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. 2024 Nov 4;14(12):290. doi: 10.1007/s13205-024-04133-0

Bioinformatics analysis and experimental validation of the oncogenic role of COL11A1 in pan-cancer

Xiaofeng Wan 1, Qingmei Deng 1, Anling Chen 2,3, Xinhui Zhang 2, Wulin Yang 2,✉
PMCID: PMC11534945  PMID: 39507058

Abstract

The intricate expression patterns and oncogenic attributes of COL11A1 across different cancer types remain largely elusive. This study used several public databases (TCGA, GTEx, and CCLE) to investigate the pan-cancer landscape of COL11A1 expression, its prognostic implications, interplay with the immune microenvironment, and enriched signaling cascades. Concurrently, western blot analyses were performed to verify COL11A1 expression in lung adenocarcinoma (LUAD) cell lines and clinical samples. In addition, COL11A1 knockout cell lines were generated to scrutinize the functional consequences of COL11AI expression on cancer cell behavior by use MTT, colony formation, and scratch wound healing assays. A comprehensive database investigation revealed that COL11A1 was upregulated in a majority of tumor tissues and its expression was highly correlated with a patient’s prognosis. Notably, genetic alterations in COL11A1 predominantly occurred as mutations, while its DNA methylation status inversely mirrored gene expression levels across multiple promoter regions. Our findings suggest that COL11A1 helps to modulate the tumor immune landscape and potentially acts through the epithelial-mesenchymal transition (EMT) pathway to exert its oncogenic function. Western blot analyses further substantiated the specific upregulation of COL11A1 in LUAD cell lines and tissues, suggesting a close association with the EMT process. Ablation of COL11A1 in cancer cells significantly reduced their proliferative, clonogenic, and migratory abilities, underscoring the functional significance of COL11A1 in tumor cell behavior. Collectively, this research revealed the prevalent overexpression of COL11A1 in pan-cancer tissues, its profound prognostic and microenvironmental correlations, and the mechanistic underpinnings of its tumor-promoting effects as mediated via EMT signaling. Our findings suggest that COL11A1 could serve as a prognostic and diagnostic biomarker and therapeutic target for cancer.

Keywords: COL11A1, EMT, Tumor microenvironment, Prognosis, Biomarkers

Introduction

Despite advances in the diagnosis and treatment of cancer, it remains one of the leading causes of death (Ferlay et al. 2013). Therefore, further efforts are needed to reveal its pathogenesis, explore potential cancer therapeutic targets, and identify new tumor biomarkers for early diagnosis. Over the past decade, increasing numbers of studies have described the multiple roles played by cancer-associated fibroblasts (CAFs) in tumorigenesis (Li et al. 2021; Pei et al. 2023; Bertero, et al. 2019). CAFs, which may originate from a specific type of fat-derived stromal cell, are major components of the tumor microenvironment (TME) and closely associated with tumor progression. Under normal physiological conditions, collagen-rich fibroblasts maintain tissue architecture, and act as a barrier to epithelial cell migration. However, cancer cells can convert surrounding fibroblasts into activated CAFs. CAFs secrete specific collagen, growth factors, and enzymes that promote tumor growth, angiogenesis, invasion, and metastasis (Li et al. 2021; Hinz et al. 2012). Increasing evidence shows that collagen, as a major component of the extracellular matrix (ECM), is abnormally expressed in CAFs and involved in promoting the proliferation, metastasis, and malignant development of cancer cells (Toss et al. 2019; Zhao et al. 2020).

COL11A1 is a member of the type XI collagen subfamily. Under normal conditions, the COL11A1 gene and its derivatives are mainly expressed in chondrocytes, mesenchymal stem cells, and osteoblasts (Nissen et al. 2019; Ricard-Blum 2011). COL11A1 is rarely expressed in normal epithelial cells and static fibroblasts found at various sites (Katayama et al. 2018). However, COL11A1 is recognized as a CAF biomarker in a variety of cancer matrices and is also a common specific gene associated with poor clinical outcomes (Jia et al. 2016; Iwai et al. 2021). COL11A1 expression is always increased in the proliferative zone of tumor connective tissue composed mainly of CAFs, whereas an increase in COL11A1 is not observed in the fibroblasts of inflammatory diseases (Jia et al. 2016; Raglow and Thomas 2015). The pathological stage of a tumor is also positively correlated with COL11A1 expression (Jia et al. 2016; Chen 2020). COL11A1 may alter the mechanical properties of ECM, and thereby increase the aggressiveness of tumor cells (Jia et al. 2016; Iwai et al. 2021). Recent studies have shown that COL11A1 is not expressed in various benign or precancerous lesions, but is highly expressed in invasive ovarian cancer, breast cancer, esophageal cancer, colorectal cancer, pancreatic cancer, and lung cancer. Moreover, an increased level of COL11A1 expression is significantly associated with an advanced tumor stage, lymph node involvement, and a poor prognosis (Nallanthighal et al. 2021; Patra et al. 2021; Wu et al. 2021a; Tu et al. 2021; Wang et al. 2021; Yi et al. 2022; Liu et al. 2021). However, the underlying mechanism of the relationship between COL11A1 and tumorigenesis from a pan-cancer perspective remains obscure.

In the present study, we performed a comprehensive pan-cancer analysis of COL11A1 in 33 human cancers sourced from the TCGA database, investigating its oncogenic characteristics. Concurrently, we utilized both cellular and clinical specimens to assess the expression and biological function of COL11A1 in LUAD, and also examined the downstream pathways influenced by COL11A1, with the goal of deciphering its intricate relationship with cancer development and progression. Our findings provide novel insights into the oncogenic role of COL11A1.

Materials and methods

Clinical sample collection

Paired tissue samples were collected from six LUAD patients. Serum samples were collected from 16 healthy subjects and 16 LUAD patients. The specimens were obtained from Hefei Cancer Hospital, Chinese Academy of Sciences. This study was approved by the ethics review board of Hefei Institute of Physical Science, Chinese Academy of Sciences (SWYX-Y-2022-39), and Hefei Cancer Hospital, Chinese Academy of Sciences (PJ-KY2024-009). All samples were stored at − 80 °C before experiments.

Analysis of COL11A1 expression in human cancers

RNA-seq data for COL11A1 in 33 types of human cancer were obtained from the TCGA database (Weinstein et al. 2013). The Wilcoxon test method was used to compare differences in COL11A1 expression between tumor tissues and normal tissue, and the R package "ggpubr" was used to plot a graph. Furthermore, the online tool GEPIA2 (http://gepia2.cancer-pku.cn/) was used to analyze differences in COL11A1 expression (Tang et al. 2019). The Cancer Cell Line Encyclopedia (https://sites.broadinstitute.org/ccle) (Barretina et al. 2012) database contains COL11A1 expression data for 33 types of primary tumor cell lines.

The cBio cancer genomics portal (cBiopportal, httpswww.cbioportal.org/) (Cerami et al. 2012) is an open platform used for exploring multi-dimensional cancer genomic data. By using the cBioPortal, we obtained information regarding the main mutation types and mutation sites of COL11A1 in tumors and created a schematic diagram of those sites in the COL11A1 protein. The GSCA website (GSCA, http://bioinfo.life.hust.edu.cn/ GSCA/#/) (Liu et al. 2018) was used to evaluate the association between COL11A1 mRNA expression and copy number variation (CNV) in different tumors, as well as the association between single nucleotide variations (SNVs) copy number variations (CNVs) of COL11A1 and tumor prognosis.

Analysis of COL11A1 gene methylation

We used the GSCA website to analyze differences in COL11A1 gene methylation between tumors and adjacent normal tissues, and also the correlation between COL11A1 mRNA expression and gene promoter methylation for each tumor type. MEXPRESS is a data visualization tool that visualizes gene expression, DNA methylation, clinical data, and the relationships between them. The DNA methylation level of COL11A1 in KIRC and the relationship between COL11A1 expression and clinical features of kidney renal clear cell carcinoma (KIRC) were explored using the MEXPRESS website (https://mexpress.be/) (Koch et al. 2015; Koch et al. 2019).

Survival analysis

The GSCA website was used to analyze the relationship between COL11A1 expression, patient survival, and four indicators of survival, including overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) to study the relationship between COL11A1 expression and patient prognosis. The “Survival” package was further used to show the associations between COL11A1 expression and OS, DSS, DFS, and PFS by forest plots and Kaplan–Meier curves, and then calculate the Hazard ratio (HR). An HR > 1 indicated a risk factor that affected patient survival. In contrast, an HR < 1 indicated a protective effect in patients. Kaplan–Meier curves were drawn using the R packages “survival” and “survminer.”

Relationship between COL11A1 expression and tumor mutation load and microsatellite instability

Tumor mutational burden (TMB) is the number of somatic mutations in germline DNA in the whole genome and is used to quantitatively evaluate the mutations carried by tumor cells. It is a potential biomarker as a measure of the number of mutations in cancer cells (Jardim et al. 2021), and can predict the response of a cancer to immunotherapy (Yarchoan et al. 2017). Microsatellite instability (MSI) is a tumor molecular phenotype caused by genomic hypermutations and is characterized by a gain or loss of nucleotides in repetitive DNA fragments, which may also affect immune checkpoint therapy (Hause et al. 2017). We used the R package "fmsb" to generate radar plots of computed TMB and MSI score results with COL11A1 expression.

Correlation between COL11A1 expression and immunity

The “estimate” R package and “limma” R package were used to calculate the immune score, stromal score, and estimated score of 33 TCGA tumor samples, respectively. Gene expression data were also tested for their Spearman correlation with those scores. Immune subtype data was downloaded from the UCSC Xena website (https://xenabrowser.net/datapages/). The “limma,” “ggplot2,” and “shape2,” R packages were used for immune-subtyping analysis. Furthermore, we evaluated the relationship between COL11A1 expression and immune-related genes. The results are presented as heat maps, which were visualized using the “reshape2” and “RColorBrewer” packages.

Cell culture and generation of COL11A1 knockout (KO) cell lines

Human lung adenocarcinoma cell lines H460, A549, H1299, HCC827, and PC9 were obtained from the Dr. Han Wei Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences (CASHIPS) (Hu et al. 2023). The human normal alveolar epithelial cell line HPAEPIC was purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). A549 cells were cultured in Ham's F-12K medium, and all other cell lines were cultured in RPMI1640 medium (NSERA) supplemented with 10% fetal bovine serum (FBS; Gibco, Waltham, MA, USA), 100 units/mL penicillin, and 100 μg/mL streptomycin (Gibco, USA). All cells were incubated at 37 °C in a humidified incubator containing 5% CO2.

COL11A1 KO cell lines were generated using CRISPR-Cas9 technology. Briefly, based on the protocol used in the laboratory of Feng Zhang (https://zhanglab.bio), COL11A1 KO cell lines (A549-KO COL and H1299-KO COL) were generated using the Lenti-CRISPRV2 plasmid. The sgRNA target sequence of COL11A1 was 5ʹ-GGTCAATGCGAGGGTTGTTA-3ʹ. The recombinant viral plasmid was co-transfected along with the packaging vector GV708 (purchased from GeneChem Jikai Gene, Shanghai, China) into HEK 293T cells to produce lentivirus. The vector element sequence was U6-sgRNA-EF1a-Cas9-FLAG-CMV-EGFP-P2A-puro. Virus-containing supernatants were collected at 48 h after transfection and used to infect A549 and H1299 cells. Suspended cells (approximately 5 × 104 cells) were seeded in a 6-well plate, and when cell confluence reached approximately 30%, 50 uL of virus (MOI: 4E + 8) was added, and the medium was replaced 24 h later. Three days after infection, fluorescence was observed, and the cells were screened with puromycin for 1 week. The cells were then digested with trypsin, diluted into single cell isolates, seeded into 96-well plates, and positive monoclones were identified by western blotting. Control cell lines were generated using the empty Lenti-CRISPRV2 vector.

RNA extraction and qRT-PCR

Total RNA was extracted from cells using a TRIzol kit (Invitrogen, Carlsbad, CA, USA). Next, a 1 µg sample of RNA was immediately reverse-transcribed to cDNA by using a Primer-script RT kit (Takara, Shiga, Japan) according to the manufacturer’s instructions. After a fivefold dilution, 1 μL of cDNA was used as a template and the real-time PCR reaction was performed by using Power SYBR®Master Mix (Life Technologies, Waltham, MA, USA) on an ABI Prism 7500 rapid RT-PCR instrument according to the manufacturer's protocol. Each experiment was performed in triplicate. Relative levels of mRNA expression were calculated using the 2-ΔΔCT method with GAPDH serving as an internal control. The qRT-PCR primer sequences were as follows:

Gene Sequence
Human GAPDH Forward:5-ʹGGAGCGAGATCCCTCCAAAATʹ -3
Reverse:5-ʹGGCTGTTGTCATACTTCTCATGGʹ-3
Human COL11A1 Forward:5-ʹACCCTCGCATTGACCTTCCʹ-3
Reverse:5-ʹTTTGTGCAAAATCCCGTTGTTTʹ-3
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Western blotting

Total cellular or tissue proteins were extracted using RIPA buffer (Beyotime Biotechnology) containing protease inhibitors (SparkJade, China) and subsequently denatured by boiling in SDS-PAGE protein loading buffer (SparkJade). The proteins were then separated on NuPAGE (10% or 4% to 12%) Bis–Tris gels (Life Technologies, Carlsbad) and the protein bands were transferred onto PVDF membranes (Millipore, Billerica, MA, USA), which were subsequently blocked with 5% nonfat milk in 1 × Tris buffer containing 0.1% Tween 20 (TBST) for 1 h at room temperature. Next, the membranes were incubated overnight at 4 °C with specific primary antibodies, including anti-COL11A1 (Cell Signaling Technology, Danvers, MA, USA), anti-E-cadherin, (Zen-Bioscience, Durham, NC, USA), anti-N-cadherin (Zen-Bioscience), and anti-GAPDH (Transgen, Beijing, China). The following day, the cells were incubated with horseradish peroxidase-conjugated anti-mouse or anti-rabbit secondary antibodies (SparkJade or ZEN-BIOSCIENCE) for 1 h at room temperature. After three washing steps, the bands were stained using a chemiluminescence system (ECL, Yeasen Biotechnology, Shanghai, China) and exposed to X-ray film.

ELISA

Serum biomarker levels were measured using a double-antibody sandwich ELISA according to the manufacturer's instructions (COL11A1, Cusabio Technologies, Houston, TX, USA). Briefly, 100 μL of the experimental sample was added to each well of an assay plate and incubated for 2 h at 37 °C. Next, 100 μL of biotin antibody was added to each well and incubated at 37 °C for 1 h; after which, 100 μL of HRP-avidin was added and incubated at 37 °C for 1 h. Finally, the substrate solution (tetramethylbenzidine) was added, the reaction was terminated with H2SO4, and the absorbance of each well was read at 450 nm.

Cell proliferation assay

Cells were seeded in 96-well plates at a density of 2000 cells/well and monitored for 4–5 days. Next, 10 µL of CCK8 solution (C0005, TargetMol, Wellesley Hills, MA, USA) was added to each well and incubated for 1.5 h at 37 °C. The spectral absorbance of each well at 450 nm was measured with a microplate reader (Molecular Devices, LLC, San Jose, CA, USA).

Colony formation assay

Cells were seeded in six-well plates at a density of 100 cells/well and incubated for approximately 5 days in DMEM containing 10% FBS. After incubation, the cells were fixed with methanol and stained with 0.1% crystal violet (1 mg/mL). The number of colonies containing > 50 cells was counted.

Cell wound scratch assay

Aliquots of cells (5 × 105 cells) were seeded into the wells of 12-well plates and incubated for 24 h to achieve 100% confluence; after which, the cell monolayer was uniformly scratched with a 20 μL sterile pipette tip. Next, the wells were washed with PBS to remove exfoliated cells and cell debris and the medium was replaced with fresh serum-free RPMI-1640 medium. Images of specific wound sites were recorded under a microscope (Olympus CKX53) after 0, 12, 36, and 72 h and the wound area was measured using Image J software.

Statistical analysis

The Wilcoxon test was used to compare gene expression in normal and tumor tissues. Survival analyses were performed by using the Kaplan–Meier method or Cox regression model with the log-rank test. Spearman or Pearson methods were used to investigate the correlations between two variables. The Mann–Whitney test was used to compare the concentration of each protein in different groups. A p value < 0.05 was considered to be statistically significant. All statistical analyses were performed using GraphPad Prism 9 or R software.

Results

Analysis of COL11A1 expression in pan-cancer

First the TIMER, GEPIA, and CCLE databases were used to analyze COL11A1 expression in normal and tumor tissues. The results based on the TIMER database showed that when compared with normal tissues, the levels of COL11A1 expression in 33 different cancer types were significantly up-regulated, including BLCA, BRCA, CHOL, COAD, ESCA, HNSC, HNSC, KIRC, LIHC, LUAD, LUSC, READ, STAD, THCA, and UCEC (Fig. 1A). We further confirmed the expression levels of COL11A1 in various cancers by using the GEPIA database, and the results showed that COL11A1 expression was generally elevated in almost all tumor tissues except for LAML and TGCT (Fig. 1B). In addition, we examined COL11A1 expression in 33 pan-cancer cell lines by using the CCLE database, and those results showed that COL11A1 was highly expressed in multiple types of tumor cells (Fig. 1C).

Fig. 1.

Fig. 1

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Comparison of COL11A1 mRNA expression in pan-cancer. A The levels of COL11A1 expression in different types of cancer were analyzed based on the TCGA database. Blue box plots indicate normal tissue. Red box plots indicate cancer tissue. B COL11A1 mRNA expression in different cancers was analyzed by the GEPIA2 network tool. C The expression of COL11A1 mRNA in 33 tumor cell lines as sourced from the CCLE database. (*p < 0.05, ** p < 0.01, *** p < 0.001)

Gene mutation and survival assay of the COL11A1 gene in pan-cancer

Genomic mutations are closely related to tumorigenesis. Thus, we analyzed genetic alterations of the COL11A1 gene by using the online database cBioportal to identify the frequency and type of COL11A1 gene mutations in cancer. The results showed that genetic alterations of COL11A1 were dominated by the “mutation” type, which was observed in almost all cases of TCGA cancer (Fig. 2A). More than 25% of the mutations were observed in melanoma patients, followed by non-small cell lung cancer (> 15%). In addition, the COL11A1 missense mutation was identified as the dominant type of genetic alteration and R524W/X524_splice alterations were detected in R524Q cases (Fig. 2B). Furthermore, the potential correlation between COL11A1 alterations in pan-cancer samples and patient clinical survival prognosis was examined. We analyzed the association between COL11A1 copy number changes and overall survival (OS), progression-free survival (PFS), disease-specific survival (DSS), and disease-free interval (DFI) in cancer patients. As shown in Fig. 2C, the change in COL11A1 copy number variation might be closely related to the progression of UCEC, LGG, KIRP, KIRC, and COAD. The difference in survival between patients with the COL11A1 mutant gene vs. the wild type gene was statistically significant in STAD, PRAD, and BLCA (Fig. 2D).

Fig. 2.

Fig. 2

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Mutation characteristics of COL11A1 in pan-cancer. A Histograms showing the type and frequency of COL11A1 genetic alterations in each type of cancer. B Gene mutation map of COL11A1 across protein domains. C Association between COL11A1 gene CNV and patient survival in multiple cancer types. Bubble colors from blue to red represent hazard ratios from low to high. Bubble size was negatively correlated with Cox p values. D Differences of survival between mutant and wild-type groups in pan-cancer. Hazard ratios and Cox p values are shown by the color and size of the bubbles

COL11A1 DNA methylation analysis

DNA methylation is closely related to tumorigenesis and may serve to either promote or inhibit tumorigenesis (Saghafinia et al. 2018; Oakes et al. 2016). Therefore, we used the UALCAN database to analyze differences in COL11A1 promoter methylation levels between samples of tumor tissue and adjacent normal tissue. A correlation analysis showed that the COL11A1 methylation level was negatively correlated with mRNA expression in HNSC, BLCA, COAD, KIRP, ESCA, THCA, and LUAD (Fig. 3A). An additional correlation analysis showed similar results in LGG, UVM, SKCM, THYM, THCA, GBM, TGCT, MESO, COAD, PAAD, ACC, SARC, CESC, HNSC, UCS, LUAD, KIRC, BRCA, KIRP, DLBC, LAML, PRAD, ESCA, READ, and BLCA (Fig. 3B). We next investigated the potential correlation between COL11A1 DNA methylation and KIRC clinical traits by using the MEXPRESS database, and KIRC as an example. The results showed that the level of DNA methylation was negatively correlated with the grade of KIRC (Fig. 3C). In addition, COL11A1 DNA methylation was negatively correlated with gene expression on many probes in the promoter region.

Fig. 3.

Fig. 3

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DNA methylation analysis of COL11A1 in pan-cancer. A Methylation levels of COL11A1 in different cancer tissues. Blue dots represent methylation downregulation in tumors and red dots represent methylation up-regulation in tumors. The darker the color, the greater the difference. The size of the dots was positively correlated with FDR significance. B Genomic methylation correlated with COL11A1 mRNA expression in different cancers. C The correlations between COL11A1 gene expression and various clinical traits were analyzed by using KIRC as an example. DNA methylation near the COL11A1 promoter was negatively correlated with COL11A1 expression (p = − 0.209). (*p < 0.05, **p < 0.01, ***p < 0.001)

Analysis of the relationship between COL11A1 expression and prognosis

To investigate whether COL11A1 affects cancer prognosis, a univariate Cox proportional hazards regression model was used to analyze the association between COL11A1 expression and the OS, DSS, DFI, and (PFI) of patients. First, the association between COL11A1 expression and OS was presented in a forest plot (Fig. 4A). The KM survival curve showed that a high level of COL11A1 expression in ACC, CESC, KIRC, LGG, LUAD, and MESO patients was associated with a lower overall survival rate (Fig. 4B). The associations between COL11A1 expression and DSS, DFI, and PFI are shown in Fig. 5A. The survival curve showed that the levels of COL11A1 expression were negatively correlated with DSS in patients with ACC, KIRC, and LGG tumors (Fig. 5B). The DFI, which assesses the effect of radical surgery, is often used to represent the time from treatment to recurrence, and reflects the response of a cancer to palliative treatment. DFI and PFI analyses showed that after radical surgery, a high level of COL11A1 expression was still negatively correlated with the DFI and PFI in patients with some types of cancer, such as ACC, KIRC, and AGG (Fig. 5A, B).

Fig. 4.

Fig. 4

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Relationship between COL11A1 expression and OS. A A forest plot showing the relationship between COL11A1 expression and OS of patients as determined by univariate Cox regression analysis. A Hazard ratio greater than 1 (HR > 1) indicates a risk factor for patient survival. A HR < 1 indicates a protective effect on the patient. CI confidence interval. B Kaplan–Meier survival curves showed a significant correlation between COL11A1 gene expression and OS in ACC, CESC, KIRC, KIRP, LGG, LUDA, MESO, and UVM

Fig. 5.

Fig. 5

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Relationship between COL11A1 expression and DSS, DFI, and PFI. A A forest plot showing the association between COL11A1 expression and the disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) of patients as determined by univariate Cox regression analysis. B Kaplan–Meier survival curves showed that COL11A1 gene expression was significantly associated with PFS and DSS in ACC, KIRC, and LGG

Correlations between COL11A1 expression and immunity

Immune surveillance plays a crucial role in determining the prognosis of various types of cancer. Tumors can use the immune checkpoint genes PD-1 and CTLA-4 to evade immune responses (Roh et al. 2017). In this study, we investigated the association between COL11A1 and TMB/MSI in multiple cancers. Our results showed that COL11A1 expression was positively correlated with TMB in THYM, STAD, PRAD, and LUAD. Moreover, COL11A1 expression was positively correlated with MSI in TGCT, PRAD, GBM, and COAD. Tumors with an inverse correlation between COL11A1 expression and MSI included LUSC, KIRP, and HNSC (Fig. 6A). Based on TCGA data, we analyzed the correlation of COL11A1 with immune score, stromal score, estimated score, and tumor purity in different cancers (Fig. 6B). The results showed that COL11A1 expression was significantly and positively correlated with the immune score, stromal score, and estimate score for most cancer types, but negatively correlated with tumor purity. The higher the level of COL11A1 expression, the higher the content of immune cells and stromal cells, suggesting that COL11A1 may be involved in regulating the tumor immune microenvironment. Furthermore, we used the TIMER tool to examine the potential correlation between COL11A1 gene expression and different levels of immune cell infiltration in various cancers. The results showed that COL11A1 expression was positively correlated with macrophages and fibroblasts, but negatively correlated with B cell and CD8 + T cell expression (Fig. 6C), confirming the association of COL11A1 with CAFs.

Fig. 6.

Fig. 6

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Association of COL11A1 expression with TMB, MSI, and the tumor immune microenvironment. A Radar plot showing the correlation of COL11A1 expression with tumor mutation burden (TMB) and microsatellite instability (MSI) in pan-cancer. *p < 0.05, **p < 0.01, ***p < 0.001. B Correlation of COL11A1 with ESTIMATE score, immune score, stromal score, and tumor purity. C A heat map showing the correlation between COL11A1 expression and different immune cells in pan-cancerous tissues. Blue represents a negative correlation, red represents a positive correlation, and the darker the color, the greater the correlation. *p < 0.05, **p < 0.01, ***p < 0.001

Identification of COL11A1-associated signaling pathways via GSEA

To further investigate the potential mechanism of COL11A1 in promoting tumor progression, we performed a gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) based on TCGA data to explore the downstream pathways related to COL11A1. The GSVA analysis showed that COL11A1 was significantly associated with a variety of signaling pathways, and COL11A1 was found to be positively correlated with the EMT pathway in > 19 cancer types. These results suggested that the EMT pathway might mediate COL11A1 in regulating tumor development and progression (Fig. 7A and B). The GSEA analysis showed that COL11A1 expression was associated with cancer signals in a variety of tumors, such as “MAPK signaling” and “cytokine-cytokine receptor interaction” (Fig. 7C).

Fig. 7.

Fig. 7

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Cancer-associated pathway analysis for COL11A1. A The potential effect of COL11A1 upregulation on oncogenic pathway activity was analyzed in pan-cancer. The number in each cell represents the percentage of cancer types associated with a particular pathway in the total cancer types. B A heatmap showing the association between the COL11A1-based GSVA score and cancer-related pathway activity in pan-cancer. *p <  = 0.05; # fdr <  = 0.05. C Signaling pathways associated with COL11A1 expression were analyzed by GSEA

Next, we further examined COL11A1 expression in human alveolar epithelial cells and lung cancer cell lines. Consistent with results of the tissue gene expression analysis described above, COL11A1 expression was higher in lung cancer cells than in normal alveolar epithelial hepatic cells, at both the protein and mRNA levels (Fig. 8A). Next, a western blot analysis was performed to detect the expression of COL11A1, E-Cadherin, N-Cadherin, and Snail in lung adenocarcinoma tissues and corresponding adjacent normal tissues (Fig. 8B). The results showed that the levels of N-cadherin, Snail, and COL11A1 were significantly increased in six lung adenocarcinoma tissues examined, and expression of the epithelial cell marker E-cadherin was significantly decreased when compared with its expression in the corresponding adjacent tumor tissues. ELISA was used to detect the serum levels of COL11A1 in 16 lung adenocarcinoma patients and 16 normal control subjects. The results showed that the concentrations of COL11A1 in the serum of patients with lung adenocarcinoma were much higher than those in the serum of normal subjects, and the difference was statistically significant (Fig. 8C). Crisp/CAS9 was used to knockout COL11A1 expression in A549 and H1299 cells (Fig. 8D). When compared with the control cells, the COL11A1 knockout cells showed significantly decreased proliferation, colony formation, and migration abilities (Fig. 8E–G), indicating that COL11A1 regulates the malignant behavior of tumor cells.

Fig. 8.

Fig. 8

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COL11A1 knockdown affected cancer cell activity. A Western blotting was used to detect the expression of COL11A1 and EMT bio-markers in six paired human LUAD tissues and adjacent normal tissues. T represents cancer tissue and N represents adjacent normal tissue. B ELISA was used to detect the expression level of serum COL11A1 in 16 healthy individuals and 16 LUAD patients. *p < 0.05, **p < 0.01, ***p < 0.001. C CAS9 technology was used to knock out COL11A1 in A549 and H1299 cells; after which, cell behavior experiments were performed. The efficiency of COL11A1 knockdown in A549 and H1299 cells was determined by western blotting (D). Cell proliferation assays (E), colony formation assays (F), and cell migration assays (G) were performed

Discussion

In contrast to existing therapeutic strategies which focus on tumor cells and their genetic alterations, increasing numbers of studies are now investigating the tumor-associated microenvironment (TAM), and especially the abnormal ECM accumulation-induced pathological environment(Roma-Rodrigues et al. 2019). Because increasing numbers of molecules in the TAM have been identified as possible contributors to cancer development, a comprehensive understanding of these molecules is a prerequisite for developing more precise and effective therapies.

COL11A1 has emerged as a pivotal target in tumor therapeutic strategies, owing to its pivotal role in augmenting ECM accumulation, and thereby expediting tumorigenesis via intricate mechanisms that foster interactions between tumor cells and diverse cell populations (Nallanthighal et al. 2021; Truong et al. 2022). Nevertheless, despite its promising potential, clinical endeavors aimed at targeting COL11A1 have fallen short of expectations, and are possibly hindered by the variability in COL11A1 expression patterns and the elusive intricacies of its underlying mechanisms (Liu et al. 2021; Truong et al. 2022).

In the present study, our bioinformatics analysis showed that COL11A1 expression was significantly increased in multiple cancer types. We also confirmed that a high level of COL11A1 expression was negatively correlated with the prognosis for many cancers. Moreover, our in vitro study further demonstrated that COL11A1 expression was significantly increased in tumor cells and cancer tissues when to their normal counterparts. The conserved signature of COL11A1 expression suggests a commonality in the cellular response to various cancer types, which transcends their distinct origins or genetic profiles. This finding suggests the possibility of developing effective pan-cancer therapeutic strategies that harness this conserved response as a therapeutic target, irrespective of the specific cancer subtype.

Furthermore, our research corroborated the association between genetic mutations of COL11A1 and tumor progression in numerous cancer types. The mutated form of COL11A1 emerged as a pivotal player in tumorigenesis and predicted a poor prognosis across a broad spectrum of cancers. Additionally, our correlation analysis revealed a strong association between COL11A1 levels and key tumor characteristics such as tumor mutation burden (TMB) and microsatellite instability (MSI) across a broad range of cancer types. Notably, COL11A1 levels were significantly associated with the tumor immune microenvironment, underscoring its potential as a promising target for immunotherapy strategies. These findings underscore the multifaceted role of COL11A1 in cancer biology and highlight its potential as a therapeutic target.

To improve the therapeutic efficacy of COL11A1 targeting strategies in treatment of cancer, it is necessary to investigate the expression pattern of COL11A, and elucidate the underlying mechanism by which it promotes tumor initiation and progression. Although most studies have shown that COL11A1 is mainly expressed in cancer-associated fibroblasts (CAFs), COL11A1 has also been found to be expressed in certain tumor cells, such as salivary gland carcinoma (SGC) cells with interannual origin (Arolt et al. 2022). High levels of COL11A1 are often associated with aggressive tumor phenotypes and a poor prognosis in a variety of solid tumor types, including ovarian, breast, pancreatic, and colorectal cancer.

In our investigation into the potential pathways intertwined with COL11A1-mediated tumorigenic regulation, we identified the epithelial-to-mesenchymal transition (EMT) pathway as the foremost and most plausible candidate. Abnormal activation of EMT signaling is believed to be the primary driver of CAF activation (Roma-Rodrigues et al. 2019; Yuan et al. 2023; Fiori et al. 2019; Ma et al. 2023). An abnormally activated EMT pathway has been shown to up-regulate the expression of COL11A1 and several other COL11A1-related genes in immortalized normal ovarian fibroblasts (Icay et al. 2018). Nevertheless, the pleiotropic nature of EMT poses a significant challenge, as it exposes patients to the risk of adverse reactions. There is a pressing need to devise therapies that are highly specific to more nuanced targets to avoid the undesirable consequences of broad-spectrum EMT-targeting therapies while effectively reducing fibroblast activation.

It has also been reported that COL11A1 is involved in the interactions between CAFs and cancer cells, and subsequently promotes tumor progression via different mechanisms. For example, one study analyzed three large microarray datasets of serous ovarian cancer and reported 10 genetic signatures that were associated with a poor prognosis, including COL11A1, which is modulated by TGF signaling (Karpinski et al. 2023). Those investigators also found that COL11A1 expression was increased during ovarian cancer progression, and inhibition of COL11A1 expression could significantly inhibit tumor development in vivo (Zhu et al. 2021; Wu et al. 2014; Wu et al. 2021b). Similar results suggest that COL11A1 can be used as a specific marker for activated CAFs, and that COL11A1 expression is correlated with tumor stage, tumor grade, and patient prognosis in 13 tumor types (Galván et al. 2014). Additional studies have reported that COL11A1 can induce TGF-β expression and secretion in ovarian cancer cells via the NF-κB/IGFBP2 axis, and thereby promote the transformation of ovarian fibroblasts into CAFs. COL11A1 can also induce the secretion of IL-6 from CAFs, and thus promote the growth and invasion of cancer cells (Wu et al. 2021a).

Our study suggests a potential role for COL11A1 in facilitating tumor progression by augmenting ECM remodeling. The ECM, a complex milieu comprising genes that code for ECM proteins and their associated assembly factors, has received significant attention in various areas of cancer research. At its core lie ECM glycoproteins, which include laminins, tenascins, thrombospondins, fibrillins, fibronectin, collagens, and proteoglycans (Najafi et al. 2019). Comprehensive analyses of the human pan-cancer matrisome have highlighted COL11A1 as a prominent gene whose expression is significantly upregulated in the advanced stage of aggressive cancers. This trend is evident across multiple cancer types, with COL11A1 expression consistently reported to be increased in ovarian, gastric, breast, esophageal, colorectal, and lung cancers (Tu et al. 2021; Wu et al. 2014; Li et al. 2017; Luo et al. 2022; Zhang et al. 2018; Zhang et al. 2016). Furthermore, a pivotal study emphasized COL11A1’s status as the top upregulated protein in non-small cell lung cancer (NSCLC) patients. COL11A1 also exhibits drug resistance and is associated with adverse clinical outcomes (Chen et al. 2023), further emphasizing its potential as a key player in tumor progression and therapeutic resistance.

Remarkably, the expression profile of COL11A1 in pancreatic, colorectal, and breast cancers exhibits a gradual escalation as the disease progresses from normal tissue to adenoma, carcinoma in situ, and ultimately, invasive cancer. In another pan-cancer investigation, multi-omics analyses were performed to decipher the expression patterns of ECM components and their correlations with clinical outcomes. Unsurprisingly, COL11A1 emerged as the most prominently upregulated component among the four key proteins studied, and was positively correlated with a poor clinical outcome, underscoring its pivotal role.

Intriguingly, COL11A1 is highly expressed the tumor stroma across a broad spectrum of invasive cancers, including colorectal, breast, esophageal, glioma, gastric, ovarian, lung, salivary gland, and pancreatic cancers. COL11A1 levels are positively correlated with disease progression and lymph node metastasis in those malignancies. Moreover, a pan-oncogene expression study further revealed the significance of COL11A1 by identifying a cohort of genes that were co-expressed with COL11A1; several of which function as EMT promoters (Xu et al. 2021). While the precise signaling pathways activated by COL11A1 in stromal versus tumor-intrinsic cells remain unclear, there is undeniable evidence that COL11A1, regardless of its cellular origin, significantly contributes to cancer invasion and metastasis. This underscores the importance of further investigating the multifaceted roles played by COL11A1 in cancer biology.

When considering COL11A1 as a specific biomarker and therapeutic target for cancers, it is necessary to realize that COL11A1 is also expressed in some normal tissues. The potential side effects of COL11A1 targeting can be predicted based on the phenotypes of mice and humans expressing the nonfunctional mutant form of COL11A1. Homozygous truncation mutations in mouse COL11A1 lead to poor chondrogenesis (So et al. 2001), while human COL11A1 mutations are associated with joint hypermobility, skin hyperelasticity, and extensive tissue fragility (Leone et al. 2023). Notably, these collagen lesions are associated with a loss of COL11A1 function throughout development and are unlikely to manifest in adults under conditions of transient COL11A1 targeting. In addition, because COL11A1 and many pancreatic cancer COL11A1 co-expressed genes have multiple tissue-specific mRNA splicing isomers, it will be valuable to determine whether any mRNA isomers are specifically expressed in different tissues, as that information can aid in identifying future targets. In conclusion, our study underscores the multifaceted nature of COL11A1, which not only underpins tissue integrity as a scaffold, but also serves as a pivotal source of critical biomechanical and molecular signals that influence tumor growth and biology. These influences extend to core cancer-associated processes, such as cell proliferation, survival, immune microenvironment, and oncogenic signaling. An enhanced comprehension of COL11A1 and its intricate signaling landscape may allow for novel anticancer strategies rooted in this knowledge to emerge in the future.

Acknowledgements

None.

Author contributions

WLY conceived and designed the study. XFW, QMD, and ALC: data collection/entry. XFW and ALC: data analysis/statistics. WLY and XFW: preparation of the manuscript. XFW made a literature analysis/search. All authors read and approved the final manuscript.

Funding

This study was supported by the Hefei Municipal Natural Science Foundation (2022050 & 2023032), the Industrialization project of Wanjiang Emerging Industry Technology Development Center (202304), and the Foundation for Excellent Young Talents Training program of Hefei Cancer Hospital (YZJJ2022QN49).

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval and consent to participate

This study was approved by the ethics review board of Hefei Institute of Physical Science, Chinese Academy of Sciences (SWYX-Y-2022–39), and Hefei Cancer Hospital, Chinese Academy of Sciences (PJ-KY2024-009).

References

  1. Arolt C et al (2022) Mutually exclusive expression of COL11A1 by CAFs and tumour cells in a large panCancer and a salivary gland carcinoma cohort. Head Neck Pathol 16(2):394–406 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Barretina J et al (2012) The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483(7391):603–607 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bertero T et al (2019) Tumor-stroma mechanics coordinate amino acid availability to sustain tumor growth and malignancy. Cell Metabol 29(1):124–140 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Cerami E et al (2012) The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2(5):401–404 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Chen D et al. (2020) BGN and COL11A1 regulatory network analysis in colorectal cancer (CRC) reveals that BGN influences CRC cell biological functions and interacts with miR-6828–5p. Cancer Management and Research, 2020: p. 13051–13069 [DOI] [PMC free article] [PubMed]
  6. Chen M et al (2023) Circular Circrangap1 contributes to non-small cell lung cancer progression by increasing Col11a1 expression through sponging Mir-653-5p. Biochem Genet 61(6):2580–2598 [DOI] [PubMed] [Google Scholar]
  7. Ferlay J et al (2013) Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. Eur J Cancer 49(6):1374–1403 [DOI] [PubMed] [Google Scholar]
  8. Fiori ME et al (2019) Cancer-associated fibroblasts as abettors of tumor progression at the crossroads of EMT and therapy resistance. Mol Cancer 18:1–16 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Galván JA et al (2014) Validation of COL11A1/procollagen 11A1 expression in TGF-β1-activated immortalised human mesenchymal cells and in stromal cells of human colon adenocarcinoma. BMC Cancer 14:1–12 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Hause RJ et al (2017) Corrigendum: Classification and characterization of microsatellite instability across 18 cancer types. Nat Med 23(10):1241 [DOI] [PubMed] [Google Scholar]
  11. Hinz B et al (2012) Recent developments in myofibroblast biology: paradigms for connective tissue remodeling. Am J Pathol 180(4):1340–1355 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Hu L et al (2023) The tumorigenic effect of the high expression of ladinin-1 in lung adenocarcinoma and Its potential as a therapeutic target. Molecules 28(3):1103 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Icay K, Liu C, Hautaniemi S (2018) Dynamic visualization of multi-level molecular data: the Director package in R. Comput Methods Programs Biomed 153:129–136 [DOI] [PubMed] [Google Scholar]
  14. Iwai M et al (2021) Cancer-associated fibroblast migration in non-small cell lung cancers is modulated by increased integrin α11 expression. Mol Oncol 15(5):1507–1527 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Jardim DL et al (2021) The challenges of tumor mutational burden as an immunotherapy biomarker. Cancer Cell 39(2):154–173 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Jia D et al (2016) A COL11A1-correlated pan-cancer gene signature of activated fibroblasts for the prioritization of therapeutic targets. Cancer Lett 382(2):203–214 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Karpinski P et al (2023) Expression of genes associated with epithelial-mesenchymal transition in Merkel cell polyomavirus-negative Merkel cell carcinoma. Lab Invest 103(8):100177 [DOI] [PubMed] [Google Scholar]
  18. Katayama K et al (2018) Giantin is required for coordinated production of aggrecan, link protein and type XI collagen during chondrogenesis. Biochem Biophys Res Commun 499(3):459–465 [DOI] [PubMed] [Google Scholar]
  19. Koch A et al (2015) MEXPRESS: visualizing expression, DNA methylation and clinical TCGA data. BMC Genomics 16:1–6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Koch A et al (2019) MEXPRESS update 2019. Nucleic Acids Res 47(W1):W561–W565 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Leone MP et al (2023) Specifications and validation of the ACMG/AMP criteria for clinical interpretation of sequence variants in collagen genes associated with joint hypermobility. Hum Genet 142(6):785–808 [DOI] [PubMed] [Google Scholar]
  22. Li A et al (2017) COL11A1 is overexpressed in gastric cancer tissues and regulates proliferation, migration and invasion of HGC-27 gastric cancer cells in vitro. Oncol Rep 37(1):333–340 [DOI] [PubMed] [Google Scholar]
  23. Li C et al (2021) Cancer associated-fibroblast-derived exosomes in cancer progression. Mol Cancer 20(1):154 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Liu C-J et al (2018) GSCALite: a web server for gene set cancer analysis. Bioinformatics 34(21):3771–3772 [DOI] [PubMed] [Google Scholar]
  25. Liu Z et al (2021) Collagen XI alpha 1 chain, a potential therapeutic target for cancer. FASEB J 35(6):e21603 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Luo Q et al (2022) COL11A1 serves as a biomarker for poor prognosis and correlates with immune infiltration in breast cancer. Front Genet 13:935860 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Ma C et al (2023) Pan-cancer spatially resolved single-cell analysis reveals the crosstalk between cancer-associated fibroblasts and tumor microenvironment. Mol Cancer 22(1):170 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Najafi M, Farhood B, Mortezaee K (2019) Extracellular matrix (ECM) stiffness and degradation as cancer drivers. J Cell Biochem 120(3):2782–2790 [DOI] [PubMed] [Google Scholar]
  29. Nallanthighal S, Heiserman JP, Cheon D-J (2021) Collagen type XI alpha 1 (COL11A1): a novel biomarker and a key player in cancer. Cancers 13(5):935 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Nallanthighal S, Heiserman J, Cheon D (2021) Collagen type XI alpha 1 (COL11A1): a novel biomarker and a key player in cancer. Cancers 13(5):935. 10.3390/cancers13050935 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Nissen NI, Karsdal M, Willumsen N (2019) Collagens and Cancer associated fibroblasts in the reactive stroma and its relation to Cancer biology. J Exp Clin Cancer Res 38(1):115 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Oakes CC et al (2016) DNA methylation dynamics during B cell maturation underlie a continuum of disease phenotypes in chronic lymphocytic leukemia. Nat Genet 48(3):253–264 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Patra R, Mukherjee S, Das N (2021) Exploring the differential expression and prognostic significance of the COL11A1 gene in human colorectal carcinoma: an integrated bioinformatics approach. Front Genet 12(88):2021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Pei L et al (2023) Roles of cancer-associated fibroblasts (CAFs) in anti-PD-1/PD-L1 immunotherapy for solid cancers. Mol Cancer 22(1):29 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Raglow Z, Thomas SM (2015) Tumor matrix protein collagen XIα1 in cancer. Cancer Lett 357(2):448–453 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Ricard-Blum S (2011) The collagen family. Cold Spring Harb Perspect Biol 3(1):a004978 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Roh W et al (2017) Integrated molecular analysis of tumor biopsies on sequential CTLA-4 and PD-1 blockade reveals markers of response and resistance. Sci Translat Med 9(379):3560 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Roma-Rodrigues C et al (2019) Targeting tumor microenvironment for cancer therapy. Int J Mol Sci 20(4):840 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Saghafinia S et al (2018) Pan-cancer landscape of aberrant DNA methylation across human tumors. Cell Rep 25(4):1066–1080 [DOI] [PubMed] [Google Scholar]
  40. So C et al (2001) Impact of mutations of cartilage matrix genes on matrix structure, gene activity and chondrogenesis. Osteoarthritis and Cartílage 9:S160–S173 [PubMed] [Google Scholar]
  41. Tang Z et al (2019) GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res 47(W1):W556–W560 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Toss MS et al (2019) Collagen (XI) alpha-1 chain is an independent prognostic factor in breast ductal carcinoma in situ. Mod Pathol 32(10):1460–1472 [DOI] [PubMed] [Google Scholar]
  43. Truong DQ et al (2022) Collagen XI Alpha 1 (COL11A1) expression in the tumor microenvironment drives neuroblastoma dissemination. Pediatr Dev Pathol 25(2):91–98 [DOI] [PubMed] [Google Scholar]
  44. Tu H et al (2021) COL11A1 was involved in cell proliferation, apoptosis and migration in non-small cell lung cancer cells. J Invest Surg 34(6):664–669 [DOI] [PubMed] [Google Scholar]
  45. Wang H et al (2021) The COL11A1/Akt/CREB signaling axis enables mitochondrial-mediated apoptotic evasion to promote chemoresistance in pancreatic cancer cells through modulating BAX/BCL-2 function. J Cancer 12(5):1406 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Weinstein JN et al (2013) The cancer genome atlas pan-cancer analysis project. Nat Genet 45(10):1113–1120 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Wu Y et al (2014) COL11A1 promotes tumor progression and predicts poor clinical outcome in ovarian cancer. Oncogene 33(26):3432–3440 [DOI] [PubMed] [Google Scholar]
  48. Wu Y-H et al (2021a) COL11A1 activates cancer-associated fibroblasts by modulating TGF-β3 through the NF-κB/IGFBP2 axis in ovarian cancer cells. Oncogene 40(26):4503–4519 [DOI] [PubMed] [Google Scholar]
  49. Wu Y-H et al (2021b) miR-335 restrains the aggressive phenotypes of ovarian cancer cells by inhibiting COL11A1. Cancers 13(24):6257 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Xu F et al (2021) Development and validation of a six-gene prognostic signature for bladder cancer. Front Genet 12:758612 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Yarchoan M, Hopkins A, Jaffee EM (2017) Tumor mutational burden and response rate to PD-1 inhibition. N Engl J Med 377(25):2500–2501 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Yi X et al (2022) SPP1 facilitates cell migration and invasion by targeting COL11A1 in lung adenocarcinoma. Cancer Cell Int 22(1):324 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Yuan Z et al (2023) Extracellular matrix remodeling in tumor progression and immune escape: from mechanisms to treatments. Mol Cancer 22(1):48 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Zhang D, Zhu H, Harpaz N (2016) Overexpression of α1 chain of type XI collagen (COL11A1) aids in the diagnosis of invasive carcinoma in endoscopically removed malignant colorectal polyps. Pathology-Research and Practice 212(6):545–548 [DOI] [PubMed] [Google Scholar]
  55. Zhang B et al (2018) Cytoplasmic collagen XIαI as a prognostic biomarker in esophageal squamous cell carcinoma. Cancer Biol Ther 19(5):364–372 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Zhao T et al (2020) A new tandem peptide modified liposomal doxorubicin for tumor “ecological therapy.” Nanoscale 12(5):3359–3369 [DOI] [PubMed] [Google Scholar]
  57. Zhu M et al (2021) Activation of COL11A1 by PRRX1 promotes tumor progression and radioresistance in ovarian cancer. Int J Radiat Biol 97(7):958–967 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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