Identification of the anoikis-related prognostic gene signature in pancreatic adenocarcinoma

doi:10.21203/rs.3.rs-2326682/v1

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Identification of the anoikis-related prognostic gene signature in pancreatic adenocarcinoma

https://doi.org/10.21203/rs.3.rs-2326682/v1

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Pancreatic adenocarcinoma (PAAD) remains a tumor with high malignancy and poor prognosis. Anoikis can regulate tumor cell invasion and metastasis, which in turn leads to poor prognosis for cancer patients. However, the role of anoikis-related genes (ARGs) in PAAD remains unclear. In our study, we performed a comprehensive bioinformatics analysis to construct a prognostic gene model. Correlations between ARGs and tumor immune infiltration, tumor mutation burden and microsatellite instability were assessed using Spearman correlation analysis. A total of 23 ARGs were upregulated in PAAD. Functional enrichment analysis revealed that these 23 ARGs were mainly involved in proteoglycans in cancer, regulation of anoikis, lipid and atherosclerosis, focal adhesion, and regulation of apoptotic Prognostic analysis showed that patients with PAAD with high expression of PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3 had a low survival rate. The prognostic ARGs model constructed using the above six prognostic genes was constructed to predict the overall survival of PAAD patients. There was a significant correlation between prognostic ARGs and immune cell infiltration and tumor mutation burden, while there was no correlation with microsatellite instability. Meanwhile, knockdown of BCL2L1 expression could inhibit the proliferation of pancreatic cancer cells. In conclusion, we performed a comprehensive bioinformatics analysis to identify PAAD patients containing six genes (PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3). Further studies are needed to validate our findings.

Pancreatic adenocarcinoma (PAAD) is a common malignant tumor with a very high degree of malignancy(Klein, 2021). The main treatment methods currently available are surgery, chemotherapy and radiation therapy(Torphy, Fujiwara, & Schulick, 2020). Early surgical resection is currently the only effective treatment. However, because early detection and diagnosis of pancreatic cancer is very difficult, surgical opportunities are often missed and lead to poor prognosis(Cai et al., 2021). Therefore, it becomes extremely important to reveal the genetic profile of pancreatic cancer prognosis and to develop new approaches for the treatment of pancreatic cancer.

Anoikis, a programmed cell death, is induced when cells are detached from the extracellular matrix and is a key mechanism to prevent the growth and attachment of apposition-independent cells to inappropriate matrices, thereby avoiding distal organ colonization. Cancer cells resist anoikis through two major pathways, anchorage-independent growth and epithelial-mesenchymal transition, thereby promoting cancer progression and metastasis.(Paoli, Giannoni, & Chiarugi, 2013) Cancer cells must overcome anoikis (detachment-induced death) to successfully metastasize. It has been found that high expression of IL1RAP in Pancreatic can inhibit anoikis to promote cancer progression(Zhang et al., 2021). Therefore, it is important to further explore the mechanism of the role of anoikis in the development of pancreatic cancer.

In this study, we used bioinformatics analysis to investigate ARGs expression of proprotein and its prognostic significance as well as immune infiltration. Our data may provide additional evidence for prognostic biomarkers and therapeutic targets for pancreatic cancer.

Data Collection

RNA-seq transcriptomic data and corresponding clinical medical information of pancreatic cancer patients were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). A total of 179 Pancreatic tumor samples and 4 adjacent normal samples were included in our study, and supplemented with RNA transcriptome data from the GTEx database of 328 healthy human normal tissue samples. ARGs were obtained from the GeneCards database (https://www.genecards.org/). The "limma" and "remodeling2" software packages were used to identify differences in ARGs expression in PAAD and normal tissues. Then, we constructed a protein-protein interaction (PPI) network of 23 ARGs using the search for interacting genes (strings).

Functional enrichment analysis

The R package for GO and KEGG statistical analysis through clusterProfiler applies biological term classification and enrichment analysis to gene cluster comparisons and provides a visualization of the results(Yu, Wang, Han, & He, 2012).

Correlation analysis

Spearman's correlation analysis was used to describe correlations between quantitative variables without a normal distribution. P-values less than 0.05 were considered statistically significant.

Survival analysis

Log rank was used to test the KM survival analysis to compare the survival differences between the two groups described above. For Kaplan-Meier curves, p-values and hazard ratios (HR) with 95% confidence intervals (CI) were derived by logrank test and univariate Cox regression. All of the above analytical methods and R packages were performed using v4.0.3 version of R software (R Foundation for Statistical Computing, 2020). P-values < 0.05 was considered statistically significant.

Development of the anoikis-related signature

A lasso regression-based approach was used to downscale and construct a prognostic model. The model is a riskscore formula containing multiple genes with weights for each gene, with negative numbers representing the gene as a protected gene and positive numbers representing the gene as a risk gene. lasso regression is where the coefficients of selected features are shown by lambda parameters, with the horizontal coordinate representing the value of the independent variable lambda and the vertical coordinate representing the coefficient of the independent variable. log rank is used to test KM survival analysis comparing the difference in survival between two or more groups as described above, a timeROC analysis was performed to discriminate the accuracy of the prediction model. For Kaplan-Meier curves, p-values and hazard ratios (HR) with 95% confidence intervals (CI) were derived by logrank test and univariate Cox regression. All the above analytical methods and R packages were performed using v4.0.3 version of R software (R Foundation for Statistical Computing, 2020). P-values < 0.05 was considered statistically significant.

Cell culture

Human normal pancreatic ductal epithelial cells (HPDE6-C7) and pancreatic cancer cells (SW1990, PANC-1) were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). HPDE6-C7 cells were cultured in DMEM (HyClone, Beijing, China) containing 10% FBS. SW1990 cells were cultured in L-15 (HyClone, Beijing, China) containing 10% fetal bovine serum (FBS) (Gibco, Carlsbad, CA). PANC-1 cells were cultured in DMEM (HyClone, Beijing, China) containing 10% FBS. The cell culture medium was mixed with 100 units/mL penicillin and 100 mg/mL streptomycin. Cells were cultured at 37°C in a humidified atmosphere with 5% CO2.

siRNA transfection

BCL2L1 mRNA expression was down-regulated using small interfering RNA (siRNA) and si-NC was used as a negative control. siRNA-BCL2L1 and si-NC were purchased from GenePharma (Suzhou, China). Transfection of siRNA (30 nM) was performed according to the manufacturer's recommendations, and the expression of BCL2L1 was confirmed to be down-regulated by qRT-PCR.

qRT-PCR

Total RNA was extracted using RNAiso Plus (Takara, Shiga, Japan) according to the manufacturer's steps, and RevertAid First Strand cDNA Synthesis kit (Thermo Fisher Scientific) was used for cDNA synthesis according to the manufacturer's steps. Levels of FN1 were quantified by IQ SYBR Premix Ex Taq Perfect Real-Time (Bio-Rad Laboratories, Hercules, CA) to perform real-time polymerase chain reaction (qRT-PCR) and tracked by β-actin. Primers were synthesized by Sangon Biotech (Sangon Biotech, Shang- hai, China). Relative miRNA level comparisons were calculated by the 2-ΔΔCT method.

Cell Counting Kit-8 assay

The effect of BCL2L1 on the proliferative capacity of pancreatic cancer cells was assessed by Cell Counting Kit-8 (CCK-8) assay. Cells were inoculated into a 96-well plate. Based on 1000 cells inoculated per well, each group has 6 wells and the wells without cells were used as blank control group. After adding Cell Counting Kit-8 (CCk-8) on days 1, 2, 3, 4, and 5, respectively, the cells were incubated in the dark of the incubator for 1 hour. The OD was measured by enzyme marker according to the wavelength of 490 nm.

Studying the expression of ARGs in PAAD

We first explored the expression of 23 ARGs in PAAD and normal tissues using the TCGA PAAD dataset, and a total of 23 ARGs were all up-regulated in PAAD with P-values < 0.05 (Fig. 1A). Then correlation analysis among ARGs (Fig. 1B) and protein-protein interaction (PPI) analysis were constructed to detect the interaction of these ARGs (Fig. 1C).

Functional enrichment analysis of ARGs

To clarify the functions of ARGs, we analyzed these pathways using GO and KEGG databases. GO term enrichment results including biological pathway (BP), cellular component (CC) and molecular function (MF) and KEGG enrichment analysis results are shown in the Fig. 2A-B.

Construction of the anoikis-related signature

To construct a prognostic gene model, one-way Cox regression analysis was used to screen ARGs with prognostic value. As a result, we identified all six genes with prognostic value, whose Kaplan-Meier survival curves are shown in Fig. 3. The results suggested that the survival rate of PAAD with high expression of PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3 had lower survival rates (Fig. 3A-F). LASSO Cox regression analysis was performed to construct prognostic gene models based on these in vitro prognostic ARGs (Fig. 4A-B). Riskscore = (0.0385) * ITGB1+(0.1502) * EGFR+(0.0654) * CASP8+(0.2971) * BCL2L1. Patients with pancreatic cancer were divided into low-risk group and high-risk group based on riskscore. The distribution of riskscores, survival status and expression of these three genes are shown in Fig. 4C. As the risk score increased, patients had an increased risk of death and decreased survival time. Kaplan-Meier curves showed that Pancreatic patients with high riskscores had worse overall survival probability than pancreatic patients with low riskscores (median time = for 1.3 years vs. 2 years, p = 0.00134, Fig. 4D), and the AUC on the ROC curves at 1, 3 and 5 years 0.711, 0.738 and 0.678, respectively (Fig. 4E).

Validation of the anoikis-related signature

The ARGs were analyzed and validated in the ICGC database, and the results showed that the overall survival rate was significantly lower in the high-risk group than in the low-risk group (median time = 1.2 years and 2.7, p = 1.83e-05 Fig. 5A-B), and the AUC on the ROC curves at 1, 3 and 5 years 0.717, 0.738 and 0.832, respectively (Fig. 5C).

Construction of a predictive nomogram

Considering the clinicopathological characteristics and these important prognostic ARGs, we also created a predictive column line graph to predict the probability of survival. Univariate and multifactorial analyses showed that BCL2L1 expression, pT stage, pN stage, and pM stage were independent factors affecting the prognosis of PAAD patients (Fig. 6A-B). To further exploit the prognostic value of anoikis-related signature, we combined clinical factors and the anoikis-related signature to create a nomogram, but due to data limitations, only the 1-year and 2-year survival Prob were obtained (Fig. 6C-D).

ARGs were associated with immune infiltration of pancreatic cancer

In our study, we also analyzed the correlation between the expression of ARGs (PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3) and immune infiltration in PAAD using the TIMER database. The data showed that the expression of PTK2, ITGB1, EGFR, and CASP3 was positively correlated with the infiltration level of B cell, macrophage, medullary dendritic cell, neutrophil, and T cell CD8+. In contrast, BCL2L1 expression did not correlate with the immune infiltration level. In addition, CASP8 expression was positively correlated with the infiltration level of B cell, neutrophil, medullary dendritic cell, and T cell CD8+. Interestingly, the expression of ARGs did not correlate with T cell CD4 + infiltration level (Fig. 7A). Then, we further analyzed the correlation between the signature's riskscore and various immune cells. The results showed a positive correlation between riskscore and the expression of B cell, T cell CD8+, neutrophil, and medullary dendritic cell, but no correlation with the expression of T cell CD4 + and macrophage (Fig. 7B-J). This evidence suggests a significant correlation between ARGs and tumor immune infiltration.

TMB and MSI analysis

The correlation between ARGs and TMB as well as MSI in PAAD was analyzed. The results showed that TMB was positively correlated with the expression of CASP8, BCL2L1 and CASP3, but TMB was not significantly correlated with the expression of PTK2, ITGB1 and EGFR (Fig. 8A). In MSI analysis, there was no significant correlation between MSI and ARGs (Fig. 8B).

Knockdown of BCL2L1 expression inhibited the proliferation of pancreatic cancer cells

Firstly, the relative expression of BCL2L1 in human normal pancreatic ductal epithelial cell (HPDE6-C7) and pancreatic cancer cells (SW1990, PANC-1) was detected by qRT-PCR, and the results showed that the expression of BCL2L1 was significantly higher in SW1990 and PANC-1 than in HPDE6-C7 (Fig. 9A). Then si-BCL2L1 was transfected in SW1990 and PANC-1. qRT-PCR assays showed that BCL2L1 expression was significantly knocked down in SW1990 and PANC-1 (Fig. 9B). Finally, the cell proliferation ability of SW1990 and PANC-1 was examined by CCK-8 assay, and the results showed that the cell proliferation ability was inhibited after the expression of BCL2L1 was knocked down (Fig. 9C-D).

Anoikis is known as a specific type of programmed cell death, apoptosis caused by loss of cell adhesion or inappropriate cell adhesion. Dysplastic cell growth or cells attached to inappropriate substrates can be effectively removed by anoikis, thereby maintaining tissue homeostasis and development. However, during tumorigenesis, cancer cells often acquire resistance to anoikis and become more aggressive and metastatic, making anoikis a potential prognostic and therapeutic target for cancer(Malagobadan, Ho, & Nagoor, 2020). In endometrial cancer, novel ARGs signatures have been identified to predict prognosis(S. Chen, Gu, Zhang, Hu, & Ge, 2021). However, the role of ARGs in PAAD has not been elucidated, and our study aimed to elucidate this role.

We first elucidated the screening for pancreatic anoikis-related genes and their prognostic value. We found that the expression of BRMS1, PTK2, BCL2L11, SRC, CEACAM6, CAV1, ITGB1, CEACAM5, EGFR, BCL2, CASP8, PTRH2, STAT3, CTNNB1, ZNF304, MAPK1, BMF, ITGA5, TP53, MCL1, BCL2L1, CASP3, and CDH1 was increased in PAAD compared with normal tissues.

Prognostic analysis showed that pancreatic patients with high expression of PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3 had a lower survival rate. These data are consistent with previous results. Hayato Fujita et al. found that EGFR mRNA expression may help predict the prognosis and sensitivity of PDAC patients to gemcitabine(Fujita et al., 2011). We also performed a functional enrichment analysis of ARGs and found that these 23 ARGs are mainly involved in proteoglycans in cancer, regulation of anoikis, lipid and atherosclerosis, focal adhesion, regulation of apoptotic signaling pathway, EGFR tyrosine kinase inhibitor resistance, and human cytomegalovirus infection. Interestingly, these functions or pathways are involved in pancreatic carcinogenesis and progression. In addition, a previous study showed that Inhibition of focal adhesion kinase (FAK) alters the production of pro-inflammatory and immunosuppressive cytokines by pancreatic cancer cells and impairs their ability to avoid immune surveillance(H. Jiang et al., 2016). These results suggest that these 23 ARGs may also play an important role in the tumorigenesis and progression of PAAD.

LASSO Cox regression analysis was used to construct a prognostic genetic model based on six prognostic ARGs (PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3), which predicted overall survival of pancreatic patients with moderate to high precision. The predictive nomogram shows that overall survival at 1- and 2- year can be predicted relatively well compared to the ideal model in the entire cohort. A previous study identified several ferroptosis-related prognostic features in pancreatic cancer and validated the potential value of ferroptosis-related prognostic features in the personalized immunotherapy of pancreatic cancer(P. Jiang et al., 2021). It has been found that CD73 can be used as a prognostic marker that can promote pancreatic cancer progression as well as achieve immune escape by disrupting T-cell infiltration and immune response, which ultimately leads to poor prognosis(Q. Chen et al., 2020). In addition, a prognostic signature was constructed based on immune-related genes in pancreatic cancer, which showed better ability to predict 1-, 2- and 3-year survival in pancreatic cancer patients(Liu, Fu, He, Du, & Xu, 2021). In our study, we identified for the first time a prognostic gene signature associated with anoikis of pancreatic cancer, which provides additional options for prognostic prediction of Pancreatic.

In our study, PTK2 was found to be one of the gene signatures. A previous study showed that PTK2 regulates cell cycle progression and plays an important role in cell proliferation and apoptosis. PTK2, also known as focal adhesion kinase 1, regulates cell migration and adhesion and is significantly associated with loss-of-nest apoptosis, thus being involved in cancer development and progression(Weisser et al., 2014; Zachary, 1997). In addition, the causal role of PTK2 in tumor development and its association with poor clinical outcome, PTK2 may be a useful target for tumor therapy(McLean et al., 2005). However, studies on the role of PTK2 in anoikis in pancreatic cancer are limited. In our study, we found that PTK2 is one of the prognostic biomarkers associated with anoikis in PAAD. We need to perform further in vivo and in vitro studies to verify whether PTK2 is involved in anoikis in pancreatic cancer.

Another important finding of our study showed that the above prognostic genes associated with anoikis were significantly associated with immune infiltration, which further supports the fact that anoikis plays an important role in the tumor immune microenvironment. Interleukin-8 expression was found to be negatively associated with anoikis in colorectal cancer cells(Xiao et al., 2015). Anti-GD2 ganglioside antibodies can induce anoikis in cancer cells and thus treat human small cell lung cancer(Aixinjueluo et al., 2005). EGFR regulates the immune microenvironment of pancreatic cancer tumors, and its inhibitor erlotinib has been approved by the FDA for the treatment of pancreatic cancer(Li et al., 2021). Recently, ITGB1 has been identified as a potential biomarker for predicting gastric cancer patients who may benefit from immune checkpoint inhibitors(Gu et al., 2022). TMB is considered a potential biomarker for predicting the effect of immunotherapy.

Our findings identified BCL2L1 as an independent risk factor for pancreatic cancer, while in past studies, BCL2L1 was found to promote the growth of colon cancer cells. However, we further explored the expression of BCL2L1 in pancreatic cancer cells and human normal pancreatic ductal epithelial cell and found that the expression of BCL2L1 in pancreatic cancer cells was significantly higher than that in human normal pancreatic ductal epithelial cells, and we constructed an interference plasmid for BCL2L1 and found that knocking down the expression of BCL2L1 in pancreatic cancer cells could inhibit the proliferation ability of the cells by CCK-8 assay.

We also investigated the relationship between six prognostic ARGs and tumor mutational load, and microsatellite instability. Tumor mutational load (TMB), microsatellite instability (MSI) are current biomarkers of cancer immunotherapy effectiveness and are clinically important for patients with a wide range of solid tumors(Addeo, Friedlaender, Banna, & Weiss, 2021; Cortes-Ciriano, Lee, Park, Kim, & Park, 2017). Our final results showed that only the expression of CASP8, BCL2L1 and CASP3 correlated with TMB analysis.

We performed a comprehensive and systematic bioinformatic analysis of PAAD and identified anoikis-related prognostic gene signature containing six genes (PTK2, ITGB1, EGFR, CASP8, BCL2L1, and CASP3) for pancreatic cancer patients. In vivo and in vitro experiments should be needed to further confirm our findings.

Authors’ Note

Wei Chen conceived, designed, and analyzed the data. Zeyan Xu and Jingjing Jiang wrote the manuscript. Hong Chen,and Ruihua Shi reviewed the data and revised the manuscript. All authors read and approved the final manuscript.

Ethics Approval

None to declare.

Availability of Data and Materials

All the data were extracted from TCGA dataset, GTEx database and GeneCards database.

Funding Statement

The study was funded by the Jiangsu Chinese Hospital Administration (NO: ZT202120).

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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No competing interests reported.

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Identification of the anoikis-related prognostic gene signature in pancreatic adenocarcinoma

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