumor microenvironment, and that specific subgroups of macrophages may have unique roles in tumor progression and anti-tumor immunity[29]. In this study, the high expression of RGCC showed a high correlation with macrophage infiltration, which was consistent with the antitumor effect. Neutrophils can also indirectly inhibit carcinogenesis by creating an anti-tumor microenvironment and experimental evidence suggests that neutrophils have early anti-tumor effects[30, 31]. Th1 cells contribute to the anti-tumor immune response, and CD8 + T cells are the main drivers of anti-tumor immunity, targeting the killing of tumor cells. Th1 cells play an important auxiliary role in this process[32]. The results of this study are consistent with those reported in previous literatures. These results suggest that overexpression of RGCC may influence the progression and prognosis of breast cancer by regulating the level of microenvironmental immune cells.

Immunotherapy is considered the most promising cancer treatment. A variety of cancer immunotherapies, including tumor vaccines, antibodies, immune checkpoint inhibitors and small molecule inhibitors, have made some progress[33]. In this paper, we analyzed the correlation between the expression level of RGCC and the expressions of immune cell chemokines and chemokine receptors in breast cancer using TISIDB database. Variation with literature reports the CCL2, CCL4 may help predict the progression and metastasis of breast cancer[34]. CCR10 promotes invasion and migration of breast cancer cells by activating the ERK1/2/MMP-7 signaling pathway[35]. CXCR4 has been reported to play an important role in cell survival, proliferation, migration, and metastasis of several cancers, including breast cancer. The CXCR4/CXCL12 complex makes CXCR4 a unique molecular target for breast cancer prevention and treatment[36]. It was also found that CX3CL1 overexpression increased NK cell-mediated cytotoxicity in vitro, making it a potential predictive biomarker[37].One study found that produced by breast cancer cells activated fibroblasts chemokines CCL11 accelerated the growth of breast cancer cells, and induced the drug resistance and metastasis[38]. The above results explain how RGCC regulates immune infiltration in breast cancer.

Single-cell transcriptome sequencing is a key technique to analyze the potential functions of candidate molecules at the single-cell level and has been widely used to study the tumor immune microenvironment in recent years[39]. In this study, it was proved that RGCC was associated with immunity through immunoinfiltration analysis and GO/KEGG enrichment analysis. Then, single-cell data sets were introduced to verify the expression and function of this gene. Through the analysis of three single-cell data sets, the results showed that RGCC was highly expressed in macrophages, which was consistent with the results of immunoinfiltration. In addition, functional analysis showed that this gene was significantly positively correlated with Angiogensis, and negatively correlated with the damage and repair of already RNA. Studies have shown that the ability of tumor invasion and metastasis is closely related to tumor Angiogensis[40]. According to the above research results, RGCC may be related to Angiogensis and DNA damage and repair, which provides the direction for further research.

we demonstrated that RGCC is differentially expressed at various levels in breast cancer tissues and normal breast tissues, and that RGCC has a high diagnostic sensitivity. By modulating cell adhesion, MHC immune complex formation, and immune infiltration, RGCC may affect the progression and prognosis of breast cancer. It could function as a novel biomarker for prognosis. In spite of this, this study still has the following deficiencies: first, there is no further experimental verification; Secondly, there is a lack of relevant clinical research, which cannot be combined with clinical information for analysis; Finally, there may be batch differences that cannot be avoided or removed when the data set is analyzed. In the subsequent study, we will address the aforementioned shortcomings and provide additional evidence for RGCC as a breast cancer biomarker.

1. Data Collection and Processing

The messenger RNA (mRNA) expression data (including 1226 BRCA samples and 113 adjacent nontumor samples) and clinical information were downloaded from TCGA database (https://cancergenome.nih.gov). We also downloaded the following gene expression profiles from the GEO: GSE35525. The Human Protein Atlas (HPA) database (http://www.proteinatlas.org/) and the UALCAN (https://ualcan.path.uab.edu/) were used to verify the expression of RGCC in BRCA at the protein level.

2. Survival Analysis

The Kaplan-Meier method with the log-rank test was used for survival analysis, and the cut-off value was set at the median expression level of MCTS1. Univariate and multivariate Cox regression analyses were used to assess the effect of clinical variables on patient outcomes. The prognostic variables p < 0.1 in the univariate Cox regression analysis were entered into multivariate Cox regression analysis. The R package ggplot2 was used to visualize the forest map.

3. Immune Infiltration Analysis

A total of 24 immune cells were used to calculate the level of immune infiltration, and the relative enrichment score of these immune cells in breast cancer was assessed by ssGSEA, which was accomplished using the R package GSVA[41, 42]. The correlation between the expression of RGCC and these immune cells was investigated using the Spearman’s correlation analysis, and the differences in the level of immune infiltration between the high and low RGCC expression groups were evaluated using the Wilcoxon ranksum test.

4. Protein–Protein Interaction Network Analysis

The PPI network was constructed using the online STRING database (http://string-db.org/) for 105 intersection genes and visualized using Cytoscape software (version 3.8.2). Next, CytoHubba, a plug-in in Cytoscape software, is utilized to identify the top five hub genes of the network.

5. Establishment and Evaluation of the Nomograms for BRCA Survival Prediction

In this study, we selected all independent clinicopathological prognostic factors from Cox regression analysis and constructed a contingency table to evaluate the 1-, 3-, and 5-year overall survival (OS) probability of LUAD patients. By comparing the predicted probability of the line chart with the observed actual probability through a calibration curve, the accuracy of the line chart was verified. The overlapping reference lines show that the model is accurate. Receiver operating characteristic (ROC) analysis was used to compare the prediction accuracy of the line chart of the combined model and the line chart of various clinicopathological prognostic factors.

6. Functional Enrichment Analysis

To understand the biological processes and pathways that RGCC may participate in, we conducted the following analysis. RGCC related genes were obtained from TCGA database, and BRCA prognostic data were obtained from a cell article[43]. 1247 RGCC related genes and 105 genes intersecting with prognostic genes were enriched by Gene Ontology (GO) [including biological processes (BP), cellular components (CC), and molecular function (MF)] and KEGG pathway analyses using the R package clusterProfiler and visualized by the R package ggplot2. A corrected p < 0.05 was determined to be statistically significant.

7. TISIDB Database Analysis

TISIDB (http://cis.hku.hk/TISIDB/) is an integrated knowledge base portal that plays a vital role in detecting interactions between tumors and the immune system[44]. In order to further study the RGCC immune correlation in breast cancer, we use TISIDB "immune controller module in the database to analyze and evaluate RGCC expression and immune checkpoint of the correlation between the genetic level. We further evaluated the chemokine/chemokine receptor expression levels of RGCC through the chemokine module of the website.

8. Single cell sequencing

Based on scTIME Portal database, the expression of RGCC in single cell dataset GSE114725 [45]was verified and visualized. Subsequently, we analyzed the correlation between RGCC expression and different tumor functions in the CancerSEA website GSE75367[46] and GSE86978[47]. T-SNE diagram shows the expression profile of RGCC in individual cells in the TCGA sample.

9. Statistical Analysis

All statistical analyses were conducted using R (version 4.2.1). The Wilcoxon rank-sum test and paired sample t-test were used to assess statistical significance for the expression of RGCC in the non-paired and paired tissues, respectively. The Wilcoxon rank-sum test and logistic regression were used to assess the correlations between clinical features and RGCC expression. All of the tests were two-sided, and p values < 0.05, which were regarded as statistically significant.

Ethical Approval

The study involving human material were performed in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Affiliated Hospital of Inner Mongolia Medical University.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

Huiwen Zhang wrote the main manuscript text. All authors prepared Figs. 1-14. All authors read and approved the final manuscript.

Funding

None.

Availability of data and materials

The open-access datasets are available through the following URL: https:// cancergenome.nih.gov and https://www.ncbi.nlm.nih.gov/gds. All data generated or analyzed during this study are available from the corresponding author on reasonable request.

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No competing interests reported.

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RGCC is a Prognostic Biomarker and Correlates with Immune Infiltrates in Breast Cancer

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