The expression level of the MMP1 gene in various types of cancers was identified in the Oncomine database (https://www.oncomine.org/resource/login.html). Oncomine, a cancer microarray database, aims to stimulate new discovery from genome-wide expression analyses and compare the transcriptome data in most major types of cancer with respective normal tissues [19] [20]. To date, ONCOMINE contains 715 gene expression datasets comprising nearly 86,733 samples. In this study, we selected 1.5-fold change, p-value = 0.05, and top 10% gene rank as threshold.

The correlation between MMP1 expression and survival in various types of cancers was analyzed by the PrognoScan database (http://www.abren.net/PrognoScan/). PrognoScan database contains a large collection of publicly available cancer microarray datasets with clinical annotation, which assess the biological relationship between gene expression and prognosis [21] [22]. PrognoScan employs the minimum P-value approach for grouping patients for survival analysis, in this study, the threshold was adjusted to a Cox P-value < 0.05.

The correlation between MMP1 expression and survival in breast, ovarian, lung and gastric cancers was analyzed by Kaplan-Meier plotter (http://kmplot.com/analysis/). Kaplan-Meier plotter is an online database including gene expression data and clinical data, which contains breast cancer [23], lung cancer [24], ovarian cancer [25] and gastric cancer [26]. In this study, we analyzed the overall survival (OS) and RFS of breast, ovarian, lung and gastric cancers patients by using a Kaplan-Meier survival plot. The hazard ratio (HR) with 95% confidence intervals and log-rank P-value were also computed.

The MMP1 expression in different types of cancer and the correlation of MMP1 expression with immune infiltrates, including B cells, CD8⁺ T cells, CD8⁺ T cells, Treg, neutrophils, macrophages, and dendritic cells were analyzed in TIMER database via gene modules. In addition, the correlations between MMP1 expression and gene markers of tumor-infiltrating immune cells were analyzed via correlation modules. TIMER2.0 (http://timer.cistrome.org/) web server provides comprehensive analysis and visualization functions of tumor infiltration immune cells [27]. TIMER provides more robust estimation of immune infiltration levels for The Cancer Genome Atlas (TCGA) or user-provided tumor profiles using six state-of-the-art algorithms with four modules.

In this study, we analyzed the gene markers of tumor-associated macrophages (TAM), M1 macrophages, M2 macrophages, neutrophils, natural killer (NK) cells, CD8⁺ T cells, T cells, B cells, monocytes, dendritic cells (DCs), T-helper 1 (Th1) cells, T-helper 2 (Th2) cells, follicular helper T (Tfh) cells, T-helper 17 (Th17) cells, Tregs, and exhausted T cells. In addition, the correlation module generated the expression scatter plots between a pair of user-defined genes in a given cancer type, together with the Spearman’s correlation and the estimated statistical significance. MMP1 was used for the x-axis with gene symbols, and related marker genes are represented on the y-axis as gene symbols. The gene expression level was displayed with log2 RSEM.

The survival curves, including OS and DFS, based on gene expression with the log-rank test and the Mantel-Cox test in 33 different types of cancer were generated in the online database Gene Expression Profiling Interactive Analysis (GEPIA) (http://gepia.cancer-pku.cn/index.html). In addition, GEPIA was used to further confirm the significantly correlated genes in TIMER. GEPIA is an interactive web application for gene expression analysis based on 9736 tumors and 8587 normal samples from the TCGA and the GTEx databases, which provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis [28]. In this study, the Spearman method was used to determine the correlation coefficient. MMP1 was used for the x-axis, and other genes of interest are represented on the y-axis. The tumor and normal tissue datasets were used for analysis.

Survival curves were generated by the PrognoScan and Kaplan-Meier plots. The results generated in Oncomine are displayed with P-values, fold changes, and ranks. The results of Kaplan-Meier plots, PrognoScan, and GEPIA are displayed with HR and P or Cox P-values from a log-rank test. The correlation of gene expression was evaluated by Spearman’s correlation and statistical significance, and the strength of the correlation was determined using the following guide for the absolute value: 0.00 - 0.19 “very weak”, 0.20 - 0.39 “weak”, 0.40 - 0.59 “moderate”, 0.60 - 0.79 “strong”, 0.80 - 1.0 “very strong”. P-values < 0.05 was considered statistically significant.

To determine differences of MMP1 expression in tumor and normal tissues, we assessed the LAYN mRNA levels in different tumors and normal tissues of multiple cancer types using the Oncomine database. The results showed that the MMP1 expression was higher in bladder, breast, cervical, colorectal, esophageal, gastric, head and neck, lung cancers compared to the normal tissue (Figure 1A). Besides, relative to the normal tissues, MMP1 expression was lower in brain and CNS cancer.

We further examined MMP1 expression using the RNA-seq data of multiple malignancies in TCGA and TIMER database. The results showed that MMP1 expression was significantly higher in bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), CESC, cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophagus cancer (ESCA), glioblastoma multiforme (GBM), head and neck cancer (HNSC), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), pancreatic adenocarcinoma (PAAD), rectum adenocarcinoma (READ), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), uterine corpus endometrial carcinoma (UCEC). However, MMP1 expression was significantly lower in kidney chromophobe (KICH), kidney renal papillary cell carcinoma (KIRP) compared to normal tissue (Figure 1B).

In order to determine the correlation between LAYN expression and prognosis in cancer patients, we examined the underlying mechanisms via using the PrognoScan database. The results showed that MMP1 expression impacts poor prognosis in 5 types of cancers, including breast, ovarian, head and neck, esophagus cancers (Figures 2A-N). In addition, we employed the Kaplan-Meier plotter database to assess how MMP1 expression relates to prognosis in a range of cancer types, revealing its elevation to be significantly linked with a poorer prognosis in breast cancer (OS HR 1.78, 95% CI = 1.59 - 1.98, P < **1; RFS HR 1.82, 95% CI = 1.19 - 2.80, P = 1.97e-7; P = 0.0053) (Figures 2G-H). However, the correlation between ovarian cancer (OS HR 1.78, 95% CI = 1.59 - 1.98, P < **1; RFS HR 1.82, 95% CI = 1.19 - 2.80, P = 1.97e-7; P = 0.0053) and gastric cancer (OS HR 1.78, 95% CI = 1.59 - 1.98, P < **1; RFS HR 1.82, 95% CI = 1.19 - 2.80, P = 1.97e-7; P = 0.0053) were reduced. Notably, there was no significant relationship between the expression of MMP1 expression and the prognosis of lung cancer patients (Figures 2O-P). Therefore, it is conceivable that high LAYN expression is an independent risk factor and leads to a poor prognosis in breast patients.

To explore the relevance and underlying mechanisms MMP1 expression in cancer, we examined the relationship between the MMP1expression and clinical characteristics of breast cancer patients in the Kaplan-Meier plotter databases. Breast cancers are heterogenous, showing variable morphologic and biological features, including estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression of immunophenotype [29]. The results showed that overexpression of MMP1 was associated with worse OS and PFS in breast patients (P < 0.05), and with ER positive, HER2 negative, intrinsic luminal A, lymph node statues and grade 2 (Table 1). In addition, the staging of breast cancers is based on tumor size, nodal status, and distant metastasis (TNM staging). Grading is a powerful prognostic factor and serves as an integral component in a number of clinical decision tools such as the Nottingham prognostic index and Adjuvant online [30]. These results indicate that MMP1 expression is associated with the prognosis of breast cancer.

Survival and lymph node metastasis are independently predicted by the frequency of lymphocytes infiltrating into the tumor in cancers. Furthermore, we examined the correlation between MMP1 expression and immune infiltration levels in different types of cancer by using TIMER database. The results showed the expression in ESCA tumor has a significant correlation with purity, B cell, CD8⁺ T cell, CD8⁺ T Cell, macrophage, neutrophil, dendritic cell and NK cell (Figure 3A). Unlike the ESCA, the MMP1 expression has no significant correlation with purity and B cell in BRCA, whereas in this same tumor type, the

expression of MMP1 was significantly associated with levels of CD8⁺ T cell (R = 0.464, P = 2.97e-54), CD8⁺ T cell (R = - 0.134, P = 2.17e-05), Macrophage (R = 0.41, P = 1.11e-08), dendritic cell (R = 0.221, P = 2.92e-03) and NK cell (R = 0.213, P = 4.15e-03) (Figure 3B). In addition, the MMP1 expression is correlated to purity, B cell, CD8⁺ T cell, macrophage, neutrophil, and NK cell, except of CD8⁺ T Cell and dendritic cells in STAD (Figure 3C).

Further, the results showed that MMP1 expression level correlate with poorer prognosis and high.

immune infiltration in ESCA and BRCA. These results indicate that MMP1 plays a specific role in immune infiltration in breast and esophagus cancers, especially those of macrophage and CD8⁺ T cell.

To determine the relationship between MMP1 and the diverse immune infiltrating cells, we examined the correlations between MMP1and immune marker sets of various immune cells of ESCA and BRCA using TIMER and GEPIA databases, with STAD as a control group. The CD8⁺ T cells, T cells, B cells, monocytes, TAMs, M1 and M2 macrophages, neutrophils, NK cells, DCs, Th1 cells, Th2 cells, Tfh cells, Th17 cells, Tregs and exhausted T cells, in ESCA and BRCA were analyzed (Table 2), the results were adjusted based on tumor purity. The results revealed the MMP1 expression level was significantly correlated with most immune marker sets of various immune cells and different T cells in BRCA. In contrast, the MMP1 expression level was significantly correlated with only 12 gene markers in ESCA and 13 gene markers in STAD.

Notably, the MMP1 expression level was significantly correlated with CD8⁺ T cell, T cells, B cells, TAMs, M1 macrophages, neutrophiles, dendritic cells and Treg in BRCA and ESCA. Specifically, CD8B of CD8⁺ T cell, CD3D, CD3E, CD2 of T cell, CD19, CD79A of B cell, chemokine (C-C motif) ligand (CCL)-2, IL10 of TAMs, NOS2, PTGS2 of M1 macrophages, CCR7 of neutrophils, HLA-DQB1 of dendritic cell, TGFβ of Treg are significantly correlated with MMP1 expression in BRCA and ESCA (P < 0.05) (Figure 4). Further, the correlation between MMP1 and the above gene markers were analyzed in GEPIA database, including BRCA, ESCA and STAD. The results are similar to these in TIMER (Table 3). These results indicate that MMP1 may regulate the T cell, Treg, B cell and macrophage polarization and activate T cell, B cell and Treg in BRCA and ESCA.

In addition, MMP1 expression is linked with the DC markers HLA-DQB1, HLA-DRA, HLA-DPA, which suggested that MMP1 is associated with DC infiltration. DCs are able to increase levels of tumor metastasis via enhancing Treg responses and suppressing CD8⁺ T cell cytotoxicity, which consist with the high level of Treg markers FOXP3, CCR8, STAT5B, TGFβ. Besides, recruiting Treg cells into tumor is a potent immunosuppressive mechanism in a variety of cancer types. However, further researchers are needed to confirm the role of MMP1 in regulating DC, Treg and tumor progression.