Prognostic Value and Immune Infiltration of ARMC10 in Pancreatic Adenocarcinoma Via Integrated Bioinformatics Analyses

doi:10.21203/rs.3.rs-936587/v1

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Prognostic Value and Immune Infiltration of ARMC10 in Pancreatic Adenocarcinoma Via Integrated Bioinformatics Analyses

https://doi.org/10.21203/rs.3.rs-936587/v1

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published 01 Oct, 2023

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Background: Armadillo repeat-containing 10 (ARMC10) is involved in the progression of multiple types of tumors. Pancreatic adenocarcinoma (PAAD) is a lethal disease with poor survival and prognosis.

Methods: We acquired the data of ARMC10 in PAAD patients from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) datasets and compared the expression level with normal pancreatic tissues. We evaluated the relevance between ARMC10 expression and clinicopathological factors, immune infiltration degree and prognosis in PAAD.

Results: High expression of ARMC10 was relevant to T stage, M stage, pathologic stage, histologic grade, residual tumor, primary therapy outcome (P<0.05) and related to lower Overall-Survival (OS), Disease-Specific Survival (DSS), and Progression-Free Interval (PFI). Gene set enrichment analysis showed that ARMC10 was related to methylation in neural precursor cells (NPC), G alpha (i) signaling events, APC targets, energy metabolism, potassium channels and IL10 synthesis. The expression level of ARMC10 was positively related to the abundance of T helper cells and negatively to that of plasmacytoid dendritic cells (pDCs). Knocking down of ARMC10 could lead to lower proliferation, invasion, migration ability and colony formation rate of PAAD cells in vitro.

Conclusions: Our research firstly discovered ARMC10 as a novel prognostic biomarker for PAAD patients and played a crucial role in immune regulation in PAAD.

Cancer Biology

ARMC10

Pancreatic adenocarcinoma

Immune infiltration

Nomogram

The Cancer Genome Atlas (TCGA)

Gene Expression Omnibus (GEO)

Pancreatic adenocarcinoma (PAAD) is an aggressive disease with a low survival rate and a poor clinical prognosis[1]. Recent data has shown that PAAD is the third leading cause of deaths in cancers, projected to account for 7.8% of all cancer-related mortality in 2020 [2]. Albeit exploring for a long time, there is still few satisfactory results on early prognosis and therapy of PAAD, and most patients suffer from PAAD still present with surgically unresectable and widespread metastasis[1, 3]. In recent years, various biomarkers have been uncovered to promote or inhibit the procession of pancreatic cancer on molecular levels, such as miRNA[4–6], lncRNA[7–9], circRNA[10–12] and protein [13–15]. However, neither specificity nor sensitivity of these found biomarkers in early diagnosis and therapy is satisfied. Therefore, identification of novel biomarker and effective treating target are urgently needed in terms of curing pancreatic cancer and prolonging the survival time of patients.

Armadillo repeat-containing X-linked protein (ARMCX) is a family of proteins which is encoded by genes with multiple armadillo repeats located on the X chromosome. Armadillo repeat-containing 10 (ARMC10), a close phylogenetically related gene, is considered to be the ancestor gene of ARMCX family [16]. This protein family localizes to the mitochondria, regulates mitochondrial dynamics and is well-known to regulate protein-protein interaction (PPI) involved in nuclear transport, cellular connection, and transcription activation [17, 18]. Recent studies have shown that the members of the ARMCX family are involved in human carcinogenesis and tumor progression, such as lung, ovarian, gastric, colorectal and hepatocellular carcinomas, in terms of cell migration, cell proliferation, signal transduction and maintenance of integrating cell constitution [18–23]. However, the relationship and intrinsic mechanism between ARMC10 and PAAD is still indistinct.

Thus, to explore the role of ARMC10 in development and aggravation of PAAD, we viewed and analysed RNA sequencing data from TCGA, GEO datasets (http://www.tcga.org; https://www.ncbi.nlm.nih.gov/gds/) and a series of statistical and bioinformatics methods such as differentially expressed genes (DEGs) analysis, Kaplan-Meier (KM) survival analysis, logistic regression analysis, cox regression analysis, nomogram, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, gene set enrichment analysis (GSEA) and single sample gene set enrichment analysis (ssGSEA). To deeply dig how ARMC10 acts in PAAD, we knockdown the expression of ARMC10 in vitro and gained more compelling evidence of how ARMC10 affects the ability of proliferation, invasion, and migration of PAAD cells.

2.1. Data Collection and Preprocessing

The RNA expression level data and clinical characteristics of PAAD patients were acquired from TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/gds/). The exclusion criteria were Overall-Survival (OS) less than 30 days and normal pancreatic tissues. Transformed from the HTSeq-FPKM information of level 3, Transcripts Per Million (TPM) information of 179 pancreatic samples from TCGA were applied for following analyses. Unavailable clinical factors in 179 samples were viewed as missing values and the information were shown in Supplemental Table 1.

2.2. ARMC10 Differentially Expressed in PAAD Tissues in the TCGA Databases

We performed scatter plots and boxplots with considering disease state (normal and tumor) as variable to gauge unaccord expression of ARMC10. Receiver operating characteristic (ROC) curves were wielded to estimate the diagnostic capability of ARMC10. Expression of ARMC10 ranked below the median statistically was defined as ARMC10-low, and ARMC10-high as ARMC10 above the median.

2.3. The Identification of DEGs Between ARMC10-Low and -High Expression PAAD Groups

Applying student’s t-test, DESeq2 (4.0) package was used to analyzed DEGs between ARMC10-low and ARMC10-high samples from TCGA database. Differences with the absolute log2-fold change (FC) higher than 1.5 and the adjusted p value < 0.05 were considered to be statistically different. All of the DEGs were displayed in the volcano plots and heat map.

2.4. Functional Enrichment and Evaluation of Immune Related Cell Infiltration

To probe the enrichment of pertinent DEGs of ARMC10, Metascape (http://metasape.org) was applied by pathway and process. The criterion is set as following: the enrichment factor >1.5, a minimum count of 3, and P <0.01 to attain saliently statistical differences. In order to analyze the ARMC10-related pathways and 6 phenotypes, we explored the discrimination on pathways between ARMC10-low and ARMC10-high patients and performed GSEA with the ARMC10 differentially expressed matrix. To scan the obvious changes in signal pathways, we performed a permutation test with 1000 times. FDR < 0.25 and adjusted P < 0.01 were affirmed as significantly related genes. Using R package clusterProfiler (4.0), graphical plots and statistical analysis were performed. Applying STRING database, we selected the PPI pairs whose interaction score>0.95 by constructing the PPI network by DEGs. The ssGSEA was applied to query expression data of genes in published gene lists in order to quantify the relative infiltration levels of 24 types of immune cells, which included 509 genes, and the signatures applied contained a multiple set of innate and adaptive immune cell types. Spearman correlation and Wilcoxon test were used to demonstrate the association between the infiltration degree of immune cells and ARMC10, as well as the different groups of ARMC10 expression accompanied by immune cells infiltration.

2.5. Clinical Evaluation on Prognosis State, Model Construction and Estimation

With the application of R package (V3.6.2), the compact correlation between ARMC10 and clinicopathological characteristics was analyzed integrally by logistic regression and Wilcoxon signed-rank sum test. For further exploration, we obtained and dig the clinical pathologic factors related to 10-year OS, Disease-Specific Survival (DSS) and Progression-Free Interval (PFI) from TCGA database using KM survival analysis and Cox regression. We chose Multivariate Cox analysis to construct the interior relationship between expression level of ARMC10 and series of clinical performance, such as survival rate and other clinical pathologic factors (T stage, M stage, pathologic stage, histologic grade, residual tumor, primary therapy outcome). Median of ARMC10 expression was set as the cut-off value. P < 0.05 was set as statistically significant in all tests we did above. Using KM analysis accompanied by a two-sided log rank test, we analyzed the difference of OS, DSS, PFI between ARMC10 -low and -high group. We used the independent prognostic factors acquired from multivariate Cox regression to construct a nomogram, so as to evaluate the survival potential for 1-, 3-, and 5-year, respectively. We constructed nomograms, which included calibration plots and significant clinical factors using RMS package (https://cran.r-project.org/web/packages/rms/index.html). Based on the observed occurrences, we drafted the nomogram predicted probabilities, and graphically evaluated the calibration curves, the 45 degrees line indicated the best predictive values. 1,000 resamples were calculated by a bootstrap approach, then we performed a concordance index (C-index), which was applied to estimate the discrimination power of this nomogram. Using C-index, we evaluated the separate prognostic factors and predictive accuracies of the nomogram. All the statistical tests in this research were two tailed and the statistical significance level was set at 0.05.

2.6. Cell Culture and Transfection

PAAD cell lines (HPNE, AsPC-1, BxPC-3, CFPAC-1, Mia, Panc-1, PATU8988) were acquired from the American Type Culture Collection (Virginia, USA). The cells were cultured in Dulbecco’s Modified Eagle Medium (Hyclone) with 10% fetal bovine serum (Gibco) and incubated at 37◦C with 5% carbon dioxide. The siARMC10 (5’- GGAGAUUCUUCUUCGAGUATT-3’) and siNC were bought from RiboBio (Guangzhou, China) and introduced into cells at a concentration of 50 nM. Transfections were performed using OPTI-MEM (Invitrogen) and Lipofectamine 3000 under the manufacturer’s instructions. After that, the cells were harvested for 48 hours.

2.7. Western Blotting

The cells were cultured till the confluence reached 70 percent. The western blotting (WB) was performed as previously recorded. In brief, 25 µg proteins were subjected to Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis (SDS-PAGE) using a 10% gel. The WB experiment was performed using the anti-ARMC10 at 1:1000 dilution (Catalog number: 20506-1-AP) and anti-GAPDH at 1:5000 dilution (Catalog number: 10494-1-AP) bought from Proteintech Group (Rosemont, United States).

2.8. Cell Proliferation, Invasion, and Migration Assays

To detect the proliferation level of PAAD cells in different groups, we used the Cell Counting Kit-8 (CCK-8) and colony formation assays. A total of 2500 PAAD cells were added into each well of 96-well plate with 10 µL of CCK-8 solution (Beijing Solarbio Science & Technology Co., Ltd, Bei, China). After an incubation at 37°C for 2h, the absorbance of each well was analyzed at 450nm. In colony formation experiment, 1000 cells in different groups were added into each well of 6-well plate. The cell culture medium was replaced every 72 hours. When the appearance of colonies showing, crystal violet and 4% paraformaldehyde were applied to stain and fix the cells. Then the wound healing and transwell assays were used to evaluate the ability of cellular invasion and migration.

2.9. Statistical Analysis

RStudio software and the R software (version 3.8.0) were used for statistical analyses. Data analyses were processed using one-way analysis of variance (ANOVA) and two-tailed Student’s t-test. Statistically significance of the difference was set at p–value < 0.05.

3.1. ARMC10 expression in Pan-cancers and PAAD

We analyzed the expression data of pan-cancer tissues and normal controls based on TCGA and Genotype-Tissue Expression (GTEx) datasets. ARMC10 expression was significantly elevated in adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), breast filtrating carcinoma (BRCA), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), diffuse large B cell lymphoma (DLBCL), esophageal carcinoma (ESCA), pleomorphic glioma (GBM), Head and Neck squamous cell carcinoma (HNSC), renal clear cell carcinoma (KIRC), renal papillary cell carcinoma (KIRP), brain low grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), ovarian serous cystadenocarcinoma (OV), PAAD, prostate cancer (PRAD), rectum adenocarcinoma (READ), skin melanoma (SKCM), gastric cancer (STAD), testicular germ cell tumors (TGCT), thyroid cancer (THCA), thymic cancer (THYM), endometrial cancer (UCEC) and down-regulated in acute myeloid leukemia (LAML) based on the Wilcoxon test (P<0.05; Figure 1A). Besides, to detect the diagnostic effects of ARMC10, which was found to be higher expressed in tumor tissues than that in normal controls in three different datasets (P < 0.05; Figure 1B-D), the ROC curves were plotted. As shown in Figure 1E, the area under the curve (AUC) of ARMC10 is 0.957, indicating it may be a potential diagnostic biomarker for PAAD.

3.2. Recognition of DEGs in PAAD

We compared 90 ARMC10-high PAAD samples with 89 ARMC10-low PAAD samples and found a total of 668 DEGs with statistical significance (adjusted p-value < 0.05, absolute log2-FC> 1.5) (Figure 1F; Supplementary Table 2). Using DESeq2 package, we analyzed DEGs in HTSeq-Counts. The top 10 DEGs ranked by relative expression were shown in Figure 1G.

3.3. Functional Enrichment Analysis of ARMC10-associated Genes in PAAD

To further study the biological functions of ARMC10-associated genes, the DEGs were subjected to the GO analysis using Metascape and classified into Biological Processes (BPs), Cellular Components (CCs), and Molecular Functions (MFs) categories. The results showed that cellular potassium ion transport, regulation of membrane potential, glutamate receptor signaling pathway, cation channel complex, transmembrane transporter complex, glutamate receptor activity and growth factor activity were related to modulation of ARMC10-associated genes (Figure 2A; Supplementary Table 3).

3.4. Protein-Protein Interaction (PPI) Network Analysis

A PPI network was framed grounded on the STRING dataset to identify the association among the 1033 DEGs in PAAD group with the high confidence of interaction score was set as 0.70. We found that 311 proteins and 566 edges presented significant disease associations and 16 clusters of hub genes were picked from the PPI network with scores ≥ 5000 (Supplementary Figure1A-Q; Supplementary Table 4). Thereinto, the top 10 hub genes were CBX3, USP39, TMEM41A, RPE, RALB, BRSK2, AMY1B, TMEM72, LINC01625 and CCKBR.

3.5. Potential Mechanism of ARMC10 in the progression of PAAD

Using GSEA, we compared the expression data between ARMC10-high and -low samples to excavate ARMC10-related signaling pathways and significant differences (nominal, NOM p value < 0.05; false discovery rate, FDR q value < 0.25) in enrichment of the Molecular Signatures Database (MSigDB) Collection (c2.cp.reactome/biocarta/kegg.v6.2.symbols.gmt). The top significantly enriched pathways were selected according to their normalized enrichment score (NES). Enrichment plots of GSEA showed that Genes with high-CpG-density promoters (HCP) bearing histone H3 dimethylation mark at K4 (H3K4me2) in neural precursor cells (NPC), G alpha (i) signaling events, APC targets, integration of energy metabolism, potassium channels, FCGR3A mediated IL10 synthesis, were significantly enriched in ARMC10-high patients (Figure 2B-G; Supplementary Table 5).

3.6. Relevance Between ARMC10 Expression Level and Immune Infiltration

The ssGSEA was performed in PAAD samples and relevance between ARMC10 expression level and immune infiltration was demonstrated using Spearman correlation. As shown in Figure 3, the expression level of ARMC10 was positively relevant to the abundance of T helper 2 (Th2) cells (R=0.530, P<0.001), T helper cells (R=0.220, P=0.003) and negatively related to the abundance of plasmacytoid dendritic cells (pDC) (R=-0.380, P<0.001).

3.7. Relevance Between ARMC10 Expression Level and Clinicopathological Variables

To better seize the potential mechanisms of ARMC10 expression in PAAD, we explored its association with clinical characteristics of PAAD patients from TCGA. As the results showed, higher ARMC10 expression was relevant to clinical T stage (T3&4 vs. T1&2, P=0.003), clinical M stage (M1 vs. M0, P=0.043), pathologic stage (Stage II-IV vs. Stage I, P=0.029), residual tumor (R1&2 vs. R0, p=0.03), histologic grade (G3/G4 vs. G1, P<0.001; G2 vs. G1, P<0.001), primary therapy endpoint (PR&CR vs. PD&SD, P=0.018), OS event (Dead vs. Alive, P<0.001), DSS event (Dead vs. Alive, P<0.001) and PFI event (Dead vs. Alive, P<0.001) (Table1, Figure4). Using univariate logistic regression, we found that ARMC10 expression level in PAAD was positively associated with T stage (T3&4 vs. T1&2, OR=5.122, P=0.003), pathologic stage (Stage II-IV vs. Stage I, OR=4.609, P=0.011), primary therapy outcome (PR&CR vs. PD&SD, OR=0.353, P=0.040), Residual tumor (R1&2 vs. R0, OR=2.757, P=0.034) (Table2). The results above indicated that PAAD patients with higher ARMC10 expression were more likely to develop into advanced stage.

3.8. Relevance Between ARMC10 Expression Level and prognosis of PAAD patients

As shown in Figure5A-C, patients with higher expression of ARMC10 have lower OS (HR=2.45, P<0.001), DSS (HR=2.57, P<0.001), and PFI (HR=2.12, P<0.001) compared to those with lower expression. Furthermore, we performed subgroup analysis and found that higher ARMC10 expression had prognostic value for lower OS in PAAD patients of T1-T4 stage, N0& N1, pathologic stage I& II, G1& G2 histologic grade, head of pancreas neoplasm and male (Figure5D-K). We also performed subgroup analysis of DSS and PFI in PAAD patients with high ARMC10 expression (Seen in Supplementary Table8, 9). Besides, we did univariate Cox regression and discovered that higher ARMC10 expression was correlated to lower OS (HR: 2.424; CI: 1.567-3.750; P <0.001; Table3), DSS (HR: 2.560; CI: 1.555-4.216; P <0.001; Supplementary Table6) and PFI (HR: 2.246; CI: 1.506-3.349; P <0.001; Supplementary Table7). Then using multivariate Cox regression, we excavated factors correlated to prognosis including ARMC10 expression, anatomic neoplasm subdivision, histology grade, residual tumor, T, N and pathologic stage, radiation therapy and primary therapy outcome. It showed that high ARMC10 was also relevant to poor OS (HR: 2.797; CI: 1.581-4.950; P <0.001; Table3), DSS (HR: 2.681; CI: 1.432-5.018; P =0.002; Supplementary Table6) and PFI (HR: 2.063; CI: 1.227-3.468; P =0.006; Supplementary Table7). Selected from Cox regression results, multiple subgroups of PAAD patients with higher ARMC10 expression had lower OS, DSS and PFI (Seen in Table4, Supplementary Table8, 9).

Table 1: Relevance Between ARMC10 Expression Level and Clinicopathological Variables

Characteristic	levels	Low expression of ARMC10	High expression of ARMC10	p
n		89	89
T stage, n (%)	T1	4 (2.3%)	3 (1.7%)	0.024
	T2	18 (10.2%)	6 (3.4%)
	T3	63 (35.8%)	79 (44.9%)
	T4	2 (1.1%)	1 (0.6%)
N stage, n (%)	N0	26 (15%)	24 (13.9%)	0.829
	N1	60 (34.7%)	63 (36.4%)
M stage, n (%)	M0	35 (41.7%)	44 (52.4%)	0.386
	M1	1 (1.2%)	4 (4.8%)
Pathologic stage, n (%)	Stage I	15 (8.6%)	6 (3.4%)	0.069
	Stage II	68 (38.9%)	78 (44.6%)
	Stage III	2 (1.1%)	1 (0.6%)
	Stage IV	1 (0.6%)	4 (2.3%)
Radiation therapy, n (%)	No	55 (33.7%)	63 (38.7%)	0.398
	Yes	25 (15.3%)	20 (12.3%)
Primary therapy outcome, n (%)	PD	19 (13.7%)	30 (21.6%)	0.261
	SD	3 (2.2%)	6 (4.3%)
	PR	4 (2.9%)	6 (4.3%)
	CR	39 (28.1%)	32 (23%)
Age, n (%)	<=65	48 (27%)	45 (25.3%)	0.764
	>65	41 (23%)	44 (24.7%)
Race, n (%)	Asian	5 (2.9%)	6 (3.4%)	0.285
	Black or African American	1 (0.6%)	5 (2.9%)
	White	81 (46.6%)	76 (43.7%)
Gender, n (%)	Female	43 (24.2%)	37 (20.8%)	0.451
	Male	46 (25.8%)	52 (29.2%)
Histologic grade, n (%)	G1	24 (13.6%)	7 (4%)	0.001
	G2	44 (25%)	51 (29%)
	G3	18 (10.2%)	30 (17%)
	G4	2 (1.1%)	0 (0%)
Residual tumor, n (%)	R0	61 (37.2%)	46 (28%)	0.020
	R1	18 (11%)	34 (20.7%)
	R2	3 (1.8%)	2 (1.2%)
Anatomic neoplasm subdivision, n (%)	Head of Pancreas	70 (39.3%)	68 (38.2%)	0.857
	Other	19 (10.7%)	21 (11.8%)
Alcohol history, n (%)	No	34 (20.5%)	31 (18.7%)	0.658
	Yes	48 (28.9%)	53 (31.9%)
Smoker, n (%)	No	36 (25%)	29 (20.1%)	0.482
	Yes	38 (26.4%)	41 (28.5%)
History of chronic pancreatitis, n (%)	No	68 (48.2%)	60 (42.6%)	0.076
	Yes	3 (2.1%)	10 (7.1%)
History of diabetes, n (%)	No	52 (35.6%)	56 (38.4%)	0.774
	Yes	20 (13.7%)	18 (12.3%)

Table 2: Univariate logistic regression between ARMC10 expression level and clinical characteristics

Characteristics	Total(N)	Odds Ratio(OR)	P value
T stage (T3&T4 vs. T1&T2)	176	5.122 (1.804-15.704)	0.003
N stage (N1 vs. N0)	173	1.208 (0.480-3.010)	0.685
M stage (M1 vs. M0)	84	15.330 (0.839-469.031)	0.080
Pathologic stage (Stage II&Stage III&Stage IV vs. Stage I)	175	4.609 (1.432-15.426)	0.011
Radiation therapy (Yes vs. No)	163	0.928 (0.378-2.330)	0.872
Primary therapy outcome (PR&CR vs. PD&SD)	139	0.353 (0.124-0.913)	0.040
Gender (Male vs. Female)	178	1.520 (0.684-3.459)	0.307
Race (White vs. Asian&Black or African American)	174	0.422 (0.096-1.673)	0.236
Age (>65 vs. <=65)	178	1.047 (0.472-2.335)	0.910
Residual tumor (R1&R2 vs. R0)	164	2.757 (1.120-7.370)	0.034
Histologic grade (G3&G4 vs. G1&G2)	176	1.788 (0.730-4.636)	0.215
Anatomic neoplasm subdivision (Other vs. Head of Pancreas)	178	0.836 (0.328-2.180)	0.708
Smoker (Yes vs. No)	144	1.249 (0.524-3.020)	0.615
Alcohol history (Yes vs. No)	166	1.124 (0.481-2.620)	0.784
History of diabetes (Yes vs. No)	146	0.564 (0.211-1.493)	0.246
History of chronic pancreatitis (Yes vs. No)	141	3.447 (0.708-19.167)	0.141

Table 3: Univariate and multivariate survival results (Overall Survival) of prognostic covariates in PAAD patients.

Characteristics	Total(N)	Univariate analysis		Multivariate analysis
Characteristics	Total(N)	Hazard ratio (95% CI)	P value	Hazard ratio (95% CI)	P value
T stage (T3&T4 vs. T1&T2)	176	2.023 (1.072-3.816)	0.030	1.099 (0.385-3.139)	0.860
N stage (N1 vs. N0)	173	2.154 (1.282-3.618)	0.004	2.355 (1.086-5.107)	0.030
M stage (M1 vs. M0)	84	0.756 (0.181-3.157)	0.701
Pathologic stage (Stage II&Stage III&Stage IV vs. Stage I)	175	2.291 (1.051-4.997)	0.037	0.391 (0.084-1.824)	0.232
Radiation therapy (Yes vs. No)	163	0.508 (0.298-0.866)	0.013	0.381 (0.196-0.742)	0.005
Primary therapy outcome (CR&PR vs. PD&SD)	139	0.425 (0.267-0.677)	<0.001	0.489 (0.289-0.826)	0.007
Gender (Male vs. Female)	178	0.809 (0.537-1.219)	0.311
Race (White vs. Asian&Black or African American)	174	1.161 (0.582-2.318)	0.672
Age (>65 vs. <=65)	178	1.290 (0.854-1.948)	0.227
Residual tumor (R1&R2 vs. R0)	164	1.645 (1.056-2.561)	0.028	1.438 (0.841-2.458)	0.184
Histologic grade (G3&G4 vs. G1&G2)	176	1.538 (0.996-2.376)	0.052	1.709 (0.974-2.997)	0.062
Anatomic neoplasm subdivision (Other vs. Head of Pancreas)	178	0.417 (0.231-0.754)	0.004	0.418 (0.194-0.900)	0.026
ARMC10 (High vs. Low)	178	2.424 (1.567-3.750)	<0.001	2.797 (1.581-4.950)	<0.001

Table 4: The prognostic value of ARMC10 (Overall Survival) in multiple PAAD subgroups.

Characteristics	N (%)	HR (95% CI)	P value
T stage
T1&T2	31 (17.6%)	5.35 (1.13-25.25)	0.034
T3&T4	145 (82.4%)	1.71 (1.09-2.70)	0.021
N stage
N0	50 (28.9%)	4.18 (1.36-12.83)	0.013
N1	123 (71.1%)	1.73 (1.07-2.80)	0.026
M stage
M0	79 (94%)	1.64 (0.85-3.16)	0.140
M1	5 (6%)	-	-
Pathologic stage
Stage I-Stage II	167 (95.4%)	2.23 (1.44-3.47)	0.000
Stage III-Stage IV	8 (4.6%)	-	-
Histologic grade
G1& G2	126 (71.6%)	3.75 (2.12-6.64)	0.000
G4&G3	131 (37)	1.05 (0.52-2.13)	0.886

3.9. A Nomogram Constructed to Predict Survival Rates of PAAD Patients

We further constructed a nomogram based on ARMC10 expression and some related clinicopathologic variables to predict the survival rates of PAAD patients (Figure6A). The point scale of nomogram was set as 0-100 assigned to the variables according to the multivariate Cox regression and the total scores were calculated. The predictive value of each variable for prognosis was defined as vertical dimension from the total score axis to the outcome axis. For instance, a PAAD patient with high ARMC10 expression (97.5 points), N1 stage (62 points), R0 residual (0 points), G3 histologic grade (55 points) tumor located on head of pancreas (80 points), radiation therapy YES (0 points), PD (77.5 points) has a total point of 372. Correspondingly, the 1-, 3- and 5-year survival probability are 65%, 14% and 6%, respectively. Then using Hosmer test of the calibration curve, we evaluated the predictive validity of the nomogram in the TCGA-PAAD cohort. The C-index was 0.727 (95% CI 0.696-0.758), indicating a moderate predictive accuracy of this nomogram model (Figure6B).

3.10. Knockdown of ARMC10 Alleviated Malignant Phenotype of PAAD in vitro

To better recognize the role of ARMC10 in PAAD, we observed the expression of ARMC10 in some PAAD cell lines (HPNE, CFPAC-1, Aspc-1, Bxpc-3, Panc-1, PATU8988, Mia). As shown in Figure7A, the expression level of ARMC10 was higher in Bxpc-3 and Aspc1 than in other cell lines. Thus, further analysis was performed in Bxpc-3 and Aspc1 cell lines. Using western blotting, the transfection efficiency of si-ARMC10 was identified (Figure7B). We performed colony formation and CCK8 experiments and the results showed that inhibition of ARMC10 could lead to lower proliferation and colony formation rate in Bxpc-3 and Aspc1 cells (Figure7C-E). Besides, the result of wound healing assay showed that lower expression of ARMC10 could restrain the migration ability of Bxpc-3 and Aspc1 cells (Figure7F, G). Then using transwell experiment, we found that cells after ARMC10 knockdown had lower invasion ability (Figure7H, I). These results demonstrated that ARMC10 played an important role in PAAD progression. Further studies are required to excavate the underlying mechanisms.

The armadillo genes, also named as X-linked (ARMCX or ALEX) for being clustered on the X chromosome, encode a new family of proteins [16]. They are reported to be connected with many biological processes, such as embryogenesis and tumorigenesis, cell assembly, RNA modification, DNA replication, nuclear transport, mitochondrial fission and fusion, transport in neurons and execute different functions through binding its armadillo repeat domain with different binding partners [17, 18, 24, 25]. A research demonstrated that ARMCX proteins regulate mitochondrial dynamics and trafficking in neurons through interacting with the Kinesin/Miro/Trak2 complex [17]. ARMC10 alteration in neurons regulate mitochondrial trafficking through KIF5/Miro1-2/Trak2 complex [16]. Besides, phosphorylation of the S45 site of ARMC10 participating in regulating mitochondrial fission and fusion through AMPK-dependent signaling pathways [26]. Several previous studies have demonstrated that ARMCX are related to the process of several diseases, especially in several carcinomas. ALEX1 and ALEX2 was speculated to be novel tumor suppressors in several human carcinomas, such as lung, prostate, colon, pancreas and ovarian [21]. The lost or significantly reduced expression of ALEX1 in colorectal and pancreatic cell lines were found to be regulated by cAMP-response element binding protein (CREB) and Wnt/β-catenin signaling in transcriptional level [27]. Besides, ARMCX also was verified to be a potential prognostic indicator of gastric carcinoma. The combination of lower ARMCX1 and ARMCX2 was related to higher OS [18]. ARMCX, located at the outer membrane of mitochondria, was reported to promote signal transduction between mitochondria and nucleus through interactions with Sox10 [28]. Down-regulation of ARMCX3 also was reported to have a protective effect against hepatocarcinogenesis mediated through Sox9 by affecting hepatic cell proliferation [23]. Through AKT/Slug/E-cadherin pathway, ALEX3 could inhibit the invasion of non-small cell lung cancer and predict clinical outcome [19]. A previous research has identified an ARMC10-BRAF fusion in malignant melanoma [29]. But researches about ARMC10 in carcinomas are still facing a severe shortage.

In our study, we first discovered that ARMC10 was differentially expressed in several types of cancers, including PAAD. The prognostic effect of ARMC10 in PAAD is relatively high with an AUC of 0.957. We then performed GO and GSEA to excavate the function of ARMC10. We discovered that ARMC10 was significantly relevant to glutamate receptor activity and signaling pathway, cellular potassium ion transport, cation channel complex, transmembrane transporter complex. Several studies have illustrated the role of glutamate receptor in the pathophysiology mechanisms underlying multiple cancers, including breast cancer, glioblastoma, squamous cell lung cancer, prostate cancer, bladder cancer and melanoma [30–35]. Besides, researches have demonstrated the critical role of cation channels in the progression of various type of cancer, especially transient receptor potential cation channel subfamily V member 4 (TRPV4) [36–38], transient receptor potential cation channel subfamily M member 7/8 (TRPM7/8) [39–42] and two-pore channel 2 (TPC2) [43, 44]. TPC2 is one of the cation channels located on endolysosomal membranes that could speed up movement between organelles and regulate phagocytosis, autophagy and lysosomal exocytosis [43]. In HCC, TPC2 knockout inhibited cell proliferation and tumor growth through affecting energy metabolism [44]. Another nonselective cation channel TRPM7 involved in Ca²⁺ and Mg²⁺ homeostasis mainteinance has been found to manage cell differentiation, proliferation and migration. TRPM7 suppression in triple-negative breast cancer (TNBC) cells could amplify tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)- induced antiproliferation and apoptosis [42]. In PAAD, TRPM7 was higher expressed in tumor tissues compared to the normal and correlated to poor prognosis. The study unveiled that TRPM7 affected BxPC-3 cell migration via a Mg²⁺-dependent mechanism [41]. But the precise mechanism in PAAD is still unclear. From the GSEA results, we similarly found potassium channels were enriched. Potassium channel modulatory factor 1 (KCMF1) has previously been found to be upregulated in PAAD tissues with a pro-oncogenic function affecting tumor progression [45]. The potassium channel K_V 11.1 in PDAC cells exerts a critical role on cell migration and metastatic progression through f-actin re-organization [46]. We also discovered that APC targets and H3K4ME2 were associated with ARMC10. Adenomatous polyposis coli (APC) gene has been previously found to have a Wnt/β-catenin dependent effect on tumorigenesis and could regulate cell proliferation and migration via activating long non-coding RNA [47–49]. Lysine-specific demethylase 1 (LSD1), a chromatin regulator and its target H3K4ME2 often increased and contributed to cell proliferation, migration and apoptosis inhibition in cancer [50, 51]. Moreover, we constructed the PPI network and discovered multiple pathways and biological processes related to those hub genes. But their relationship with ARMC10 was firstly been put forward and the precise mechanism needed further exploration.

Tumor microenvironment has been brought into focus in the progression of cancer for a long time, especially immune infiltrates [52]. Tumor-infiltrating immune cells (TIICs) have close connection with prognosis in various cancers [53, 54]. In our study, we detected that ARMC10 expression was positively relevant to the infiltration of Th2 cells, T helper cells and negatively relevant to pDC cells infiltration. In addition, we found the relevance between ARMC10 expression and several clinicopathological factors in PAAD patients, including higher clinical T/M stage, higher pathologic stage, R1&R2 residual tumor, histologic grade G3/G4, indicating that ARMC10 may be a prognostic biomarker of PAAD concerning with immune infiltration. pDC could secrete type I IFN, present antigen to naïve T cells and so was related to cancer immunology [55–57]. Current trials have shown that pDCs could generate anti-tumor CD8 T cell immunity and had a suppressive role in tumor progression [58]. The neoantigen-induced tumor progression in PAAD due to pathogenic Th17 response was associated with infiltrating conventional DCs (cDCs) and cDC function restoration had multiple roles in different stage of PAAD [59]. Thus, it has been an important target to improve cancer immunotherapy [60]. It can be concluded that pDC in highly expressed ARMC10 patients may affect antigen-specific immunity and mediate tumor progression. As for the clinical factors, ARMC10 was shown to be an independent prognostic indicator of poor OS, DSS and PFI in PAAD patients. The constructed nomogram showed a great prediction of 1-, 3-, 5- year survival with the C-index of 0.727 (95%CI: 0.696-0.758). Then, the influence of ARMC10 on PAAD cell proliferation, invasion and migration was examined in vitro. We discovered that ARMC10 suppression could alleviate malignant phenotype of PAAD, indicating the probability of ARMC10 to be the potential therapeutic target. The critical role of immune infiltration and ARMC10 in PAAD needs further explorations.

There still exists some limitations in our study. The clinical factors involved in our prediction model are not comprehensive enough. But as our data was acquired from public databases, the missing of some clinical factors was inevitable due to the heterogeneity of different clinical centers. Because of the restricted condition, the relevance between ARMC10 expression and PAAD prognosis was failed to be verified in another large cohort. In addition, subject to the analytical methods, the associated functions and pathways must not be limited to the results in our study. So, the exploitable role of ARMC10 in PAAD and underlying mechanisms need more studies in depth.

Collectively, we put forward the prognostic value of ARMC10 in PAAD patients with regard to glutamate receptor, cation channels, histone modification and immune infiltration, etc. We also revealed the association between ARMC10 and clinicopathological significance in PAAD, indicating ARMC10 may be an exploitable therapeutic target. The in-depth molecular mechanism and concrete application for clinical conditions need more studies afterwards.

ARMC10

Armadillo repeat-containing 10

PAAD

Pancreatic adenocarcinoma

TCGA

The Cancer Genome Atlas

GEO

gene expression omnibus

Overall-Survival

DSS

Disease-Specific Survival

PFI

Progression-Free Interval

ARMCX/ALEX

Armadillo repeat-containing X-linked protein

PPI

protein-protein interaction

DEGs

differentially expressed genes

Kaplan-Meier

gene ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

GSEA

gene set enrichment analysis

ssGSEA

single sample gene set enrichment analysis

TPM

Transcripts Per Million

ROC

Receiver operating characteristic

fold change

western blotting

SDS-PAGE

Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis

CCK-8

Cell Counting Kit-8

GTEx

Genotype-Tissue Expression

ACC

adrenocortical carcinoma

BLCA

bladder urothelial carcinoma

BRCA

breast filtrating carcinoma

CHOL

cholangiocarcinoma

COAD

colon adenocarcinoma

DLBCL

diffuse large B cell lymphoma

ESCA

esophageal carcinoma

GBM

pleomorphic glioma

HNSC

Head and Neck squamous cell carcinoma

KIRC

renal clear cell carcinoma

KIRP

renal papillary cell carcinoma

LGG

brain low grade glioma

LIHC

liver hepatocellular carcinoma

LUAD

lung adenocarcinoma

LUSC

lung squamous cell carcinoma

ovarian serous cystadenocarcinoma

PRAD

prostate cancer

READ

rectum adenocarcinoma

SKCM

skin melanoma

STAD

gastric cancer

TGCT

testicular germ cell tumors

THCA

thyroid cancer

THYM

thymic cancer

UCEC

endometrial cancer

LAML

acute myeloid leukemia

AUC

area under the curve

BPs

Biological Processes

CCs

Cellular Components

MFs

Molecular Functions

MSigDB

the Molecular Signatures Database

NES

normalized enrichment score

HCP

high-CpG-density promoters

H3K4me2

histone H3 dimethylation mark at K4

NPC

neural precursor cells

Th2 cell

T helper 2 cells

pDCs

plasmacytoid dendritic cells

CREB

cAMP-response element binding protein

TRPV4

transient receptor potential cation channel subfamily V member 4

TRPM7/8

transient receptor potential cation channel subfamily M member 7/8

TPC2

two-pore channel 2

TNBC

triple-negative breast cancer

TRAIL

tumor necrosis factor-related apoptosis-inducing ligand

KCMF1

Potassium channel modulatory factor 1

APC

Adenomatous polyposis coli

LSD1

Lysine-specific demethylase 1

TIICs

Tumor-infiltrating immune cells

cDCs

conventional dendritic cells

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

The data generated or used in this study were available in the article/Supplementary materials. Requests could be made to corresponding author directly.

Competing interests

The authors declare no conflict of interests.

Funding

This work was supported by the Natural Science Foundation of China (No. 81773215) and the Chinese Academy of Medical Sciences (No. 2019XK320002).

Author information

Tian-Hao Li, Xiao-Han Qin, Cheng Qin contributed equally to this work.

Affiliations

Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.

Tianhao Li, Cheng Qin, Bangbo Zhao, Hongtao Cao, Xiaoying Yang, Yuanyang Wang, Zeru Li, Xingtong Zhou & Weibin Wang

Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.

Tianhao Li, Cheng Qin, Bangbo Zhao, Hongtao Cao, Xiaoying Yang, Yuanyang Wang, Zeru Li & Weibin Wang

Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.

Xingtong Zhou

Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.

Xiaohan Qin

Authors’ contributions

Tianhao Li: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Methodology (equal); Software (equal); Writing-original draft (equal); Writing-review & editing (equal). Xiaohan Qin: Conceptualization (equal); Data curation (equal); Investigation (equal); Methodology (equal); Visualization (equal); Writing-original draft (equal); Writing-review & editing (equal). Cheng Qin: Conceptualization (equal); Data curation (equal); Writing-original draft (equal); Writing-review & editing (equal). Bangbo Zhao: Formal analysis (equal); Visualization (equal); Writing original draft (equal); Writing-review & editing (equal). Yuanyang Wang: Conceptualization (equal); Data curation (equal); Visualization (equal); Writing-review & editing (equal). Zeru Li: Data curation (equal); Formal analysis (equal); Software (equal); Visualization (equal). Hongtao Cao: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Resources (equal); Software (equal). Xiaoying Yang: Conceptualization (equal); Data curation (equal); Visualization (equal). Xingtong Zhou: Conceptualization (equal); Software (equal); Writing-review & editing (equal). Weibin Wang: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Funding acquisition (equal); Investigation (equal); Project administration (equal); Supervision (equal).

Corresponding authors

Correspondence to Weibin Wang.

Email: [email protected]

Acknowledgements

Not applicable.

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Prognostic Value and Immune Infiltration of ARMC10 in Pancreatic Adenocarcinoma Via Integrated Bioinformatics Analyses

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Journal Publication

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Abstract

Figures

1. Background

2. Materials And Methods

2.1. Data Collection and Preprocessing

2.2. ARMC10 Differentially Expressed in PAAD Tissues in the TCGA Databases

2.3. The Identification of DEGs Between ARMC10-Low and -High Expression PAAD Groups

2.4. Functional Enrichment and Evaluation of Immune Related Cell Infiltration

2.5. Clinical Evaluation on Prognosis State, Model Construction and Estimation

2.6. Cell Culture and Transfection

2.7. Western Blotting

2.8. Cell Proliferation, Invasion, and Migration Assays

2.9. Statistical Analysis

3. Results

3.1. ARMC10 expression in Pan-cancers and PAAD

3.2. Recognition of DEGs in PAAD

3.3. Functional Enrichment Analysis of ARMC10-associated Genes in PAAD

3.4. Protein-Protein Interaction (PPI) Network Analysis

3.5. Potential Mechanism of ARMC10 in the progression of PAAD

3.6. Relevance Between ARMC10 Expression Level and Immune Infiltration

3.7. Relevance Between ARMC10 Expression Level and Clinicopathological Variables

3.8. Relevance Between ARMC10 Expression Level and prognosis of PAAD patients

3.9. A Nomogram Constructed to Predict Survival Rates of PAAD Patients

3.10. Knockdown of ARMC10 Alleviated Malignant Phenotype of PAAD in vitro

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Supplementary Files

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