Analysis of CAFs‑related Genes Identifies COL11A1 Associated with Lung Adenocarcinoma Diagnosis and Prognosis

doi:10.21203/rs.3.rs-1982542/v1

Download PDF

Research Article

Analysis of CAFs‑related Genes Identifies COL11A1 Associated with Lung Adenocarcinoma Diagnosis and Prognosis

https://doi.org/10.21203/rs.3.rs-1982542/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Difficulties in the treatment of lung adenocarcinoma(LUAD) are due to lack of understanding of relevant molecular mechanisms and limited potential therapeutic options. Cancer-related fibroblasts(CAFs) play an important role in the occurrence and development of cancers. Therefore, this study aimed to identify a promising molecular target associated with CAFs for the diagnosis and prognosis of LUAD.

Methods

The Cancer Genome Atlas (TCGA) LUAD dataset was used to screen out the hub genes by EPIC algorithm and Weighted Gene Co-expression Network Analysis (WGCNA). GEPIA database, Kaplan-Meier Plotter database, GSE72094, GSE75037, and GSE32863 were used to verify the differential expression and survival of hub genes. Immunohistochemistry (IHC) was used to assess the expression of COL11A1 in LUAD and adjacent normal tissues. GO/KEGG functional analyses and single-cell TISCH database were used to elucidate the underlying mechanisms of COL11A1.

Results

Based on the TCGA LUAD dataset, 13 hub genes associated with CAFs were screened out by the EPIC algorithm and WGCNA. These were ADAM12, ADAMTS12, COL11A1, COL1A1, COL1A2, COL3A1, COL5A1, COL5A2, ITGA11, LRRC15, POSTN, THBS2, THY1. Using the GEPIA database, Kaplan-Meier Plotter database, GSE72094, GSE75037, and GSE32863, we confirmed that COL11A1 was overexpression in LUAD tumor tissues and high expression of COL11A1 had a poor prognosis. Using IHC experiment data, we confirmed that the expression of COL11A1 was significantly higher in LUAD (P < 0.001). We found that COL11A1 had a mutation frequency of 18% and COL11A1 promoter hypermethylation in LUAD (P < 0.05). Using GO/KEGG analyses, we found that COL11A1 was mainly related to the biological activities of the extracellular matrix in LUAD. Finally, in the TISCH database, we found that the expression of COL11A1 was mainly secreted by CAFs in the TME rather than from LUAD cells.

Conclusion

COL11A1 may be expressed and secreted by CAFs in the TME and affect the biological behavior of proliferation, invasion, and metastasis of LUAD cells. COL11A1 may serve as a potential diagnostic and prognosis biomarker for LUAD.

COL11A1

lung adenocarcinoma

prognosis

bioinformatics

hub genes

WGCNA.

Lung cancer, including non-small cell lung cancer (NSCLC) and small cell lung cancer, has become one of the leading causes of death in both men and women worldwide¹. NSCLC is grouped into 3 histological sub-types: lung adenocarcinoma (LUAD) which is the most common form of lung cancer, lung squamous cell carcinoma, and large cell carcinoma². Owing to the development of various LUAD therapies such as surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy, the prognosis of LUAD has been greatly improved³. However, many LUAD patients are already in the advanced stage when they are initially diagnosed resulting in a short 5-year overall survival rate of patients⁴. Therefore, there is an urgent need to identify effective diagnostic and prognostic biomarkers.

The occurrence and development of tumors are affected not only by the cancer cells but also by the tumor microenvironment (TME)⁵. The TME is an ecosystem within the human body that surrounds tumors. It includes immune cells, extracellular matrix, blood vessels, and other cells, such as fibroblasts. The tumor cells and its microenvironment constantly interact, both positively and negatively. The tumor stroma in the TME has a crucial impact on tumor invasiveness by regulating extracellular matrix deposition and cancer cell metabolism⁶. Cancer-related fibroblasts (CAFs), one of the tumor stromal cells, have received extensive attention and research in recent years. Some studies have indicated that CAFs were one of the most abundant stromal cell components in TME, and played an important role in tumor invasion, angiogenesis, and extracellular matrix remodeling by promoting cell-cell interaction and secretion of pro-invasive factors^7–9. Therefore, Studying CAFs is an important entry point for us to find diagnostic and prognostic markers.

In this study, we used the immune infiltration score EPIC algorithm (http://epic.gfellerlab.org)¹⁰ and WGCNA¹¹ method to screen out hub genes associated with CAFs in the TCGA LUAD dataset. Hub genes were further validated by relevant datasets, and finally COL11A1 was identified as a potential biomarker for diagnosis and prognosis of LUAD.

Data collection and processing

Clinical information and sequencing RNA data were downloaded from The TCGA (https://portal.gdc.cancer.gov/) and Gene Expression Omnibus (GEO) databases(https://www.ncbi.nlm.nih.gov/geo/). The inclusion criteria were as follows: (a) samples diagnosed as LUAD; (b) number of samples more than 100; (c) cancer samples with clinical information including sample serial number, survival time, and survival status at least; (d) If the dataset lacks clinical data, the data can be included unless the dataset contains paired samples (tumor vs normal). The exclusion criteria were as follows: (a) lung cancer samples without adenocarcinoma; (b) cancer samples without essential clinical information; (c) samples with no expression value in over half of the genes. The basic information of the included dataset was listed in Table 1. The “biobase” package was used to normalize the data. According to the annotation information on the platform, the probes were labeled with gene symbols. When multiple probes corresponded to one gene, the average value was taken. The case where one probe corresponded to multiple genes was canceled. And then the gene expression matrix was obtained.

Differentially Expressed Genes (DEGs) and Immune Infiltration Analysis

The "Limma" package in R was used to screen DEGs between cancer and normal tissues in the TCGA LUAD dataset(|log2FC|> 1, FDR< 0.05). The " ggplot2" package in R was used to draw the volcano plot and the heatmap(top 50 upregulated and top 50 downregulated genes) of the DEGs. Next, We scored each sample in TCGA LUAD dataset for immune infiltration using the EPIC algorithm(http://epic.gfellerlab.org). After that, variance analysis of immune infiltration scores between the tumor group and normal group was performed.

Weighted Gene Co-Expression Network Construction

The R package of WGCNA was applied to find CAFs-related modules and hub genes among DEGs. Firstly, The outlier samples were removed by the "goodsamplesgenes" function in WGCNA. The adjacency matrix was transformed into a topological overlap matrix (TOM). According to the TOM-based dissimilarity measure, genes were divided into different gene modules. Here, we set the soft-thresholding power to 8(scale-free R²=0.88, networkType=“unsigned”), the cut-off to 0.25, deepSplit to 3, and the minimal module size to 30 to identify key modules. The module with the highest correlation with CAFs was selected to explore subsequent analysis.

Function Enrichment and Pathway Analysis of Selected Module

Next, we used " enrichGO " and " enrichKEGG " functions of the " ClusterProfiler " package to perform GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis of the “Greenyellow” module. Under the condition of p.adj < 0.05, qvalue < 0.05, and count ≥2, biological processes, cellular components, and molecular functions in GO analysis and signaling pathways in KEGG analysis were identified. The results of the enrichment analysis were shown in the form of bubble chart.

Differential Expression and Survival Analysis of Hub Genes

Under the condition of Gene Significance(GS)>0. 8 and Module Membership(MM)>0. 8, the hub genes were screened out from the studied module. Next, the GEPIA database¹² was used to perform survival analysis of hub genes. And then, the Kaplan-Meier Plotter database and GSE72094 dataset were used for validation of the survival analysis of hub genes. Furthermore, to ensure more reliable results, sequencing data of normal lung tissues from the Genotype-Tissue Expression (GTEx) database(https://commonfund.nih.gov/GTEx) was used in conjunction with the TCGA LUAD dataset. After that, TCGA+GTEx, GSE75037, and GSE32863 were selected to verify the differential expression of hub genes. To explore whether the mRNA levels of the hub genes exhibit diagnostic efficiency for distinguishing LUAD from normal lung tissues, we performed receiver operating characteristic (ROC) curve analysis. The pROC package¹³ was used to plot ROC curves and calculate the areas under the curves (AUC) values in R.

COL11A1 Genomic Alteration and Promoter Methylation in LUAD

C-BioPortal¹⁴ (http://cbioportal.org) is an open-access resource for exploring, visualizing, and analyzing multidimensional cancer genome data. It currently contains 225 cancer studies. c-BioPortal was used to analyze the changes of Col11A1 gene mutations in TCGA LUAD samples. UALCAN¹⁵(http://ualcan.path.uab.edu/) is a comprehensive, user-friendly, and interactive web resource for analyzing cancer OMICS data. UALCAN was used to evaluate promoter methylation of COL11A1.

Single‑cell Analysis for the Expression Source of COL11A1

To explore whether the expression of COL11A1 in the signature is related to CAFs, we performed single-cell analysis using the TISCH database (http:// tisch. comp-genom ics. org/home/)¹⁶. TISCH is an scRNA-seq database focusing on TME and provides detailed cell-type annotation at the single-cell level, enabling the exploration of TME across different cancer types including LUAD.

Immunohistochemical Validation of COL11A1

To evaluate differences of COL11A1 expression at the protein level, paraffin specimens from 30 cases of LUAD were collected from the Pathology Department of the Sixth Affiliated Hospital of Sun Yat-Sen University. Each sample contained paired tumors and adjacent normal tissues. The study was approved by the Ethics Committees of the Sixth Affiliated Hospital of Sun Yat-sen University. Written informed consent was obtained from all patients. IHC was used to assess the expression of COL11A1 in LUAD and adjacent normal tissues. COL11A1 antibody(Proteintech 21841-1-AP-50UL) was used in the IHC operation. Each step strictly followed the SP immunohistochemical kit standard operation protocols. Cell staining distribution was observed at low power (100x) to observe cell morphology and staining position. Five event horizons were randomly selected and recorded. In this study, Image J software and GraphPad Prism 8 were used for quantitative and comparative analysis of these 30 paired samples, respectively.

Statistical analysis.

R version(version 4.1.0) was used to perform the statistical analysis. Survival analysis was carried out according to Kaplan–Meier analysis and the Log-rank test. Comparisons of continuous variables were performed using the Student’s t-test or the Wilcoxon rank sum test as appropriate. Categorical clinicopathological variables were compared by using the Chi-square test or Fisher’s exact test. Correlation analysis was performed by Spearson correlation analysis. p-value of less than 0.05 were considered statistically significant(ns, p>=0.05; *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001).

Screening the Differentially Expressed Genes

The whole process of data analysis was described in Figure 1A. Through screening the difference between tumor and normal tissues in the TCGA LUAD dataset, there were 1412 up-regulated and 1893 down-regulated genes (|log2FC|> 1, FDR< 0.05) (Figures 1B); The heat map showed the top 50 upregulated and top 50 downregulated DEGs (Figures 1C).

Immune Infiltration Analysis

We use the EPIC algorithm (http://epic.gfellerlab.org) to score immune infiltration for each sample in the TCGA LUAD dataset. The result showed that there were more B cells, CAFs cells, NK cells, and other unclassified cells in the LUAD, but with fewer CD4 + T cells, CD8 + T cells, macrophages, and endothelial cells（Figure 2A-2H）.

WGCNA of DEGs

The "WGCNA" package in the R (version 4.1.0) was used to analyze the TCGA LUAD gene expression matrix. Sample clustering analysis was performed on the data to ensure the accuracy of the results. After outlier samples(TCGA-64-5775-01、TCGA-78-7155-01、TCGA-50-6591-01、TCGA-91-6847-01) were excluded, a sample clustering tree was produced(Supplementary Figure 1). To construct a scale-free network, soft threshold power β was set to 8, the independence was 0.88, and the mean connectivity was close to 0(Figures 3A,3B). DEGs with similar expression patterns were clustered into the same modules, and similar modules were further merged according to the cutting height <0.25(Figure 3C). In this study, seven co-expression modules in the TCGA-LUAD dataset were identified. They were black, pink, purple, green, gray, greenyellow, and blue-green modules, respectively (the gray module is composed of genes that cannot be classified) (Figure 3D, Supplementary table 1). Next, we performed the correlation analysis between the modules eigenvector and the immune infiltration score of the samples and then drew the correlation heat map according to the analysis results. We found that the greenyellow module has the strongest correlation with CAFs（r=0.89， P=2.8e-197）(Figure 3E,3F). So we chose the greenyellow module as an important module for further analysis.

Functional Enrichment Analysis of Greenyellow Module

“enrichGO” and “enrichKEGG” functions of the "clusterprofiler" package in Bioconductor were used to performed GO and KEGG enrichment analysis of the Greenyellow module genes. Under the condition of p.adj < 0.05, qvalue < 0.05 and count ≥2, the greenyellow module genes were involved in 256 biological processes (GO-BP), 23 cell components (GO-CC), 38 molecular functions (GO-MF), and 11 KEGG pathways(Supplementary table 2,3). The bubble graph demonstrated the top 10 messages for GO-BP, GO-CC, GO-MF, and KEGG, respectively (Figures 4A–4D). GO functional annotations showed that the greenyellow module genes were mainly involved in the tissue development(BP), extracellular matrix organization(BP), extracellular region(CC), extracellular matrix(CC), structural molecule activity(MF), extracellular matrix structural constituent(MF), etc. KEGG pathway analysis demonstrated that the greenyellow module genes were primarily associated with protein digestion and absorption, ECM-receptor interaction, focal adhesion, etc. These enrichment analysis results further suggested that the studied module genes were closely related to the extracellular matrix in the TME.

Survival Analysis and Differential Expression Validation of Hub Genes

GS> 0.8 and MM> 0.8 were selected as screening conditions, and 13 hub genes related to CAFs were obtained from the greenyellow module. They were ADAM12， ADAMTS12， COL11A1， COL1A1， COL1A2， COL3A1， COL5A1， COL5A2， ITGA11， LRRC15， POSTN， THBS2, THY1. The GEPIA database was used to analyze the prognosis of 13 hub genes in the LUAD. Taking the median value of gene expression as the cutoff point, we found that patients with high expression of COL11A1 had a poor prognosis (HR = 1.5, P = 0.014), and the other 12 hub genes had no significant difference in the prognosis of the LUAD(Figures 5A-5M). Besides, we further verified the prognosis of the COL11A1 in the LUAD by the GSE72094 dataset and Kaplan Meier plotter database, and found that the LUAD patients with high expression of COL11A1 had a poor prognosis(P<0.05)(Figures 5N,5O). Meanwhile, we verified the differential expression of the COL11A1 gene. Based on the TCGA +GTEx database, GSE32863, and GSE75037 datasets, we further verified the expression of the COL11A1 in LUAD tissues was significantly higher than that in normal lung tissues（P<0.0001）(Figures 6A-6C). Furthermore, the results of the ROC analysis showed that the AUC value was 0.898(TCGA+GTEx) (Figure 6D), 0.885(GSE75037) (Figure 6E), and 0.864(GSE32863) (Figure 6F), respectively.

Genomic Mutation and Methylation Analysis of COL11A1 in LUAD

We used the TCGA LUAD dataset in the cBioPortal database to explore the gene mutation of COL11A1. There were 584 patients included in the study. A total of 18% of the patients had gene mutations, which contained missense mutations, shear mutations, truncating mutations, amplification, and deep deletion(Figures 7A,7B). A total of 46 mutation sites were found in the patients with these mutations(Figure 7C). Next, We used the UALCAN database to explore the methylation of the COL11A1 gene in TCGA LUAD patients. The results showed that the methylation level of COL11A1 in tumor tissues was significantly higher than that in normal tissues (P = 1.62e-12) (Figure 7D).

Single-cell Analysis of COL11A1

Three datasets of LUAD in the TISCH database were used for analysis, namely EMTAB6149, GSE127465, and GSE131907. The UMAP algorithm was used for dimensionality reduction and to visualize the three datasets. In the EMTAB6149, we could see that COL11A1 was mainly expressed by CAFs(Figures 8A,8B). In GSE127465, we could see that COL11A1 was mainly expressed by CAFs and CD8 tex cells(Figures 8C,8D). In GSE131907, we could see that COL11A1 was mainly expressed by plasma cells and CAFs(Figures 8E,8F). The quantitative expression of COL11A1 in the TME of the corresponding datasets was represented in Figure 8G. This further confirmed the strong correlation between COL11A1 and CAFs.

Immunohistochemical Validation for the Expression of COL11A1 in LUAD

By analyzing the GeneCards database, subcellular locations information of the COL11A1 gene showed that it was mainly expressed in the endoplasmic reticulum and extracellular space(Figures 9A). For validation at the experimental level, we analyzed COL11A1 protein expression by IHC staining. A total of 30 pairs of samples from 30 patients with LUAD in the Sixth Affiliated Hospital of Sun Yat-Sen University were collected. The typical images of three patients were shown as representatives(Figures 9B-9D), and the results showed that COL11A1 was mainly enriched in the stromal tissues of LUAD, whereas weak staining or even non-staining was observed in normal tissues. Randomly selecting 5 fields of view from each sample, and using Image J to determine the average optical density(AOD) value, we found that the expression of COL11A1 in LUAD tissues was significantly higher than that in normal tissues(P<0.001) (Figure 9E).

LUAD is a high mortality rate of primary lung cancer. Although LUAD has made good progress in diagnosis and treatment, it has a high degree of malignancy and poor survival prognosis. Exploring the detailed mechanisms of LUAD pathogenesis and identifying promising diagnostic and prognostic biomarkers for LUAD may help to provide effective therapeutic targets and improve patient outcomes^17,18. The TME is considered to be an important influence on tumor growth and development. The TME contains many types of cells, including CD4 + T cells, CD8 + T cells, macrophages, CAFs, B cells, and so on. In recent years, there have been more and more studies on CAFs. Many studies believed that CAFs in the tumor microenvironment were an important factor affecting tumor invasion and metastasis. Therefore, we hope to screen the biomarkers that affect the prognosis and diagnosis of LUAD from the perspective of CAFs in the tumor microenvironment. The results of our study support that COL11A1 derived from CAFs in the TME of LUAD may serve as a novel diagnostic and prognostic biomarker for LUAD.

The WGCNA and EPIC algorithm in immune infiltration were used to analyze the sample data of LUAD in TCGA, and finally 13 hub genes with a strong correlation with CAFs were obtained. They were ADAM12, ADAMTS12, COL11A1, COL1A1, COL1A2, COL3A1, COL5A1, COL5A2, ITGA11, LRRC15, POSTN, THBS2, THY1. To clarify the prognosis of 13 hub genes in LUAD, the GEPIA database was used for analysis and finally found that the COL11A1 gene was highly expressed in LUAD and had a poor prognosis. We further verified the COL11A1 on the prognosis of LUAD patients by using GSE72094 and Kaplan-Meier plotter database, and confirmed that the prognosis of LUAD patients with high expression of COL11A1 was poor. Therefore, COL11A1 has the potential as a prognostic biomarker in LUAD.

Collagen type XI alpha 1 (COL11A1) is a component of XI collagen and is found mainly in the cartilage¹⁹. The lack of type XI collagen leads to abnormal thickening of the tissues in cartilage^20,21. Importantly, emerging evidence indicates that COL11A1 is a gene associated with cancer progression that can promote tumor growth, migration, invasion, metastasis, and chemotherapy resistance²². In addition, upregulation of COL11A1 is associated with cancer recurrence and poor survival and serves as a diagnostic indicator in some types of cancer, such as breast cancer, colorectal cancer, gastric cancer, ovarian cancer, and so on^23–26. Several reports also validate that COL11A1 is an oncogene in the progression of non-small cell lung cancer^27–30. However, there is still a lack of reports on studying COL11A1 from the perspective of CAFs in the LUAD.

In our study, we used to TCGA + GTEx, GSE32863, and GSE75037 to confirm that the expression of COL11A1 was significantly higher in tumor tissues than that in normal tissues, which was verified by our own samples in IHC experiment. Besides, the corresponding AUC value of ROC curves was all greater than 0.86, which revealed that COL11A1 exhibited excellent diagnostic efficiency for LUAD and adjacent normal tissues. Therefore, COL11A1 has the potential as a diagnostic biomarker in LUAD. To explore the reasons for the high expression of COL11A1 in LUAD, we used the cBioPortal database to study the mutation of the COL11A1 gene. We found that the gene mutation rate of COL11A1 in LUAD was as high as 18%, which may be one of the reasons for the high expression of the COL11A1 gene. In addition, Gene promoter methylation is an important factor affecting gene expression. The ULCAN database was used to explore the promoter methylation of the COL11A1 gene in LUAD. We found that the tumor tissues was hypermethylated compared with the adjacent normal tissues, which could not explain why the COL11A1 gene was highly expressed in LUAD. Therefore, it is suggested that promoter methylation is not the cause of the high expression of the COL11A1 gene in LUAD. In addition to gene mutation and promoter methylation, the expression of COL11A1 at the transcriptional level may also be affected by many other factors, such as histone modification and m6A RNA methylation modification^31–33, which have not been discussed in our study.

To explore the mechanism by which COL11A1 affects the proliferation and invasion of LUAD, we performed GO and KEGG analyses. We found that the genes co-expressed with COL11A1 (greenyellow module) were mainly enriched in some signal pathways related to the extracellular matrix in the TME of LUAD. These results further suggested that COL11A1 played an indispensable role in influencing tumor proliferation and invasion through the TME. In recent years, the technology of single-cell RNA sequencing has developed rapidly, which allows us to master much information that is not available by bulk RNA sequencing³⁴. Single-cell RNA sequencing (scRNA-seq) is a technique to analyze complex tissue transcripts at the single-cell level, identifying differences in gene expression and epigenetic factors associated with unicellular genomes mutations, as well as novel cell-specific markers and cell types³⁵. scRNA-seq plays an important role in all aspects of tumor research. In our study, using single-cell analysis related database, we found that the expression of COL11A1 was mainly secreted by CAFs in the TME rather than from LUAD cancer cells. Therefore, we believed that CAFs in the TME might affect the growth and development of LUAD cells by secreting COL11A1.

There are some limitations to our study that should be noted when promoting our findings. First, our results were mainly generated from bioinformatics analysis. To increase the reliability of the conclusion, we performed IHC experiment, and the results also confirmed that COL11A1 was highly expressed in LUAD. However, more in vivo/in vitro experiments are needed to prove our results on the potential function of COL111A1 in LUAD. Second, the data used in this study was retrospective. Our results should thus be further confirmed by prospective studies in the future. Third, no anti-COL11A1 therapeutic monoclonal antibodies have been evaluated in clinical trials. Therefore, we have no specific and complete cases with data to identify the benefit of anti-COL11A1 targeting drugs in the survival of LUAD.

Our study confirmed that COL11A1 was overexpressed in LUAD, and its expression level was related to the prognosis of LUAD patients. COL11A1 may be expressed and secreted by CAFs in the TME and affect the biological behavior of proliferation, invasion and metastasis of LUAD cells. COL11A1 may serve as a potential diagnostic and prognosis biomarker for LUAD. Further in vivo and in vitro studies are need to confirm the function of COL11A1 in LUAD.

LUAD

lung adenocarcinoma

CAFs

Cancer-related fibroblasts

TCGA

The Cancer Genome Atlas

WGCNA

Weighted Gene Co-expression Network Analysis

IHC

Immunohistochemistry

NSCLC

non-small cell lung cancer

TME

tumor microenvironment

GEO

Gene Expression Omnibus

DEGs

Differentially Expressed Genes

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

GTEx

Genotype-Tissue Expression

ROC

receiver operating characteristic

AUC

areas under the curve

Ethics approval and consent to participate

The study involving human participants was reviewed and approved by the clinical research ethics committee of the Sixth Affiliated Hospital of Sun Yat-sen University(2022ZSLYEC-351). The patients/participants provided their written informed consent to participate in this study. The study was conducted in accordance with the principles of the Declaration of Helsinki.

Consent for publication

Not applicable.Availability of data and materials

Publicly available datasets were analyzed in this study. This data can be found here: LUAD cohort from TCGA database, https:// portal.gdc.cancer.gov/; GSE72094, GSE75037 and GSE32863 datasets from GEO database, https://www.ncbi.nlm.nih.gov/geo; Data of normal lung tissues from the Genotype-Tissue Expression (GTEx) database(https://commonfund.nih.gov/GTEx);

The original contributions presented in the study are included in the article /Supplementary Material. Further inquiries can be directed to the corresponding authors.

Competing interests

The authors declare that they have no competing interests.

Funding

Supporting funds for scientific research of the Sixth Affiliated Hospital of Sun Yat-sen University（P20200217202404781）

Authors' contributions

HZ and XQ conceived and designed the study. HZ, YZ, XY and WC performed the data analysis. HZ, XQ, JT, and SH analyzed and interpreted the results. HZ wrote the original manuscript. HZ and HL reviewed and revised the manuscript. All authors contributed to the article and approved the submitted version.

Acknowledgements

We would like to acknowledge TCGA, GEO and GTEx for providing data.

Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209-249. doi:10.3322/caac.21660
Hirsch FR, Scagliotti GV, Mulshine JL, et al. Lung cancer: current therapies and new targeted treatments. Lancet. 2017;389(10066):299-311. doi:10.1016/S0140-6736(16)30958-8
Succony L, Rassl DM, Barker AP, McCaughan FM, Rintoul RC. Adenocarcinoma spectrum lesions of the lung: Detection, pathology and treatment strategies. Cancer Treat Rev. 2021;99:102237. doi:10.1016/j.ctrv.2021.102237
Herbst RS, Morgensztern D, Boshoff C. The biology and management of non-small cell lung cancer. Nature. 2018;553(7689):446-454. doi:10.1038/nature25183
Hui L, Chen Y. Tumor microenvironment: Sanctuary of the devil. Cancer Lett. 2015;368(1):7-13. doi:10.1016/j.canlet.2015.07.039
Hinshaw DC, Shevde LA. The Tumor Microenvironment Innately Modulates Cancer Progression. Cancer Res. 2019;79(18):4557-4566. doi:10.1158/0008-5472.CAN-18-3962
Bu L, Baba H, Yoshida N, et al. Biological heterogeneity and versatility of cancer-associated fibroblasts in the tumor microenvironment. Oncogene. 2019;38(25):4887-4901. doi:10.1038/s41388-019-0765-y
Asif PJ, Longobardi C, Hahne M, Medema JP. The Role of Cancer-Associated Fibroblasts in Cancer Invasion and Metastasis. Cancers (Basel). 2021;13(18):4720. doi:10.3390/cancers13184720
Gunaydin G. CAFs Interacting With TAMs in Tumor Microenvironment to Enhance Tumorigenesis and Immune Evasion. Front Oncol. 2021;11:668349. doi:10.3389/fonc.2021.668349
Racle J, de Jonge K, Baumgaertner P, Speiser DE, Gfeller D. Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. Elife. 2017;6:e26476. doi:10.7554/eLife.26476
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559. doi:10.1186/1471-2105-9-559
Li C, Tang Z, Zhang W, Ye Z, Liu F. GEPIA2021: integrating multiple deconvolution-based analysis into GEPIA. Nucleic Acids Res. 2021;49(W1):W242-W246. doi:10.1093/nar/gkab418
Robin X, Turck N, Hainard A, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77. doi:10.1186/1471-2105-12-77
Gao J, Aksoy BA, Dogrusoz U, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269):pl1. doi:10.1126/scisignal.2004088
Chandrashekar DS, Karthikeyan SK, Korla PK, et al. UALCAN: An update to the integrated cancer data analysis platform. Neoplasia. 2022;25:18-27. doi:10.1016/j.neo.2022.01.001
Sun D, Wang J, Han Y, et al. TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment. Nucleic Acids Res. 2021;49(D1):D1420-D1430. doi:10.1093/nar/gkaa1020
Duffy MJ, O’Byrne K. Tissue and Blood Biomarkers in Lung Cancer: A Review. Adv Clin Chem. 2018;86:1-21. doi:10.1016/bs.acc.2018.05.001
Calvayrac O, Pradines A, Pons E, Mazières J, Guibert N. Molecular biomarkers for lung adenocarcinoma. Eur Respir J. 2017;49(4):1601734. doi:10.1183/13993003.01734-2016
Wu YH, Chou CY. Collagen XI Alpha 1 Chain, a Novel Therapeutic Target for Cancer Treatment. Front Oncol. 2022;12:925165. doi:10.3389/fonc.2022.925165
Li A, Wei Y, Hung C, Vunjak-Novakovic G. Chondrogenic properties of collagen type XI, a component of cartilage extracellular matrix. Biomaterials. 2018;173:47-57. doi:10.1016/j.biomaterials.2018.05.004
Holyoak DT, Otero M, Armar NS, et al. Collagen XI mutation lowers susceptibility to load-induced cartilage damage in mice. J Orthop Res. 2018;36(2):711-720. doi:10.1002/jor.23731
Nallanthighal S, Heiserman JP, Cheon DJ. Collagen Type XI Alpha 1 (COL11A1): A Novel Biomarker and a Key Player in Cancer. Cancers (Basel). 2021;13(5):935. doi:10.3390/cancers13050935
Patra R, Das NC, Mukherjee S. Exploring the Differential Expression and Prognostic Significance of the COL11A1 Gene in Human Colorectal Carcinoma: An Integrated Bioinformatics Approach. Front Genet. 2021;12:608313. doi:10.3389/fgene.2021.608313
Giussani M, Landoni E, Merlino G, et al. Extracellular matrix proteins as diagnostic markers of breast carcinoma. J Cell Physiol. 2018;233(8):6280-6290. doi:10.1002/jcp.26513
Zhao Q, Xie J, Xie J, et al. Weighted correlation network analysis identifies FN1, COL1A1 and SERPINE1 associated with the progression and prognosis of gastric cancer. Cancer Biomark. 2021;31(1):59-75. doi:10.3233/CBM-200594
Wu YH, Huang YF, Chang TH, et al. COL11A1 activates cancer-associated fibroblasts by modulating TGF-β3 through the NF-κB/IGFBP2 axis in ovarian cancer cells. Oncogene. 2021;40(26):4503-4519. doi:10.1038/s41388-021-01865-8
Liu Z, Lai J, Jiang H, Ma C, Huang H. Collagen XI alpha 1 chain, a potential therapeutic target for cancer. FASEB J. 2021;35(6):e21603. doi:10.1096/fj.202100054RR
Tu H, Li J, Lin L, Wang L. COL11A1 Was Involved in Cell Proliferation, Apoptosis and Migration in Non-Small Cell Lung Cancer Cells. J Invest Surg. 2021;34(6):664-669. doi:10.1080/08941939.2019.1672839
Jin Y, Zhu H, Cai W, et al. B-Myb Is Up-Regulated and Promotes Cell Growth and Motility in Non-Small Cell Lung Cancer. Int J Mol Sci. 2017;18(6):E860. doi:10.3390/ijms18060860
Turkowski K, Herzberg F, Günther S, et al. Fibroblast Growth Factor-14 Acts as Tumor Suppressor in Lung Adenocarcinomas. Cells. 2020;9(8):E1755. doi:10.3390/cells9081755
Fu Y, Dominissini D, Rechavi G, He C. Gene expression regulation mediated through reversible m⁶A RNA methylation. Nat Rev Genet. 2014;15(5):293-306. doi:10.1038/nrg3724
Ma S, Chen C, Ji X, et al. The interplay between m6A RNA methylation and noncoding RNA in cancer. J Hematol Oncol. 2019;12(1):121. doi:10.1186/s13045-019-0805-7
Lawrence M, Daujat S, Schneider R. Lateral Thinking: How Histone Modifications Regulate Gene Expression. Trends Genet. 2016;32(1):42-56. doi:10.1016/j.tig.2015.10.007
Kuksin M, Morel D, Aglave M, et al. Applications of single-cell and bulk RNA sequencing in onco-immunology. Eur J Cancer. 2021;149:193-210. doi:10.1016/j.ejca.2021.03.005
Zhang Y, Wang D, Peng M, et al. Single-cell RNA sequencing in cancer research. J Exp Clin Cancer Res. 2021;40(1):81. doi:10.1186/s13046-021-01874-1

Table 1: The basic information of TCGA and GEO datasets in the study.
dataset	data type	platform	sample type		clinical information
dataset	data type	platform	tumor	normal	clinical information
TCGA	mRNA	Illumina HiSeq	516	58	yes
GSE75037	mRNA	Illumina HumanWG-6 v3.0 expression beadchip	83	83	no
GSE72094	mRNA	Rosetta/Merck Human RSTA Custom Affymetrix 2.0 microarray	442	0	yes
GSE32863	mRNA	Illumina HumanWG-6 v3.0 expression beadchip	58	58	no

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Analysis of CAFs‑related Genes Identifies COL11A1 Associated with Lung Adenocarcinoma Diagnosis and Prognosis

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Material And Method

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1