PAMR1 is a favorable diagnostic and prognostic biomarker in hepatocellular carcinoma

doi:10.21203/rs.3.rs-2114251/v1

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PAMR1 is a favorable diagnostic and prognostic biomarker in hepatocellular carcinoma

https://doi.org/10.21203/rs.3.rs-2114251/v1

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Peptidase domain containing associated with muscle regeneration 1 (PAMR1) is downregulated in breast cancer and cervical cancer. This study aimed to evaluate the role of PAMR1 in hepatocellular carcinoma (HCC) and explore the underlying molecular mechanisms. Base on the analysis of datasets from the Gene Expression Omnibus and The Cancer Genome Atlas (TCGA), a lower mRNA level of PAMR1 was detected in HCC compared that in normal liver tissues. The result was also confirmed by the experiment with immunohistochemistry (IHC), and qRT-PCR. The area under the curve(AUC) was 0.918 through receiver operating characteristic (ROC) curve analysis. The Kaplan-Meier analysis revealed that lower PAMR1 expression predicted prognostic outcome. Then, the genes closely associated with PAMR1 were screened and enriched by Gene Ontology (GO) analysis, showing their role on extracellular matrix organization, cell adhesion, and blood vessel development. Moreover, PAMR1 expression was positively correlated with immune cells infiltration. In addition, Gene Set Enrichment Analysis (GSEA) showed that the downregulated genes in the low-PAMR1 subgroup were significantly enriched in an inflammatory response, hypoxia, epithelial-mesenchymal transition, KRAS signaling, and TNF-α signaling via NF-κB signaling pathway. Collectively, PAMR1 shows lower level in HCC,and represents a favorable diagnostic and prognostic factor for HCC.

Biological sciences/Computational biology and bioinformatics

Health sciences/Biomarkers

Biological sciences/Cancer

Biological sciences/Cancer/Tumour biomarkers

Biological sciences/Biological techniques

Biological sciences/Biological techniques/Bioinformatics

Hepatocellular carcinoma (HCC), the most common type of malignancy, is the third leading cause of cancer-related death worldwide in 2020 ¹. Amount of risk factors are considered to contribute to HCC, such as chronic viral hepatitis, alcohol abuse, excess body weight, toxic exposure and HCC-driver genes mutation^2,3. Because of aflatoxin exposure and chronic HBV infection, over half of HCC patients are prevalent in Asia⁴, and China is estimated to be with the most highest HCC incidence by 2030⁵.Surgery is the best approach in the treatment of HCC patients, but unfortunately, most cases are unresectable, as they are already at late stages at diagnosis. Other strategies, such as percutaneous ablation, radiotherapy, and liver transplantation, also have limited efficiency with a high postoperative recurrence rate and distant metastasis^6,7.Therefore, identification of biomarkers of diagnosis and prognosis is crucial for treatment efficacy improvment and clinical outcome prediction.

Peptidase domain-containing protein associated with muscle regeneration 1 (PAMR1) was first reported to be downregulated in muscle cell lines from five patients with Duchenne muscular dystrophy⁸. It was later found to be also expressed in normal cervix and endometrium tissue, and was correlated with psoriasis and type 2 diabetes^9,10. The injury mouse model showed that PAMR1 was upregulated in the regenerating area of injured skeletal muscle, while it was detected with downregulation in breast and cervical cancer cells. PAMR1 inactivation was induced by hypermethylation at promoter in breast cancer cells¹¹. The protein also inhibits the proliferation, migration, and invasion of cervical cancer cells¹². Therefore, PAMR1 appears to be characterized as a tumor suppressor, but its role in HCC is still unclear.

In this study, we evaluated PAMR1 expression level in HCC base on the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, followed by the experimental confirmation with qRT-PCR and immunohistochemistry. Additionally, its potential role as a prognostic factor for HCC was also investigated. Finally, Gene Set Enrichment Analysis (GSEA) was performed to clarify the underlying mechanism of PAMR1 on HCC pathogenesis.

Identification of DEGs in HCC. Base on the analysis of datasets from GEO (GSE45267, GSE101685, GSE121248, and GSE14520), we selected top 100 DEGs in each of the microarray dataset, and identified 15 overlapping genes that composed of 3 upregulated and 12 downregulated genes (Fig. 1A). Notably, PAMR1 was one of most obvious DEGs in HCC patients that was worth further investigation (adjusted P = 2.84e-25, logFC = − 1.51, Fig. 1B).

PAMR1 was downregulated in HCC and its expression predicted a favorable prognosis. It was showed the PAMR1 mRNA levels was obviously lower in HCC tissues than that in healthy liver tissue in the above four GEO datasets (P < 0.05; Fig. 2A-D), which was further confirmed by the cohort from TCGA(Fig. 2E, S1A). Although PAMR1 mRNA level was lower in HCC, its promoter methylation level was upregulated in liver cancer tissue (Fig. 2F). Additionally, the PAMR1 expression level was negatively correlated to HCC progression (Fig. 2G). and tumor grade of cervical cancer (Fig. S1B). In addition, PAMR1 expression is suppressed in multiple cancer types, including prostate adenocarcinoma, uterine corpus endometrial carcinoma, and skin cutaneous melanoma (Fig. S1C).

After bioinformatical identification of lower PAMR1 level in HCC, we used IHC to validate the result in clinical samples comprising 2 normal liver tissues and 29 HCC plus adjacent tissue pairs. The results showed obvious lower PAMR1 level in HCC compared with that in both normal and adjacent tissues (Fig. 3A), which were statistically quantified as the PAMR1 score (Fig. 3B). Moreover, the same result was obtained by qPCR from cDNA chip which contained 2 cDNA of HCC cell lines and 14 pairs cDNA of HCC plus adjacent tissues (Fig. 3C). In addition, the expresstion of PAMR1 in HCC cell lines was detected by qRT-PCR. Obviously, The results revealed that mRNA level of PAMR1 were lower in the HCC cell lines(HepG2, Hep3B, SMMC7721, Huh7) than that in normal liver line QSG7701(Fig. 3D).We evaluated the diagnostic value of PAMR1 in liver cancer by ROC curve, and the AUC was up to 0.918(95% CI 0.890–0.946). Furthermore, the sensitivity and specificity were 94.0% and 84.5%, indicating that PAMR1 has higher diagnostic efficiency for HCC detection (Fig. 3E).

Next, Kaplan-Meier analysis of a HCC cohort containing 364 cases was performed to evaluate the posibility of PAMR1 as a prognostic marker. As shown in Fig. 4, the HCC patients with low PAMR1 level were with lower 5-year overall survival (P < 0.05; Fig. 4A), progression-free survival (P < 0.05; Fig. 4B), relapsefree survival (P < 0.05; Fig. 4C), as well as diseasespecific survival (P < 0.05; Fig. 4D), suggesting its potential as a favorable prognostic biomarker.

GO enrichment analysis of PAMR1-related genes in HCC. We next used cBioPortal to identify PAMR1-related genes in HCC from TCGA transcriptome data (The top 20 genes were listed in Table 1). The genes with Spearman correlation coefficients over 0.5 were considered with high correlation to PAMR1 in LIHC. Then, top 100 co-expressed genes were analysed by GO on the DAVID platform and 20 hits were identified (Table 2), which were mainly enriched in the regulation of extracellular matrix organization, plasma membrane, and calcium ion binding.

Table 1

cBioPortal analysis of the 20 genes most closelyrelated to PAMR1.
Correlated Gene	Cytoband	Spearman's Correlation	p-Value	q-Value
SFRP1	8p11.21	0.682	2.23E-52	2.48E-48
PXDN	2p25.3	0.682	2.49E-52	2.48E-48
AFAP1L2	10q25.3	0.675	5.28E-51	3.51E-47
COL4A2	13q34	0.672	3.10E-50	1.54E-46
SPARC	5q33.1	0.661	3.54E-48	1.41E-44
COL4A1	13q34	0.659	9.83E-48	2.98E-44
NID2	14q22.1	0.658	1.05E-47	2.98E-44
ADGRA2	8p11.23	0.651	2.65E-46	6.61E-43
HEPH	Xq12	0.645	2.92E-45	6.47E-42
ADAMTS12	5p13.3-p13.2	0.637	6.91E-44	1.38E-40
F2R	5q13.3	0.636	1.04E-43	1.82E-40
TGFB1I1	16p11.2	0.636	1.10E-43	1.82E-40
ZFPM2	8q23	0.635	1.44E-43	2.09E-40
TCF4	18q21.2	0.635	1.47E-43	2.09E-40
COL6A3	2q37.3	0.635	1.77E-43	2.35E-40
CALHM5	6q22.1	0.635	1.99E-43	2.48E-40
FBN1	15q21.1	0.634	2.14E-43	2.52E-40
GLI3	7p14.1	0.633	3.70E-43	4.10E-40
TSHZ3	19q12	0.630	1.11E-42	1.12E-39
CD248	11q13.2	0.630	1.12E-42	1.12E-39

Table 2

Gene ontology enrichment analysis of genes related to PAMR1 in HCC.
Category	Term	Count	FDR
BP	GO:0030198 ~ extracellular matrix organization	19	2.13E-13
BP	GO:0030199 ~ collagen fibril organization	13	3.81E-11
BP	GO:0007155 ~ cell adhesion	13	0.002072
BP	GO:0007507 ~ heart development	10	1.89E-04
BP	GO:0030324 ~ lung development	8	6.82E-05
BP	GO:0001568 ~ blood vessel development	5	0.009887
CC	GO:0005886 ~ plasma membrane	38	0.034479
CC	GO:0005576 ~ extracellular region	30	3.20E-06
CC	GO:0005615 ~ extracellular space	24	5.99E-04
CC	GO:0031012 ~ extracellular matrix	16	1.91E-10
CC	GO:0005788 ~ endoplasmic reticulum lumen	13	1.33E-06
CC	GO:0005581 ~ collagen trimer	9	1.19E-06
CC	GO:0005604 ~ basement membrane	7	1.84E-04
MF	GO:0005509 ~ calcium ion binding	13	0.007163
MF	GO:0005201 ~ extracellular matrix structural constituent	12	1.33E-08
MF	GO:0030020 ~ extracellular matrix structural constituent conferring tensile strength	8	9.98E-08
MF	GO:0005516 ~ calmodulin binding	8	0.002623
MF	GO:0008201 ~ heparin binding	7	0.006459
MF	GO:0005518 ~ collagen binding	6	9.32E-04
MF	GO:0048407 ~ platelet-derived growth factor binding	4	9.32E-04

Correlation analysis between PAMR1 expression and infiltrating immune cells. It was reported that tumor-infiltrating leukcyte (TIL) was closely associated with patient survival and prognosis in types of cancers. Here, we used the TIMER database to determine whether PAMR1 expression was correlated with tumor purity and TIL in HCC. As shown in Fig. 5, the PAMR1 expression level showed negative correlation with tumor purity (r =-0.322, P = 8.25e-10) while positive correlation with B cells (r = 0.193, P = 3.20e-04), CD8 + T cells (r = 0.312, P = 3.67e-09), CD4 + T cells (r = 0.345, P = 4.70e-11), macrophages (r = 0.388, P = 1.06e-13), neutrophils (r = 0.379, P = 3.11e-13), and dendritic cells (r = 0.371, P = 1.66e-12) .

GSEA identified PAMR1-related signaling pathways. We next performed GSEA to compare the enriched signaling pathways between PAMR1-high and PAMR1-low cases in datasets from TCGA-LIHC. The PAMR1 suppressed signaling pathways were gated with the criteria of NES > 2 and nominal P < 0.05. The six most significant enriched pathways associated with low PAMR1 level were inflammatory response, hypoxia, epithelial-mesenchymal transition, KRAS signaling, TNF-a signaling via NF-κB, and myogenesis ( (Fig. 6 and Table 3)).

Table 3

The results of gene set enrichment analysis.
MSigDB collection	Gene set name	NES	NOM p-val	FDR q-val
h.all.v7.5.1.symbols.gmt	HALLMARK_INFLAMMATORY_RESPONSE	-2.41	0.001	0.001
	HALLMARK_HYPOXIA	-2.23	0.001	0.001
	HALLMARK_EPITHELIAL_MESENCHYMAL _TRANSITION	-2.13	0.001	0.017
	HALLMARK_KRAS_SIGNALING_UP	-2.13	0.001	0.014
	HALLMARK_TNFA_SIGNALING_VIA_NFKB	-2.06	0.001	0.008
	HALLMARK_MYOGENESIS	-2.03	0.001	0.007

The biomarkers for diagnosis and prognosis prediction in cancers were crucial for precision medicine¹³. It has been reported that targeted drugs showed a 30% higher response rate than chemotherapy in clinical trials¹⁴. Moreover, several novel targeted drugs have superior secondary efficacy for liver-cancer treatment, broadening prospects for HCC patients¹⁵, which make the identification of prognostic biomarkers crucial for precision medicine.

The gene PAMR1 was reported to show lower protein level in breast and cervical cancer tissues compared with that in normal tissues ^11,12. Herein, we screened DEGs between the HCC and adjacent normal tissues in GEO datasets, and identified the lower PAMR1 level in HCC, followed by validation of experimental IHC and qRT-PCR on the mRNA and protein level, respectively.Meanwhile, the ROC curve showed that AUC,sensitivity and specificity were 0.918, 94.0% and 84.5%, indicating that PAMR1 has good diagnostic efficacy. Additionally, Kaplan-Meier analysis indicated that patients with higher PAMR1 level showed higher survival rate than patients with lower PAMR1 level. Taken together, our results provide convincing evidence that PAMR1 is an appropriate biomarker for clinical prognosis in patients with HCC.

We further investigated the underlying mechanism of PAMR1 on HCC pathogenesis. CBioportal was used to screen for PAMR1 associated genes, such as SFRP1, PXDN and AFAP1L2. The results of GO analysis then indicated that PAMR1-related genes were mainly enriched in the regulation of extracellular matrix organization, cell adhesion, and blood vessel development. These functions are closely associated with cancer; loss of cellular adhesion is a crucial step in tumor metastasis, whereas blood vessel development contributes to both tumor growth and spread^16,17.

Compellingly, our findings reveal that PAMR1 expression is positively correlated with infiltration of immune cells, including B cells, CD8 + T cells, CD4 + T cells, macrophages, dendritic cells, and neutrophils in HCC. In line with its enrichment in non-tumor cells, PAMR1 expression was also negatively correlated with tumor purity. It was well acknowledged that B cells and macrophages are important antigen-presenting cells in various cancers, while CD8 + T cells are immune effectors^18,19. Therefore, PAMR1 may be important for immune responses in HCC.

Supporting the role of PAMR1 in cancer immune response, GSEA of high- and low-PAMR1-expressing HCC patients identified six signaling pathways: inflammatory response, hypoxia, epithelial-mesenchymal transition, KRAS signaling, TNF-a signaling via NF-κB, and myogenesis. Epithelial-mesenchymal transition could not only promote multiple tumor metastasis^20,21, but also played an important role in tumor drug resistance²², immunosuppression²³, and stem cells during cancer progression²⁴. KRAS is the most common oncogene in human cancer^25,26. Both of TNF-α and NF-κB signaling pathways are involved in cancer proliferation, migration, and apoptosis^27,28. The involvement of these pathways may clarify the pathophysiological mechanisms underlying HCC pathogenesis.

Although the meaningful discovery has been uncovered, this study has several limitations. First, experiments from public databases were conducted in different laboratories, making it unavaible to control the variation in interventions or data availability. Second, we did not fully verify conclusions of the pathogenic mechanism based on database analysis with empirical experiments. Our future goals include performing cell-level and animal-level validations in the laboratory to further investigate the underlying mechanism of PAMR1 on HCC pathogenesis.

Data collection. Genes were screened base on GSE45267(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45267), GSE101685(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE101685), GSE121248(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE121248), and GSE14520(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520) from GEO (http://www.ncbi.nlm.nih.gov/geo) database. Probes were converted into gene symbols based on annotations of the corresponding platform. TCGA (https://portal.gdc.cancer.gov/) containing 50 normal and 371 HCC tissues was also analysed. PAMR1 expression in liver cancer was evaluated with UALCAN (http://ualcan.path.uab.edu/) and GEPIA (http://gepia.cancer-pku.cn/) databases^29,30. Correlations between PAMR1-related genes from TCGA were analyzed using cBioPortal (http://www.cbioportal)³¹. Gene Ontology (GO) enrichment analysis of these genes was performed with DAVID (https://david.ncifcrf.gov/home.jsp)³².

Identification of differentially expressed genes. GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) is a web tool for a quick identification of differentially expressed genes (DEGs) in GEO datasets. Here, it was used to identify DEGs between HCC and healthy liver samples. A log2fold change (logFC) > 1 and adjusted P < 0.05 were cut-offs for DEGs. The top 100 DEGs from each dataset (based on adjusted p-value) were selected, and overlapped by the Venn online tool (http://bioinformatics.psb.ugent.be/webtools/Venn/) for further analysis.

Cell Culture. The human normal liver cell line QSG7701 was purchased from Beyotime Biotechnology. The human HCC cell lines Hep3B, Huh7 were purchased from Procell Life Science&Technology company. Other HCC cell lines SMMC7721、HepG2 were donated by Ming Zhao's laboratory. Cell lines were cultured in DMEM with 10% fetal bovine serum at 37°C in a 5% CO₂ air atmosphere. All media and supplements were purchased from Invitrogen.

Total Rna Extraction And Quantitative Real-time Pcr

Total RNA was extracted from five cell lines (SMMC7721, HepG2, Hep3B, Huh7, QSG7701) using a total RNA extraction kit (Solarbio, Beijing, China), according to the manufacturer’s instructions. RNA was reverse-transcribed into cDNA using the PrimeScript™ RT Reagent kit. Quantitative reverse transcription PCR (qRT-PCR) was performed using SYBR Green (Bio-Rad, Hercules, CA, USA).The cDNA chip was purchased from Outdo Biotech Co., Ltd (Shanghai, China), and amplificatied in the same method. Primers for PAMR1 and GAPDH were as follows:

PAMR1 forward primer: 5′-CTTCCGATGCAGGTTCAGT-3′,

PAMR1 reverse primer: 5′-GCTGGCTTCTTGGTAGGG-3′;

GAPDH forward primer: 5′-ACAGTCAGCCGCATCTTC-3′,

GAPDH reverse primer: 5′-CTCCGACCTTCACCTTCC-3′.

Immunohistochemistry(IHC). Liver cancer tissue arrays (HLivH060CS01) were purchased from Outdo Biotech Co., Ltd(Shanghai, China). After dewaxina, antigens were retrieved under high pressure followed by rinse and endogenous peroxidases block with 3% hydrogen peroxide for 10 min. Then, tissues were incubated in normal goat serum for 15 min and anti-PAMR1 rabbit polyclonal antibody (1:50, Wuhan, China) was incubated overnight at 4°C. The next day, tissue arrays were washed and incubated with secondary antibody at 37°C for 30 min. Then, DAB was incubated for 5 min, and the tissues were coverslipped for observation under a microscope. Staining intensity was scored on the following scale: 0 (none), 1 (weak), 2 (moderate), and 3 (strong). The extent (0–100%) of reactivity was scored with five levels: 0 (< 5%), 1 (5–25%), 2 (25–50%), 3 (50–75%), and 4 (> 75%). Two pathologists independently scored the staining of each slide.

Tumor immune estimation resource analysis. The Tumor Immune Estimation Resource (TIMER) (http://cistrome.org/TIMER/)³³ was used to evaluate the correlation between PAMR1 expression level and the abundance of infiltrating immune cells (CD8 + T cells, CD4 + T cells, B cells, dendritic cells, macrophages, and neutrophils), as well as tumor purity in HCC.

Gene set enrichment analysis (GSEA). GSEA version 4.2.1 (http://www.gsea-msigdb.org/gsea/downloads.jsp) is a computational method based on the entire gene expression matrix³⁴. The cases in TCGA-LIHC cohort was separated into PAMR1-low and PAMR1-high subgroups with median PAMR1 level as the cut-off point. Reference gene sets were H.all.v7. 2.symbols.gmt gene sets (Hallmarks) from MSigDB³⁵. Each analysis required at least 100 times permutation tests. An adjusted P < 0.05 and normalized enrichment score (NES) > 2 were considered as significant enrichment.

Statistical analysis. Survival curves were calculated with the Kaplan-Meier method with log-rank tests. Data from all experiments were analyzed with unpaired t-tests or one-way ANOVA in GraphPad Prism 8 (GraphPad, San Diego, CA, USA). The receiver operating characteristic (ROC) curve analysis was performed by pROC packages in R sofware, version 3.6.3. P < 0.05 was defined as significance.

In conclusion, this is the first study to demonstrate that PAMR1 level is lower in HCC compared with that in normal liver tissues, and is closely associated with the shape of hepatic immune microenvironment, and has high diagnostic efficiency, and predicts poor prognostic outcome, which makes it as a favorable diagnostic and prognostic factor for HCC.

Data Availability

The datasets generated and analyzed during the current study are available from public databases, TCGA (https://portal.gdc.cancer.gov/), GEO (https://www.ncbi.nlm.nih.gov/geo/), UALCAN (http://ualcan.path.uab.edu/), GEPIA (http://gepia.cancer-pku.cn/), cBioPortal(http://www.cbioportal), and DAVID (https://david.ncifcrf.gov/home.jsp)databases.

Acknowledgments

This work was supported by the Foundation of Health Commission of Chengdu (No.2022235), the Foundation of the First Affiliated Hospital of Chengdu Medical College (No.CYFY2020YB05).

Authors Contributions

Y.X. and X.P.Z. conducted the study design. X.P.Z. wrote original draft. Y.X. wrote, reviewed, and edited. X.P.Z. and T.L carried out experiments and data analysis. S.H.D.and T.Z. provided technical support and material. D.M.W.supervised. All the authors read and approved the final manuscript.

Competing interests

The authors declare no competing interests.

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No competing interests reported.

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PAMR1 is a favorable diagnostic and prognostic biomarker in hepatocellular carcinoma

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