COL1A1 is a prognostic biomarker and correlated with immune infiltrates in lung cancer

Introduction

Lung cancer (LC) is one of the top ten malignant tumors and the first leading cause of cancer-related death worldwide, which is a major global health problem (Bray et al., 2018; Ferlay et al., 2019; Sung, Ferlay & Siegel, 2021). In recent years, due to air pollution, smoking and other factors, the incidence and mortality of lung cancer have increased rapidly, making it one of the most dangerous tumors to human health. Lung cancer is also the most important type of occupational cancer (Rocco et al., 2018). Studies have shown that there is a certain relationship between high-risk occupational exposure and the occurrence of LC, and such patients are more likely to have extra-pulmonary lesions, accompanied by respiratory diseases, which have more difficult to treat (Wong et al., 2019). At present, the treatment of advanced lung cancer mainly includes chemotherapy, radiotherapy, targeted therapy and immunotherapy. The advent of targeted therapy and immunotherapy has brought the treatment into the era of ‘individualized treatment’ and ‘precision treatment’. Despite great advances in diagnostic and therapeutic techniques, due to the high recurrence and metastasis, median overall survival (OS) and the 5-year survival rate of patients with LC still very low (Ferlay et al., 2019). Therefore, it is significant for enhancing the prognosis of lung tumor patients to detect prognostic marker and potential drug targets.

Suppressing the immune system checkpoint, activating T cells and B cells to kill tumor cells, provides a new idea for tumor immunotherapy. During the decade, tumor-specific immunotherapy has made significant progress, of which the research progress of the B7-CD28 family is particularly prominent (Tanaka & Sakaguchi, 2017). The B7-CD28 costimulatory pathway is one of the most important secondary signal transduction mechanisms. It is essential for maintaining a delicate balance between immunity and suppression of autoimmunity, and has great potential in tumor therapy (Schildberg et al., 2016; Sharpe & Freeman, 2002). Although, immunotherapy has made great contributions to improving the prognosis of LC patients, and tumor-specific immunotherapy has been widely used in the clinic, there are still some problems with this treatment, including adverse reactions and drug resistance (Fesnak, June & Levine, 2016). To reduce the adverse reactions of immunotherapy and overcome rapid drug resistance, experts in the field of oncology at home and abroad have made unremitting efforts. Through basic research and clinical verification, most of the current experts believe that combined immunotherapy is the main trend of cancer treatment in the future. Previous studies have shown that immune combined targeted therapy can significantly improve the overall survival (OS) and progression-free survival (PFS) of LC patients (Hellmann et al., 2018). Hence, immune combined targeted therapy should be investigated to enhance prognosis and individualized treatments.

In this study, Collagen type I alpha 1 chain (COL1A1) was detected as the prognostic marker and potential drug target in lung tumor by screening different expression genes (DEGs) based on GEO datasets. Based on GSE31210, GSE3141 and TGA databases, it is shown that COL1A1 is closely related to the prognosis of lung cancer patients. Moreover, we investigated the correlation of COL1A1 with the B7-CD28 family in the lung tumor (Fig. S1 ). The findings suggest COL1A1 played a crucial part in the prognostic of lung tumor as well as reveal the potential mechanism between COL1A1 and tumor-immune, which lay the foundation for clinical research and immune targeted therapy.

Materials and Methods

Results

Identification of DEGs in lung cancer

After searching the appropriate datasets in GEO database according to the eligibility criteria, three genome-wide gene expression datasets (GSE32867, GSE43458 and GSE116959) regarding lung cancer were chosen as discovery set. As shown in the volcano plot, DEGs in those datasets were identified separately with a threshold of adjust p < 0.05 and —log2FC— >1 (Figs. 1A–1C). In GSE32867 dataset, a total of 1263 DEGs were screened out. In the GSE43458 dataset, there were 895 DEGs in lung cancer tissues compared with normal control samples. For GSE116959 dataset, there were 2258 DEGs were identified. After the DEGs in three datasets were intersected, we totally identified 340 DEGs that were commonly appeared in three datasets, including 268 downregulated genes (log2FC < -1) and 72 up-regulated genes (log2FC > 1) in the lung tissues (Fig. 1D, Table S1), which were selected for subsequent analyses.

Construction and analysis of PPI network

In order to understand the mutual interaction of the identified DEGs, the STRING database and Cytoscape software were used to constructed a PPI network, which shown the complicated interactions among multiple genes (Fig. 2A). The most important module was obtained using Cytoscape (MCODE plug-in) (Fig. 2B), which was performed functional enrichment analysis by the Database for Annotation, Visualization and Integrated Discovery (DAVID). The result indicated the module participated in nucleotide binding, cell cycle and cell division (Table 1), which are closely related to tumor occurrence and progression. This further highlights the importance of the 340 common DEGs in tumor treatment. Then, the top ten genes, including SPP1, TIMP1, MMP9, COL1A1, PPARG, EDN1, CAV1, PECAM1, VWF and CD34, were identified as hub genes according to the degree score generated by CytoHubba plug-in (Fig. 2C, Table 2). The biological process analyses of hub genes were analyzed using BINGO plug-in, showing hub genes participate in regulation of nitric oxide biosynthetic process, regulation of vascular smooth muscle cell proliferation, embryo implantation, response to hyperoxia and response to testosterone (Fig. 2D). These results indicate that the 10 hub genes play a vital role in the regulation of biological processes and related pathways, and may become an important target for the treatment of LC.

Table 1:

Significantly enriched GO terms of the common DEGs (BP, biological process; CC, cellular component; DEG, differentially expressed gene; GO, Gene Ontology; MF, molecular function).

Gene symbol	Full name	Function
MMP9	Matrix Metallopeptidase 9	Pathways: IL-17 Family Signaling Pathways and TNF signaling pathway; GO: endopeptidase activity and hydrolase activity.
VWF	Von Willebrand Factor	Pathways: RET signaling and Complement and coagulation cascades; GO: immunoglobulin binding and collagen binding.
PECAM1	Platelet And Endothelial Cell Adhesion Molecule 1	Pathways: NF-kappaB Signaling and Innate Immune System; GO:protein binding and protein homodimerization activity
CD34	CD34 Molecule	Pathways: NF-kappaB Signaling and Cell adhesion molecules (CAMs);GO: transcription factor binding and carbohydrate binding.
COL1A1	Collagen Type I Alpha 1 Chain	Pathways: VEGF Signaling Pathway and ERK Signaling; GO: extracellular matrix structural constituent and extracellular matrix structural constituent conferring tensile strength.
SPP1	Secreted Phosphoprotein 1	Pathways: ERK Signaling and TGF-beta Receptor Signaling; GO: cytokine activity and integrin binding.
TIMP1	TIMP Metallopeptidase Inhibitor 1	Pathways: GPCR Pathway and CREB Pathway; GO: enzyme inhibitor activity and peptidase inhibitor activity
EDN1	Endothelin 1	Pathways: RET signaling and TNF signaling pathway; GO: cytokine activity and signaling receptor binding.
PPARG	Peroxisome Proliferator Activated Receptor Gamma	Pathways: AMP-activated Protein Kinase (AMPK) Signaling and PPAR signaling pathway; GO: activating transcription factor binding and DNA-binding transcription factor activity.
CAV1	Caveolin 1	Pathways: RET signaling and ALK1 signaling events ; GO: peptidase activator activity and patched binding.

DOI: 10.7717/peerj.11145/table-1

Table 2:

Hub genes with higher degree of connectivity.

Term	Description	Count in gene set	P-Value
Molecular function
GO:0000166	nucleotide binding	6	1.05E−02
GO:0017076	purine nucleotide binding	5	3.22E−02
GO:0032553	ribonucleotide binding	5	2.78E−02
GO:0032555	purine ribonucleotide binding	5	2.78E−02
GO:0001882	nucleoside binding	5	1.78E−02
GO:0001883	purine nucleoside binding	5	1.74E−02
GO:0030554	adenyl nucleotide binding	5	1.65E−02
GO:0032559	adenyl ribonucleotide binding	5	1.38E−02
GO:0005524	ATP binding	5	1.31E−02
Cellular Component
GO:0043228	non-membrane-bounded organelle	9	6.44E−04
GO:0043232	intracellular non-membrane-bounded organelle	9	6.44E−04
GO:0005856	cytoskeleton	9	6.05E−06
GO:0015630	microtubule cytoskeleton	9	4.68E−09
GO:0044430	cytoskeletal part	8	7.06E−06
GO:0031974	membrane-enclosed lumen	6	2.09E−02
GO:0043233	organelle lumen	6	1.93E−02
GO:0070013	intracellular organelle lumen	6	1.76E−02
GO:0031981	nuclear lumen	6	7.44E−03
GO:0005829	cytosol	5	2.92E−02
GO:0005819	spindle	5	7.74E−06
Biological Process
GO:0007049	cell cycle	7	7.05E−05
GO:0022402	cell cycle process	6	1.83E−04
GO:0022403	cell cycle phase	6	4.17E−05
GO:0000278	mitotic cell cycle	6	2.43E−05
GO:0000279	M phase	6	1.38E−05
GO:0051301	cell division	5	1.87E−04
GO:0048285	organelle fission	5	7.01E−05
GO:0000087	M phase of mitotic cell cycle	5	6.43E−05
GO:0000280	nuclear division	5	5.99E−05
GO:0007067	mitosis	5	5.99E−05

DOI: 10.7717/peerj.11145/table-2

The correlation and nomograms of key member in DEGs

Hierarchical clustering of the hub genes was performed based on TCGA database (Fig. 3A), indicating the expression pattern across 10 genes, which are consistent with the results of GEO database difference analysis. Compared with normal tissues, the expression of SPP1, TIMP1, MMP9, and COL1A1 is up-regulated in LC tissues; the expression of PPARG, EDN1, CAV1, PECAM1, VWF and CD34 is down-regulated. Furthermore, we analyzed the correlation of the expression of hub gene in lung cancer tissues, of which PECAM1, VWF, CD34, COL1A1, MMP9 and TIMP1 are closely related (Fig. 3B). In order to investigate the prognostic value of hub genes, the Lasso regression was used to screen potential hub genes, indicating that SPP1, TIMP1, COL1A1, PPARG, EDN1, CAV1 and PECAM1 were essential for modeling (Figs. S2A–S2B). In addition, the ROC were applied to evaluate the accuracy and discrimination of modeling, showing low accuracy (Area Under Curve (AUC) of 1-year survival: 0.603; AUC of 5-year survival: 0.577) (Fig. S2C), indicating some potential hub genes expression may not be closely related to the survival of lung cancer patients. Then, the Risk factor association diagram represents the difference in population proportion, survival time (alive/death), and gene expression distribution between high-risk and low-risk groups distinguished by the cox risk model (Fig. S2D).

Survival analysis of potential hub genes

Because of the poor predictive value of model, we performed correlation analysis with the clinical correlative of lung cancer outcomes in GEO, EGA, and TCGA lung cancer data sets to determine whether the potential hub genes in lung tumor have clinical relevance. The hub genes were performed Univariate Cox regression and Logrank regression based on TCGA lung tumor dataset, indicating COL1A1 and SPP1 is of great significance to prognosis in both regression analyses (Figs. 4A–4B). Besides, the mutation status of hub genes was assessed in LUAD and LUSC based on TCGA datasets, of which COL1A1 mutation rate is higher (Figs. 4C–4D). Therefore, COL1A1 may be the attractive target in the treatment of carcinoma, which participated in the metastasis and was a reliable biomarker and putative therapeutic target in many cancers (Liu et al., 2018; Ma, Chang & Bamodu, 2019; Oleksiewicz et al., 2017). Besides, Kaplan–Meier Plotter also showed a high COL1A1 expression closely correlated with the over-all survival and the progression-free survival of lung cancer patients (Figs. 5A–5B). In order to verify the diagnostic and prognostic value of COL1A1, COL1A1 expression in lung tumors and matched normal tissues from TCGA dataset was analyze which indicated that COL1A1 expression is significantly higher expression in lung tumor tissues than in matched normal samples (Fig. 5C). To investigate the potential significance of COL1A1 expression, we divided the TCGA lung cancer cohort (n = 1003) into COL1A1-high and low expression groups that were performed to evaluate the prognostic value. Patients with high COL1A1 expression had an obviously shorter survival time than those patients with low expression (Fig. 5D). Similar results were obtained with the GEO dataset, including of GSE31210 and GSE3141 datasets (Figs. 5F–5G, all P < 0.01). In addition, the area under the curve (AUC) of COL1A1 from TCGA data sets was 0.8672 (Fig. 5E), supporting COL1A1 as a diagnostic and prognostic marker in lung carcinoma.

The correlation between COL1A1 and the inflammatory activities

In view of the strongly association between COL1A1 expression and prognosis in lung tumor, we theorized that COL1A1 expression may be regulated by inflammatory responses, contributing to affect the survival rates of tumor patients. The human body needs to control the immune response to obtain the best protective immunity and tolerance. Therefore, TIMER was used to assessed the correlations of COL1A1 expression with immune infiltration levels in LUAD and LUSC. The results indicate that COL1A1 expression has correlations with tumor purity and significant correlations with CD4+ T cells, Macrophage, Neutrophil and Dendritic cell in LUAD and LUSC (Fig. S3). Besides, costimulatory or co-suppressive signals mediated by B7 family molecules and corresponding receptors are involved in the immune regulation of various malignant tumors, and affect tumor progression and metastasis, which is closely related to the malignant degree and prognosis of the disease. Various immunotherapies targeting monoclonal antibodies against B7 family molecules have become the focus of current research (Schildberg et al., 2016). To further study the relationship between COL1A1 and immunity, the coexpression between COL1A1 and the B7-CD28 ligand–receptor family was analyzed in lung cancer and normal lung tissues, including CD274, CD80, CD86, CD276, CD273, CD275, B7-H4, B7-H5, CD28, B7-H7, CD152, CD279, CD278, TLT-2 and NKp30 (Fig. 6A), based on TCGA dataset. The result demonstrated that COL1A1 exhibited a significant co-expression trend with CD276 and B7-H4. Recent studies have shown that CD276 and B7-H4 have a co-inhibitory role on T-cells, participating in tumor cell immune evasion, whose overexpression has a significant relationship with poor prognosis in tumor patients (Altan et al., 2017), which was consistent with the prognostic value of COL1A1. Furthermore, we investigated the correlation between COL1A1, CD276 and B7-H4 in TCGA lung tumor cohort, which indicated there was a close positive correlation between COL1A1 and CD276 (R = 0.39, P < 2.2e − 16) (Figs. 6B–6C). To validate the positive correlation, TIMER and GEPIA were also used to evaluate the correlation between COL1A1 and CD276 in LUAD and LUSC, with R value of 0.561, 0.361, 0.48 and 0.35, respectively (P < 0.001) (Figs. 6D–6H).

COL1A1 is positively correlated with CD276

In order to assess COL1A1 correlation with CD276, we analyzed COL1A1 and CD276 expression in tumor sites and the adjacent non-tumor samples (Fig. 7A). We found that COL1A1 and CD276 showed significantly higher expression in tumor sites than in the adjacent non-tumor samples (Figs. 7C–7D). Furthermore, we divided the tumor sites into two groups according to COL1A1 expression and found that CD276 levels were positively correlated with the level of COL1A1(Fig. 7B, Fig. 7E).

Discussion

COL1A1 is involved in encoding type I collagen, which is a member of collagens family that regulate intercellular adhesion and differentiation and strengthen many tissues in the body (Marini et al., 2017). In addition, collagen is the major structural protein of extracellular matrix (ECM), which is an important part of the tumor microenvironment, and plays a vital role in tumor formation, metastasis and development (Lu, Weaver & Werb, 2012). Previous studies have shown that COL1A1 can participate in glutamine-mediated interaction between pancreatic cancer and stellate cells (Chakravarthy et al., 2018), and silencing COL1A1(siCOLIA1) inhibited the orthotopic growth of colorectal tumor in mice (Wu et al., 2020). Besides, cell experiment indicates, siCOLIA1 suppressed hepatocellular carcinoma (HCC) cells clonogenicity, motility, invasiveness and tumorsphere formation (Ma, Chang & Bamodu, 2019). These findings suggest COL1A1 plays an important role in the formation and metastasis of various tumors. However, the distinct role of COL1A1 in malignant, pre-malignant and normal tissues and its clinical significance in LC are unclear. In this study, we found that 340 genes were significantly differentially expressed in normal tissues and lung cancer tissues through genetic screening. After PPI network and Lasso regression analysis, seven genes (COL1A1, SPP1, TIMP1, PECAM1, CAV1, PPARG and CDN1) were identified as components of the risk signature to divide LC into low and high-risk groups. Kaplan–Meier Plotter also showed a high COL1A1 expression correlated with high hazard ratio (HR) for OS and PFS of lung tumor. In addition, TCGA, GSE31210 and GSE3141 datasets were used to validate the prognostic value of COL1A1, suggesting that COL1A1 played a significant part in the prognostic of lung tumor patients.

LC, the leading cause of cancer death in the worldwide, is the main part of cancer death in both men and women and accounts for a whopping 18% of all cancer deaths (Sung, Ferlay & Siegel, 2021). Emerging studies have shown Immunotherapy is a successful treatment for lung cancer. However, most adverse reactions are caused by Immunotherapy, which can be relieve by symptomatic treatment, application of glucocorticoids and related monoclonal antibodies. In addition, like other anti-tumor drugs, immunotherapy will inevitably produce drug resistance (O’Donnell, Teng & Smyth, 2019; Sharma et al., 2017). Therefore, finding new immune-related genes is crucial. The co-suppression pathway in the B7-CD28 family can provide key inhibitory signals, thereby regulating immune homeostasis and defense capabilities and protecting tissue integrity (Janakiram et al., 2017). Previous studies have shown that the immune microenvironment plays a vital role in tumorigenesis and development. Therefore, immune-related biomarkers have also attracted much attention in risk stratification and prognosis prediction (Pagni et al., 2019).

More importantly, COL1A1 expression has significant correlations with diverse immune infiltration levels and many immune marker sets in LUAD and LUSC. In addition, the study shows that COL1A1 expression is closely related with tumor purity and linked to CD4+ T cells, Macrophage, Neutrophil and Dendritic cell in LUAD and LUSC. Furthermore, the correlation between COL1A1 and the B7-CD28 ligand–receptor family was analyzed based on TCGA database, indicating there was a close positive correlation between COL1A1 and CD276, which was consistent with the prognostic value of COL1A1. Previous study shown that COL1A1 was correlated positively with the tumor infiltration levels of CD4+ T cells and macrophages in Bladder Cancer (Li et al., 2020). CD276, expressed in multiple tumor lines, tumor-infiltrating dendritic cells, and macrophages, can inhibit T-cell activation and autoimmunity (Son, Kwon & Cho, 2019). Therefore, the interaction between COL1A1 and CD276 could be a potential mechanism for the correlation among COL1A1 expression, immune infiltration and poor prognosis in LC.

Conclusions

In summary, increased COL1A1 expression correlated with poor prognosis and changed immune infiltration levels in LC, indicating COL1A1 is a potential immunity-related biomarker. This study was based on statistical analysis of bioinformatics methods, and the conclusions obtained had experimental support and multiple database verification. Thus, our study provides insights in understanding the potential role of COLA1 in tumor immunology and its use as a cancer biomarker.

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