Identification of Molecular Biomarkers and Pathways for Risk Stratification in Human Papilloma Virus-Associated Cancers

doi:10.21203/rs.3.rs-641999/v1

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Identification of Molecular Biomarkers and Pathways for Risk Stratification in Human Papilloma Virus-Associated Cancers

https://doi.org/10.21203/rs.3.rs-641999/v1

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Background: Human papillomavirus (HPV) is the major cause of cervical cancer (CC) etiology; its contribution to head and neck cancer (HNC) incidence is steadily increasing. As individual patients’ response to the treatment of HPV-associated cancer is variable, there is a pressing need for the identification of biomarkers for risk stratification that can help determine the intensity of treatment.

Methods: We have previously reported a novel prognostic and predictive indicator (HPPI) scoring system in HPV-associated cancers regardless of the anatomical locations by analyzing the TCGA and GEO databases. In this study, we comprehensively investigated the association of group-specific expression patterns of common differentially expressed genes (DEGs) between high-risk and low-risk groups in HPV-associated CC and HNC, identifying a molecular biomarkers and pathways for the risk stratification.

Results: Among the identified 174 DEGs, expression of the genes associated with extracellular matrix (ECM)-receptor interaction pathway (ITGA5, ITGB1, LAMB1, LAMC1) were increased in high-risk groups in both HPV-associated CC and HNC while expression of the genes associated with the T-cell immunity (CD3D, CD3E, CD8B, LCK, and ZAP70) were decreased vise versa. The individual genes showed statistically significant prognostic impact on HPV-associated cancers but not on HPV-negative cancers. The expression levels of identified genes were similar between HPV-negative and HPV-associated high-risk groups with distinct expression patterns only in HPV-associated low-risk groups. Each group of genes showed negative correlations, and distinct patterns of immune cell infiltration in tumor microenvironments.

Conclusion: These results identify molecular biomarkers and pathways for risk stratification in HPV-associated cancers regardless of anatomical locations. The identified targets are selectively working in only HPV-associated cancers, but not in HPV-negative cancers indicating possibility of the selective targets governing HPV-infective tumor microenvironments.

General Biochemistry

Molecular Biology

Human papillomavirus

Risk stratification

Head and neck cancer

Cervical cancer

Prognostic biomarker.

Human papillomavirus (HPV) is a small circular virus with approximately 8 kb of double-stranded DNA genome comprising the following three major functional regions: (i) an upstream regulatory region (URR) with transcription factor-binding sites controlling gene expression; (ii) an early region encoding six genes (E1, E2, E4, E5, E6, E7) having multiple functions, including viral replication, and (iii) a late region encoding capsid proteins L1 and L2 that produce the virion by self-assembly (1, 2). HPV induces a productive infection by targeting the epithelial cells in the basal layer of skin and mucosa (1). More than 150 HPV genotypes have been discovered to date, and the majority show subclinical manifestation or cause the growth of benign lesions ranging in severity from self-limiting to debilitating (3). However, the chronic infection of some oncogenic high-risk types of HPV, especially types 16 and 18, results in cancer progression (4, 5). In more than 95% of invasive cervical cancers (CC) and most cases of HPV-associated head and neck cancers (HNC), the incorporation of high-risk HPV genomes into the host genome was detected (6). Among HNCs, the rate of HPV association in oropharyngeal squamous cell carcinoma (OPSCC) has increased and is reported to have reached 80% in recent years (2). HPV-associated OPSCC is considered a unique disease entity as it has clinical and molecular characteristics distinct from non-HPV-related OPSCC and has good overall prognosis with better response to treatment, including radiation (7, 8). Persistent infection of HPV also acts as an important etiology of other anogenital tract malignancies such as anal, vaginal, and penile, making it a notable public health issue (9). Therefore, more advanced treatment strategies that consider the additional prognostic factors for each patient require urgent development.

In HPV-related cancers, high-risk type HPV continuously expresses representative oncoproteins E6 and E7 that primarily induce cellular transformation through the decomposition of p53 and pRb respectively (10, 11). The accumulation of cellular mutations by which E6 and E7 promote cell-cycle progression, chromosomal instability, and integration of foreign DNA, which ultimately contribute to carcinogenesis have also been elucidated (1, 10, 12). However, the precise molecular mechanisms involved in oncogenesis and clinical expression, or the interaction of diverse genomes at each stage, are still not clearly understood. These processes are also dynamically influenced by additional factors, such as the location of tumor occurrence including the peritumoral microenvironment and the intrinsic factors of each patient, which intensifies gene expression heterogeneity and makes it difficult to accurately identify disease progression patterns and prognostic stratification (13, 14). In recent years, cancer bioinformatics has been actively developing as a new strategy that combines molecular biology and in silico information engineering based on gene sequencing data to discover clinically meaningful biomarkers related to treatment response and prognosis in each tumor and apply it to clinical practice (15, 16). More appropriate personalized treatment will become possible as our knowledge of the gene expression profiles and molecular behavior heterogeneity in HPV-related cancers improves through such analysis.

Previously, we analyzed common HPV-specific prognostic gene signatures from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, according to HPV-related cancers in different anatomic sites of the head and neck, and cervix (17). Using these results, we developed a HPV-related prognostic and predictive indicator (HPPI) scoring system that can perform risk stratification using the 34 selected genes, and demonstrated that there was a significant difference in the prognosis. In the present study, we investigated differentially expressed genes (DEGs) in the high and low-risk groups of HPV-related HNC and CC according to HPPI scoring, and aimed to identify genes that influence clinical manifestations, including survival and their expression profiles. The study workflow is illustrated in Fig. 1.

Patient data collection and risk stratification

Clinical data and RNA-seq gene expression profiles of TCGA HPV-related HNC and CC were downloaded from Broad GDAC Firehose (http://gdac.broadinstitute.org/). Using the HPPI risk scoring system, we divided patients into high-risk (HNC = 27, CC = 43) and low-risk groups (HNC = 70, CC = 226) (17). Patient characteristics are shown in Table 1.

Table 1

Patient characteristics
		TCGA-HNC (N = 518)
		HPV (+)	HPV (-)	HPV (+)	HPV (-)
		97	421	269	21
Age	< 55	37	112	196	11
Age	\(\ge\) 55	60	309	73	10
Clinical Stage	I	3	24	148	11
	II	11	62	57	6
	III	10	71	40	1
	IV	42	223	18	3
Clinical N stage	N0	30	213	-	-
	N1	10	72	-	-
	N2	52	110	-	-
	N3	3	6	-	-
Clinical M stage	M0	91	388	-	-
Clinical M stage	M1	1	5	-	-
Gender	Female	11	124	-	-
Gender	Male	86	297	-	-

Identification of common DEGs

We identified DEGs between the high-risk and low-risk groups for each cohort using “siggenes 1.60.0” in the R package using the Significance Analysis of Microarrays (SAM) method. We then identified the common DEGs that exist in both cohorts. We used the cutoff value for False Discovery Rate (FDR) = 0.01 and set 1.7 fold change (FC) values to define upregulated and downregulated genes. We used the “gplots 3.1.1”, and “ggplot2 3.3.3” R packages to visualize the results.

KEGG pathway enrichment analysis and selection of target genes

Pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to confirm the biological function of common DEGs using NetworkAnalyst 3.0 (https://www.networkanalyst.ca/). We selected genes that clustered at least three times in the top four enrichment pathways with the most significant P-values.

Analysis of tumor-infiltrating immune cells

To investigate the correlation of the expression of selected genes expression with the levels of tumor-infiltrating immune cells, we used the Tumor Immune Estimation Resource (TIMER) version 2.0 (http://timer.cistrome.org/). The TIMER database provides several analyses of immune infiltration of tumors from the TCGA database. Of the 32 TCGA tumor data included in the TIMER database, HPV (+) HNC and HPV (+) CC were used for analysis. Although we analyzed six immune cell types (CD8 + T cells, CD4 + T cells, B cells, macrophages, neutrophils, dendritic cells), only significant results were included in the results.

Statistical analysis

After setting the cutoff as the median gene expression value for each selected gene, we performed the Kaplan-Meier (KM) analysis for computing survival, log rank test, and univariate cox regression analysis to predict overall survival (OS) and estimate the accuracy using the R packages: “survival 3.1–12” and “survminer 0.4.9”. The correlation between the selected target genes was evaluated using Spearman's correlation analysis. The threshold for significant correlation was set at P < 0.05 and Spearman's correlation coefficient was > 0.3. All statistical analyses were performed using R (version 4.0.2).

Common DEGs between high and low-risk groups in HNC and CC

According to a previous study (17), we divided HPV (+) HNC and HPV (+) CC into high-risk and low-risk groups, respectively and identified DEGs in each, as well as DEGs that were common in both cohorts and defined them as common DEGs. We identified 174 common DEGs, of which 30 were upregulated and 144 were downregulated (Supplementary Table 1). KEGG pathway enrichment analysis was performed to select target genes that explain the molecular mechanisms among common DEGs, and four upregulated genes (ITGA5, ITGB1, LAMB1, and LAMC1), and five downregulated genes (CD3D, CD3E, CD8B, LCK, and ZAP70) were selected (Fig. 2A-D, Table 2–3).

Table 2

Results of KEGG pathway enrichment analysis
	Pathway	P-value	FDR	Genes
Upregulated genes	ECM-receptor interaction	1.94E-05	0.00379	ITGA5, ITGB1, LAMB1, LAMC1
	Small cell lung cancer	3.19E-05	0.00379	ITGB1, LAMB1, LAMC1, CDK6
	Focal adhesion	3.71E-05	0.00379	ITGA5, ITGB1, LAMB1, LAMC1, SHC1
	PI3K-Akt signaling pathway	4.77E-05	0.00379	ITGA5, ITGB1, LAMB1, LAMC1, CDK6, OSMR
Downregulated genes	Primary immunodeficiency	1.25E-06	0.000398	CD3D, CD3E, CD8B, LCK, ZAP70
	Th1 and Th2 cell differentiation	0.000114	0.0141	CD3D, CD3E, LCK, ZAP70, IL12A
	Hematopoietic cell lineage	0.000146	0.0141	CD3D, CD3E, CD8B, CD2, FLT3LG
	T-cell-receptor signaling pathway	0.000177	0.0141	CD3D, CD3E, CD8B, LCK, ZAP70

Table 3

Pathway enrichment analysis–Gene list
	Gene symbol	Full name
Upregulated genes	ITGA5	Integrin Subunit Alpha 5
	ITGB1	Integrin Subunit Beta 1
	LAMB1	Laminin Subunit Beta 1
	LAMC1	Laminin Subunit Gamma 1
	CD3D	CD3d Molecule
	CD3E	CD3e Molecule
Downregulated genes	CD8B	CD8b Molecule
	LCK	LCK Proto-Oncogene, Src Family Tyrosine Kinase
	ZAP70	Zeta Chain Of T Cell Receptor Associated Protein Kinase 70

Prognostic significance of common DEGs in HNC and CC

To confirm the association between the OS and expression levels of target genes, we performed KM analysis and log rank test on the HPV (+) HNC and HPV (+) CC cohorts (Fig. 3A-B). High levels of gene expression of ITGA5, ITGB1, LAMB1, and LAMC1 were associated with a worse OS, while low levels of gene expression of CD3D, CD3E, CD8B, LCK, and ZAP70 were associated with a worse OS of patients with HPV-related cancers (Fig. 3A-B). Univariate cox regression analysis revealed nine target genes as independent prognostic factors for HPV-related cancers (Table 4). However, the nine target genes had no prognostic effects on HPV (-) cancers (Supplementary Table 2).

Table 4

Univariate cox regression in HPV (+) cancers
Dataset	Genes		P value	Correlation coefficient	HR	95 CI
HPV (+) HNC	Upregulated	ITGA5	0.000465 ***	1.5377	4.6537	1.967	11.01
		ITGB1	0.0272 *	0.8657	2.3767	1.103	5.123
		LAMB1	0.0653	0.6954	2.0044	0.9569	4.199
		LAMC1	0.0103 *	1.0245	2.7857	1.273	6.095
	Downregulated	CD3D	0.000453 ***	-1.533	0.216	0.09171	0.5086
		CD3E	0.00118 **	-1.3572	0.2574	0.1134	0.5844
		CD8B	0.000417 ***	-1.5371	0.2150	0.09156	0.5049
		LCK	0.000638 ***	-1.4263	0.2402	0.1059	0.5446
		ZAP70	0.00161 **	-1.3149	0.2685	0.1186	0.6078
HPV (+) CC	Upregulated	ITGA5	0.000219 ***	0.9798	2.6639	1.584	4.479
		ITGB1	0.00424 **	0.7334	2.0822	1.259	3.442
		LAMB1	0.12	0.3910	1.4785	0.9033	2.42
		LAMC1	0.00537 **	0.7220	2.0585	1.238	3.422
	Downregulated	CD3D	0.0365 *	-0.5273	0.5902	0.3601	0.9674
		CD3E	0.0755	-0.4455	0.6405	0.3919	1.047
		CD8B	0.00906 **	-0.6748	0.5092	0.3068	0.8453
		LCK	0.0132 *	-0.6225	0.5366	0.328	0.878
		ZAP70	0.00534 **	-0.7096	0.4919	0.2986	0.8103

Expression patterns of common DEGs in HNC and CC

To determine the expression patterns of the target genes of normal, HPV (-), and risk-stratified HPV (+) cancers, we compared the relative expression levels of the target genes between the following four groups: normal, HPV (-), HPV (+) low-risk group, and HPV (+) high-risk group (Fig. 4). Interestingly, the expression patterns of the target genes were similar in the HPV (-) and HPV (+) high-risk groups in both cancer cohorts. We found that four upregulated genes (ITGA5, ITGB1, LAMB1, and LAMC1) among the nine target genes showed high expression in HPV (-) and HPV (+) high-risk groups, but relatively low expression in HPV (+) low-risk and normal groups. In contrast, the remaining five downregulated genes (CD3D, CD3E, CD8B, LCK, and ZAP70) showed low expression in HPV (-) and HPV (+) high-risk groups, but relatively high expression in HPV (+) low-risk group (Fig. 4A-B).

Correlation analysis of common DEGs in HNC and CC

In order to determine if there were any correlations between the target genes, we conducted a Spearman's correlation analysis. We found a high correlation between genes with the same expression patterns, but no high correlation between genes with different expression patterns (Fig. 5A-B). All the downregulated genes had a high correlation and among the upregulated genes, ITGA5 and ITGB1, ITGB1 and LAMC1 had high correlations (|coefficient| > 0.6) (Fig. 5A-B).

Correlation analysis between common DEGs and immune cell infiltration in HPV (+) HNC

As the pattern of immune infiltrate is an important aspect of tumor survival and progression, we explored the correlation between the expression levels of the target genes and immune cell infiltration using data for several immune cell subtypes and the Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma (TCGA-HNSC) HPV (+) from TIMER 2.0 (Fig. 6A-I). The expression levels of the downregulated target genes were positively correlated with CD4 + T cells and neutrophils (Fig. 6E-I), and negatively correlated with macrophages, while the upregulated target genes were positively correlated with macrophages (Fig. 6A-D).

Investigation for genomic and molecular biomarkers which possess prognostic importance, including for the prognosis of HPV-related cancer, is still underway. The need for the identification of specific prognostic factors has emerged in HPV-related OPSCC, which includes a subgroup of patients with aggressive behavior, although the majority show favorable treatment responses (18). In this study, risk stratification of HPV-related HNC and CC was performed using the HPPI scoring system developed in our previous study (17). Of the identified DEGs, the expression of the four upregulated and five downregulated target genes was compared with the normal and HPV (-) cohort. A particularly noteworthy finding was that both HPV (-) and HPV (+) high-risk groups in HNC showed similar patterns of expression of the target genes, and different expression patterns only appeared in the HPV (+) low-risk group (Fig. 4). In the case of de-escalating treatment trials in HPV-related OPSCC, as part of an effort to reduce the toxicity caused by chemoradiation and to perform less invasive surgery for improving the quality of life (18, 19), it is important to accurately select the low-risk group by risk stratification of HPV (+) HNC. Our study can be instrumental in these instances as it can provide genomic data for the development of patient-specific treatment, which are important in both cancer biology and other clinical applications.

The extracellular matrix (ECM) is an essential milieu of tissues, consisting of a dynamic network of biochemical components (20, 21). ECM contributes to the interaction of cells with their microenvironments, which plays a crucial role in regulating cellular behavior and maintaining organ homeostasis. It is known that the disruption of these control processes can cause diseases such as cancer, thus, the expression of ECM-related genes has been studied (21, 22). It has been recently reported that the expression of ECM genes differs according to HPV association in OPSCC and with high-grade dysplasia and invasive cancer (23). In this current study, we focused on laminin genes LAMB1 and LAMC1, and their receptors, integrin genes ITGA5 and ITGB1 among 30 upregulated DEGs that participate in ECM-receptor interaction in the KEGG pathway enrichment analysis (Table 2) (22). Laminin is a major component of the basement membrane and is essential for the adhesive interaction between the cell and the basement membrane; laminins bind laminin-binding integrin heterodimers (24). LAMB combines with LAMC and together they initiate cell assembly to promote cancer invasion and gene expression in various invasive cancers, including LAMB1 gene expression in head and neck, liver, and uterine endometrial cancers, and LAMC1 gene expression in gastric cancer (25–27). Integrin subunits α5 and β1 primarily conjoin to form α5β1 heterodimers, and ITGA5 and ITGB1, the genes encoding α5 and β1, respectively have been suggested to be associated with tumor aggressive behavior, such as cancer progression and treatment resistance (28, 29). In our study, an increase in ITGA5 and ITGB1 expression was characteristically identified in high-risk HPV (+) and HPV (-) groups in HNC, and was associated with the worsening of overall survival in both HPV-related HNC and CC. Therefore, we postulate that LAMB1, LAMC1, ITGA5, and ITGB1 genes may be potential biomarkers for high risk and poor prognosis in HNC and CC, especially in HPV-related cancers.

Other components of the tumor microenvironment, including the expression of genetic markers, and distribution of immune cells and molecules that regulate cellular function, have been studied for targeted immunotherapy (30, 31). Of 144 downregulated DEGs in our study, five were CD3D and CD3E as T cell markers, CD8 + T cell-specific marker CD8B, LCK, and ZAP70, which participates in primary immunodeficiency and the T cell-receptor signaling pathway in the KEGG pathway analysis (Table 2) (30). LCK and ZAP70 are protein tyrosine kinases, and ZAP70 activated by LCK plays an important role in initiating intracellular signaling pathways related to T cell antigenic receptor in the early stages of T cell activation (32, 33). A meta-analysis of several solid tumors revealed that greater infiltration of CD3 + and CD8 + T cells had a positive effect on survival, and a recent report on HNC revealed that more tumor-infiltrating immune cells were associated with better prognosis (31, 34). An increase in T cell infiltrates, which emphasizes the role of immune system, was suggested as an important prognostic determinant contributing to tumor suppression, especially in HPV-related OPSCC (35). In our study, contrary to ECM genes, the expression of T cell-related genes was significantly higher in the low-risk HPV (+) group and lower in the high-risk HPV (+) and HPV (-) groups in HNC, and higher expression improved overall survival in HPV-related HNC and CC. Therefore, in addition to ECM genes, T cell-related genes CD3D, CD3E, CD8B, LCK, and ZAP70 could be useful biomarkers for risk stratification of HPV-related cancer.

We also performed correlation analyses between the selected target genes in the HPV-related HNC and CC groups. A positive correlation was observed between DEGs with the same expression pattern, while a negative correlation was observed between upregulated ECM genes and downregulated T cell-related genes. It has been proposed that cellular components, including immune cells in the tumor microenvironment, contribute to the regulation of ECM dynamics (21). A study on HNC that investigated the tumor ecosystem with a single-cell transcriptome proved that heterogeneous cellular components, including T cells, influence the expression of ECM markers related to tumor migration, such as laminin, by active interaction (36). This study also suggests the possibility that different ECM pathways and immunity within the tumor microenvironment are complexly intertwined with cancer biology, and require further study.

In summary, this study contributes to our understanding of the tumor biology of virus-related cancers, and suggests biomarkers for risk stratification in the HPV (+) group by conducting an integrated bioinformatics analysis on the association between DEGs and prognosis of HPV infection in CC and HNC. However, we only analyzed the upregulation and downregulation of genes and their association in selected pathways using the cohort in the TCGA database, and the lack of experimental verification of the expression patterns of the target genes in cancer tissues requires future investigation.

HPV

Human papillomavirus; CC:Cervical cancer; HNC:Head and neck cancer;

OPSCC

Oropharyngeal squamous cell carcinoma; DEGs:Differentially expressed genes;

HPPI

HPV-related prognostic and predictive indicator; TCGA:The Cancer Genome Atlas

GEO

Gene Expression Omnibus; ECM:Extracellular matrix; URR:upstream regulatory region

SAM

Significance Analysis of Microarrays; FDR:False Discovery Rate; FC:fold change

KEGG

Kyoto Encyclopedia of Genes and Genomes; TIMER:Tumor Immune Estimation Resource; KM:Kaplan-Meier; OS:Overall survival

Ethics approval and consent to participant

There is no need for ethical approval as all data in this study were downloaded from public databases (TCGA).

Consent for publication

Not applicable.

Availability of supporting data

Available upon request.

Competing interests

The authors declare that no competing interest exist.

Funding

This work was supported by (2020R1C1C1011647, NRF-2020R1C1C1003741, NRF-2019R1F1A1061323, NRF-2018R1A5A2023879).

Authors’ Contributions

YHK and JYJ initiated the study and guided the work. EJK, HRL, JHL, and MH, with support from all coauthors, analyzed and interpreted the experimental data. YHK, JYJ, EJK, HRL, and MH wrote the manuscript with input from all coauthors, and all authors read and approved the final manuscript.

Acknowledgements

Not applicable.

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SupplementaryTable1.Commonupordowngeneslist.docx
Table S1. Common up or down gene list.
SupplementaryTable2.Univariatecoxregressionhpv.docx
Table S2. Univariate cox regression analysis in HPV (-) cancers.
SupplementaryFigure1..pdf
Figure S1. Survival analysis based on target genes expression in HPV (-) cancers. (A) HPV (-) HNC, and (B) HPV (-) CC. P: log rank test P value.

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Identification of Molecular Biomarkers and Pathways for Risk Stratification in Human Papilloma Virus-Associated Cancers

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Abstract

Figures

Background

Methods

Patient data collection and risk stratification

Identification of common DEGs

KEGG pathway enrichment analysis and selection of target genes

Analysis of tumor-infiltrating immune cells

Statistical analysis

Results

Common DEGs between high and low-risk groups in HNC and CC

Prognostic significance of common DEGs in HNC and CC

Expression patterns of common DEGs in HNC and CC

Correlation analysis of common DEGs in HNC and CC

Correlation analysis between common DEGs and immune cell infiltration in HPV (+) HNC

Discussion

Abbreviations

Declarations

References

Supplementary Files

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