Three-gene prognostic biomarkers for seminoma identified by weighted gene co-expression network analysis

Hualin Chen; Gang Chen; Yang Pan; Xiaoxiang Jin

doi:10.1371/journal.pone.0240943

. 2020 Oct 26;15(10):e0240943. doi: 10.1371/journal.pone.0240943

Three-gene prognostic biomarkers for seminoma identified by weighted gene co-expression network analysis

Hualin Chen ¹, Gang Chen ^1,^*, Yang Pan ¹, Xiaoxiang Jin ¹

Editor: Edwin Wang²

PMCID: PMC7588113 PMID: 33104706

Abstract

Testicular germ cell tumors (TGCTs) are common in young males, and seminoma accounts for a large proportion of TGCTs. However, there are limited records on the exploration of novel biomarkers for seminoma. Hence, we aimed to identify new biomarkers associated with overall survival in seminoma. mRNA-seq and clinical traits of TGCTs were downloaded from UCSC XENA and analyzed by weighted gene co-expression network analysis. After intersection with differentially expressed genes in GSE8607, common genes were subjected to protein-protein interaction (PPI) network construction and enrichment analyses. Then, the top 10 common genes were investigated by Kaplan–Meier (KM) survival analyses and univariate Cox regression analyses. Ultimately, TYROBP, CD68, and ITGAM were considered three prognostic biomarkers in seminoma. Based on correlation analysis between these genes and immune infiltrates, we suggest that the three biomarkers influence the survival of seminoma patients, possibly through regulating the infiltration of immune cells. In conclusion, our study demonstrated that TYROBP, CD68, and ITGAM could be regarded as prognostic biomarkers and therapeutic targets for seminoma patients.

Introduction

Testicular germ cell tumors (TGCTs) are the most common malignancy in males between the ages of 15 and 35 years [1]. According to the GLOBOCAN database, there are approximately 71,000 new cases and 9500 deaths from TGCTs per year, worldwide [2]. Pathological studies reveal that seminoma accounts for over 60% of TGCT cases and this proportion is increasing [3]. Additionally, approximately 80% of seminomas are classified as stage I according to the clinical staging system [4]. Although the primary treatment for seminoma can result in a 5-year survival rate of over 90%, some patients fail cisplatin-based first-line chemotherapy and about 3%–5% of them will eventually die of the disease [5]. Moreover, for patients with solitary testicle or bilateral testicular tumors, novel treatment methods are needed to increase survival rate. Recently, from in-depth studies surrounding tumor immunity, immunotherapy has become a potential therapeutic method for patients with seminoma [5]. Hence, it is essential to identify novel biomarkers and understand the molecular mechanism of tumorigenesis with an attempt to obtain early diagnosis, better clinical application of novel treatment strategies, and prognostic prediction.

In recent years, the rapid development of microarray technologies and high-throughput sequencing technologies has provided promising approaches for screening and identifying novel therapeutic targets and prognostic biomarkers for seminoma. Weighted gene co-expression network analysis (WGCNA), which was primarily developed by Peter Langfelder and Steve Horvath, is an advanced method for exploring the correlations between genes and clinical traits. In WGCNA, the concept of soft threshold has been raised, instead of the hard threshold used in traditional bioinformatics analysis. Therefore, potential key genes with small fold changes, which may be strongly correlated with clinical traits and play important roles in tumorigenesis, may be identified through the network [6, 7].

In the present study, we used WGCNA to identify seminoma-correlated modules and core genes in an attempt to provide novel therapeutic targets and obtain a better understanding of the molecular mechanisms driving seminoma.

Materials and methods

Data acquisition and pre-processing

The workflow of this study is presented in Fig 1. The transcriptome data and clinical information of TGCT were downloaded from the TCGA Hub of the UCSC XENA database (https://tcga.xenahubs.net). Pure samples of seminoma and non-seminoma (embryonal carcinoma, choriocarcinoma, yolk sac tumor and teratoma) were screened for further analysis, while samples without clinical traits and mixed samples were removed. Subsequently, 121 samples including 66 samples of seminoma and 55 samples of non-seminoma were identified. Then, genes were ranked by median absolute deviation (MAD) from high to low, and the top 5000 MAD genes were identified for co-expression network analysis.

Construction of a weighted correlation network and identification of modules associated with seminoma

Primarily, a correlation matrix was constructed using Pearson’s correlation coefficient matrices which were calculated by average linkage method for all pairwise genes. Then, the correlation matrix was transformed into a weighted adjacency matrix using the soft-thresholding function. By utilizing the soft-connectivity algorithm, a co-expression network with a balance between scale-independence and mean connectivity was obtained. Scale-independence >0.85 and average connectivity <100 were used as the criteria for a suitable soft threshold. Subsequently, the adjacency matrix was transformed into a topological overlap matrix (TOM). Dissimilarity (1-TOM) was calculated and considered as the distance measurement to cluster genes with similar expression profiles into gene modules with a minimum size cutoff of 30. A merge height of 0.25 was used as a criterion to cluster similar modules. P‑values and correlation coefficients were calculated to identify the association between a co-expression module and the clinical phenotype.

After that, the blue and green modules were considered as the two hub seminoma-correlated modules. Then, preliminary Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for genes in these two modules to determine the more significant module. Ultimately, the blue module was identified as the candidate seminoma-correlated module since the preliminary KEGG analyses revealed no enriched pathway for genes in the green module and some interesting pathways potentially related to tumor biology for genes in the blue module. Hence, these genes were selected for further analyses. The clusterProfiler package in R was used for KEGG analyses [8]. The workflow of WGCNA is presented in S1 Fig.

Identification of DEGs in GSE8607

We screened the DEGs between seminoma and controls in GSE8607 using the ‘limma’ R package [9]. Adjust P-value < 0.01 and |logFC| ≥2 were set as the cutoff criterion for improved accuracy and significance, as described previously [10]. Heatmap and volcano maps were drawn to present the DEGs.

Identification of common genes, PPI network construction and functional annotation

Common genes in both the blue module and DEGs obtained in GSE8607 were identified using the VennDiagram package in R [11].

The common genes were submitted to The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database for protein‑protein interaction (PPI) network construction. Then, the PPI network was downloaded and visualized with Cytoscape software [12]. Hub genes were identified using the plug-in CytoHubba [13]. With the application of betweenness centrality (BC) algorithm, the top 50 genes were identified for further functional annotation [14].

Gene Ontology (GO) and pathway functional enrichment analysis were performed using the clusterProfiler package in R and REACTOME Pathway databases (https://reactome.org), respectively. Next, the top 10 genes by highest BC were further identified for identification of hub genes.

Identification of prognostic biomarkers

To investigate the clinical significance of the top 10 gene signatures, Kaplan–Meier survival analyses and univariate Cox regression analyses were performed based on survival data and normalized expression profiles of TGCT obtained from the UCSC XENA platform. The Human Protein Atlas was used for validating the immunohistochemistry (IHC) of latent hub genes [15, 16]. The Survival and Survminer packages in R were used to analyze and visualize the survival data and P<0.05 was considered as indicating a statistically significant difference.

After identification of significant genes, their expression values in multiple tumors were identified by consulting the GEPIA, a newly developed interactive web server for analyzing the RNAseq data of tumors and normal samples from TCGA and GTEx projects [17].

Immune infiltrate analysis in seminoma

The Tumor Immune Estimation Resource (TIMER) database, which is a comprehensive resource for the systematic analysis of immune infiltrates across multiple tumors, was employed to analyze the correlation between prognostic biomarkers and the abundance of immune cell infiltrates, including B cells, CD4+ T cells, CD8+ T cells, Neutrophils, Macrophages, and Dendritic cells [18]. Tumor purity was calculated using R package and CHAT, as described in a previous study [19]. Subsequently, correlations between prognostic biomarker expression and gene markers of tumor-infiltrating immune cells were further explored using Spearman’s correlation. Gene markers have been reported in previous studies [20].

Results

Construction of the co-expression network and identification of seminoma-correlated modules

As presented in Fig 2A, a power of 9 was selected as the soft threshold to construct the weighted adjacency matrix. Based on the dissimilarity of the topological overlap matrix, a cluster dendrogram was generated (Fig 2B).

As presented in Fig 2C, seminoma was significantly correlated with the blue module (r² = 0.54, P = 10^‑10) and green module (r² = 0.78, P = 5x10^‑26). Scatterplots of Gene Significance vs. Module Membership in the two modules were plotted (Fig 2D and 2E).

Then, preliminary KEGG enrichment analyses for genes in the two modules were performed. No term was enriched in the 407 genes in the green module, while 738 genes in the blue module were mainly enriched in pathways related to tumor growth, metastasis and immunology, including cytokine-cytokine receptor interaction, cell adhesion molecules, antigen processing, and presentation, chemokine signaling pathway, natural killer cell mediated cytotoxicity, primary immunodeficiency and Th17 cell differentiation. The top 20 enriched pathways are presented in Fig 2F.

Identification of DEGs in GSE8607

Under the cutoff criteria of Adjust P-value < 0.01 and |logFC| ≥2, 1297 DEGs were screened from the GSE8607 dataset. A heatmap and volcano map were plotted to show the DEGs (Fig 3A and 3B).

Fig 3 — (A) Heat map of the DEGs. (B) Volcano plot of the DEGs (cut-off criteria: adjust P-value < 0.01 and |logFC| ≥2).

Identification of common genes, PPI network construction, and enrichment analysis

A total of 155 common genes were identified for further analysis, by application of the VennDiagram package in R (Fig 4A). Subsequently, an interaction network of 155 common genes was constructed and visualized using Cytoscape software. Then, the top 50 genes by highest BC were selected for enrichment analysis (Fig 4B). The results of biological process enrichment analysis revealed that the top 50 genes were mainly enriched in inflammation and immunity (Fig 4C). REACTOME Pathway enrichment analysis showed that the top 50 genes were mainly enriched in the immune system and signal transduction (Fig 4D).

Investigation of the prognostic significance of hub genes

The top 10 genes by highest BC were identified in the PPI network and further analyses were performed on these candidate hub genes. According to Kaplan–Meier survival analyses, over-expression of TYROBP and CD68 were significantly correlated with poor prognosis in seminoma (P < 0.05) (Fig 5A and 5B). The univariate Cox proportional hazards regression analyses showed that CD68 and ITGAM were positively correlated with overall survival in seminoma (Table 1). The protein level of ITGAM was higher in seminoma tissues than in normal tissues (Fig 5C).

Fig 5 — Kaplan–Meier survival analysis for *TYROBP* (A) and *CD68* (B). (C) Immunohistochemistry graph of *ITGAM* according to the Human Protein Atlas database (Reprinted from The Human Protein Atlas under a CC BY license, with permission from Inger Åhlén, original copyright August 28, 2020). Left: protein levels in normal tissues (staining: not detected, intensity: negative, quantity: none). Right: protein levels in seminoma tissues (staining: medium, intensity: moderate, quantity: > 75%). The expression level of *TYROBP* (D), *CD68* (E), and *ITGAM* (F) according to the GEPIA.

Table 1. The results of survival analyses and univariate cox analyses of the top 10 genes in the PPI network.

Gene symbol	Gene title	P value in Survival analysis	HR	P value in Univariate Cox analysis
CD68	CD68 molecule	0.047	3984.767	0.0467
TYROBP	TYRO protein tyrosine kinase binding protein	0.049	207.445	0.0858
ITGAM	integrin subunit alpha M	0.073	1494.224	0.0257
PTPRC	protein tyrosine phosphatase, receptor type C	0.079	169.022	0.1833
IL10RA	interleukin 10 receptor subunit alpha	0.082	3961.419	0.0840
CCR5	C-C motif chemokine receptor 5 (gene/pseudogene)	0.090	603.833	0.1036
SELL	selectin L	0.195	17.488	0.2364
CD4	CD4 molecule	0.195	897.657	0.0826
IFNG	interferon gamma	0.447	1.110	0.8818
CD2	CD2 molecule	0.807	3.727	0.4816

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HR, hazard ratio.

The expression levels of TYROBP, CD68 and ITGAM in various tumors are presented in Fig 5D and 5E. We noticed that the expression values of these three genes were higher in TGCT compared to normal samples.

Correlations of prognostic biomarkers with lymphocyte infiltration levels in seminoma

The enrichment analyses revealed that the top 50 genes were mainly enriched in immune-related pathways. Previous studies have demonstrated independent predictive roles for tumor-infiltrating lymphocyte grade in the survival of cancer and sentinel lymph node status [21]. Therefore, using the TIMER database, we further analyzed the correlations between the expression of the three candidate biomarkers and immune infiltrates in seminoma. TYROBP, CD68, and ITGAM expression had a significant positive correlation with infiltrating levels of B cells, CD4+ T cells, Macrophages, Neutrophils and Dendritic cells, as depicted in Fig 6.

Furthermore, the relationships between the expression of three prognostic biomarkers and immune marker genes for B cells, CD8+ T cells, neutrophils, macrophages, dendritic cells, NK cells, Th1 cells, Treg and monocytes, as reported in a previous study [20], were also explored in the TIMER database. The results demonstrated that most of the immune marker genes were significantly associated with TYROBP, CD68 and ITGAM expression (Table 2).

Table 2. Correlation analysis between three prognostic biomarkers and immune cell type markers in the TIMER database.

Cell type	Gene markers	CD68		ITGAM		TYROBP
Cell type	Gene markers	COR	P	COR	P	COR	P
B cells	FCRL2	0.536	P < 0.01	0.406	P < 0.01	0.606	P < 0.01
	CD19	0.393	P < 0.01	0.262	P < 0.01	0.467	P < 0.01
	MS4A1	0.473	P < 0.01	0.358	P < 0.01	0.544	P < 0.01
CD8+ T cells	CD8A	0.639	P < 0.01	0.470	P < 0.01	0.671	P < 0.01
CD8+ T cells	CD8B	0.621	P < 0.01	0.386	P < 0.01	0.623	P < 0.01
Neutrophils	FCGR3B	0.212	P < 0.01	0.096	0.243	0.162	0.048
	CEACAM3	0.462	P < 0.01	0.468	P < 0.01	0.568	P < 0.01
	SIGLEC5	0.794	P < 0.01	0.637	P < 0.01	0.617	P < 0.01
	FPR1	0.680	P < 0.01	0.559	P < 0.01	0.564	P < 0.01
	CSF3R	0.791	P < 0.01	0.661	P < 0.01	0.840	P < 0.01
	S100A12	0.182	0.026	0.136	0.098	0.212	P < 0.01
Macrophages	CD68	1.000	P < 0.01	0.741	P < 0.01	0.817	P < 0.01
	CD84	0.834	P < 0.01	0.669	P < 0.01	0.640	P < 0.01
	CD163	0.461	P < 0.01	0.315	P < 0.01	0.318	P < 0.01
	MS4A4A	0.650	P < 0.01	0.480	P < 0.01	0.594	P < 0.01
Dendritic cells	CD209	0.490	P < 0.01	0.399	P < 0.01	0.412	P < 0.01
NK cells	KIR3DL3	0.383	P < 0.01	0.249	P < 0.01	0.367	P < 0.01
NK cells	NCR1	0.038	0.645	0.123	0.135	-0.150	0.066
Th1 cells	TBX21	0.681	P < 0.01	0.522	P < 0.01	0.738	P < 0.01
Treg	FOXP3	0.641	P < 0.01	0.588	P < 0.01	0.708	P < 0.01
Treg	CCR8	0.296	P < 0.01	0.404	P < 0.01	0.088	0.283
Monocyte	C3AR1	0.893	P < 0.01	0.764	P < 0.01	0.791	P < 0.01
	CD86	0.874	P < 0.01	0.727	P < 0.01	0.912	P < 0.01
	CSF1R	0.865	P < 0.01	0.689	P < 0.01	0.784	P < 0.01

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NK cells, Natural killer cells; Th1 cells, type I helper T cells; Treg, regulatory T cells; COR, r value of Spearman’s correlation.

These important findings further confirmed that the expression of the three prognostic biomarkers in seminoma was correlated with immune infiltration.

Discussion

In recent decades, accumulated experiences and rapid development in surgeries, medicine, and radiology have provided an extremely high five-year survival rate for patients with TGCT, and this malignancy has become a kind of curable solid neoplasm [22]. However, for patients who fail first-line treatment or have solitary testicles, immunotherapy can be considered an alternative treatment strategy [5]. In such situations, it is necessary to identify novel biomarkers, which may be potential therapeutic targets and play critical roles in improving the prognosis of seminomas.

In the present study, the blue module with 738 genes was selected as the hub seminoma-correlated module. After intersection with 1297 DEGs in GSE8607, 155 common genes were identified for further analysis. Based on PPI network construction and analysis, the top 50 hub genes were screened, followed by functional enrichment analyses. The results revealed that the top 50 genes were mainly enriched in inflammation, immune response and signal transduction. Subsequently, the 10 genes with the highest BC were identified and evaluated by survival analyses and Cox hazards regression analyses, after which TYROBP, CD68 and ITGAM were identified as prognostic biomarkers in seminoma. In addition, the correlation between these three biomarkers with immune infiltration implies an important role in tumor immunity in seminoma.

TYROBP, also known as DAP12, KARAP or PLOSL, encodes a transmembrane signaling polypeptide that contains an immunoreceptor tyrosine-based activation motif (ITAM) in its cytoplasmic domain. The encoded protein may associate with the killer cell immunoglobulin-like receptor (KIR) family of membrane glycoproteins and may act as an activating signal transduction element. That is to say that TYROBP plays critical roles in the immune system and signal transduction. Currently, its expression and clinical value have been studied in multiple cancers. Upregulated TYROBP, as previously reported by Stelios et al., was associated with advanced breast cancer grade and metastasis to the bone and liver [23]. Liu et al. conducted a bioinformatics analysis to identify biomarkers for liver cancer and found that TYROBP was the hub gene and may be a potential therapeutic target in liver cancer [24]. In addition, the results of a genome-wide cDNA microarray analysis showed that TYROBP was upregulated 5-fold or more in seminoma. The authors did not further explore the biological process for this gene in seminoma [25]. The overexpression of TYROBP in seminoma was also observed in our study and had predictive value for poor prognosis in patients with seminoma.

Elena et al. reviewed relevant literature and concluded that TYROBP is a wiring component for NK cell anti-tumor function via its association with NKp44. In addition, TYROBP is associated with inflammation through its binding to specific receptors displayed by inflammatory cells such as monocytes/macrophages, neutrophils, and dendritic cells. Furthermore, the authors reported that TYROBP played essential roles in brain function and bone remodeling [26]. The roles of this gene seemed contradictory in the literature, due to its high expression in tumor samples and anti-tumor function through NK cell activation. We further searched the GEPIA to determine the expression of TYROBP in multiple cancers and normal samples. The results revealed that this gene was upregulated in most cancer samples, such as TGCT, breast cancer and cervical cancer, while in large B-cell lymphoma and thymoma, this gene was downregulated. Taken together with its important role in tumor immunity, this suggests the gene may be cancer-specific and its aberrant expression is correlated with tumorigenesis.

CD68, as reported in a previous study, provided a good predictive value as a prognostic marker for survival in cancer patients. The authors described that low expression of this gene was found in tumor cells [27]. After consulting the GEPIA, we noticed that the gene was upregulated in most cancers including TGCT and downregulated in lung adenocarcinoma and thymoma.

The expression and effects of CD68 have mostly investigated in immunohistochemical studies of various tumors. For example, positive immunophenotypical features of CD68 have been observed in bellini carcinoma, a rare type of renal malignancy [28], and testicular myeloid sarcoma [29]. Regarding the features of CD68 in seminoma, Tine et al. studied the phenotypic characterization of immune cell infiltrates in 41 TGCTs and found that a high proportion of them were identified as CD68+ macrophages [30]. Moreover, the authors reported the absence of active immune surveillance in TGCT, suggesting a potential role for CD68 in tumor immunity. Sam et al. performed immunohistochemistry in 51 seminomas and 26 non-seminomatous germ cell tumors, and found that germ cell tumors primarily expressed PD-L1 (a known checkpoint in tumor immunity) on tumor-associated CD68+ macrophages [31]. Furthermore, the expression features of these macrophages were more significant in seminomas than in non-seminomatous germ cell tumors. These results provide robust evidence that CD68 is a key molecule in the pathological process of seminoma. Additionally, we speculate that the gene may have a potential association with immune checkpoint pathways according to the findings provided in the published literature.

ITGAM, also known as macrophage-1 antigen (Mac-1) or complement receptor 3 (CR3), has been explored in multiple types of diseases. Numerous previous studies have reported a biological function for ITGAM in the development of systemic lupus erythematosus [32, 33]. Agarwal et al. conducted proteomic analysis to identify core sperm proteins in patients with seminoma via cryopreserved semen samples [34]. The results revealed that ITGAM protein was downregulated in seminoma, and may be involved in spermatogenesis, motility function, and infertility. The potential mechanism for ITGAM-relevant asthenozoospermia in patients with testicular cancer was also studied by Selvam et al. [35]. However, direct evidence on ITGAM and its molecular mechanism in seminoma are limited in the present literature.

Moreover, potential roles for ITGAM in various malignant tumors have also been reported. One study by Joanna et al. explored ITGAM in the progression and prognosis of renal cancer and found that aberrant expression of ITGAM was significantly correlated with renal cell carcinoma as compared with controls. Moreover, the expression signature of this gene was strongly associated with poor survival [36]. ITGAM and ITGB6 have been confirmed to play critical roles in ovarian cancer invasion and implant metastasis [37]. One study investigating biomarkers in breast cancer brain metastasis via integrated genomic and epigenomic analysis showed that hypermethylation and downregulation of ITGAM were associated with defects in cell migration and adhesion [35]. One meeting report in 2016 described that ITGAM protein positive tumor associated macrophages were associated with tumor angiogenesis promotion and immunosuppression [38].

Nevertheless, the limitations of this study must be clearly pointed out. First, future studies in vivo or in vitro are needed to elucidate the detailed molecular mechanisms for these hub genes in seminoma. Second, a larger number of samples are required to make our findings more convincing. Third, in our study, three prognostic biomarkers were identified and analyzed. Zaman and colleagues also performed an integrated network analysis by integrating genomic alteration information and functional genetic data. They found that the networks could effectively predict subtype-specific drug targets which have been experimentally validated. By taking advantage of this integrated network analysis, more immunotherapy targets for seminoma could be identified and clinically applied [39]. Emerging evidence has shown that non-coding RNA biomarkers play important roles in various human diseases including seminoma [40]. With the rapid development of computational prediction models, Chen et al. proposed several innovative prediction models to identify non-coding RNA biomarkers correlated with human diseases [41–44]. Future studies will attempt to find significant non-coding RNA biomarkers of seminoma and may take advantage of these state-of-the-art computational models.

Conclusion

Three novel biomarkers, TYROBP, CD68 and ITGAM, were identified from databases and correlated with poor prognosis in patients with seminoma. Furthermore, all of them were significantly positively correlated with immune infiltration, indicating that they may be potential targets for immunotherapy. Future experimental studies are needed to validate our findings and explore the molecular mechanisms of the three genes in the context of seminoma.

Supporting information

S1 Fig. Flowchart of the construction of weighted correlation network.

(TIF)

Click here for additional data file.^{(92.4KB, tif)}

Data Availability

The analyzed GEO dataset with 40 samples of seminoma and 3 samples of controls, can be found with accession number GSE8607 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE8607).

Funding Statement

Corresponding author GC received the award Grant number: cstc2015shmszx120067 Full name of the funder: Chongqing Science and Technology Commission URL of the funder website: http://kjj.cq.gov.cn/ The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0240943.r001

Decision Letter 0

Edwin Wang

13 Aug 2020

PONE-D-20-17839

Three novel prognostic biomarkers for seminoma identified by weighted gene coexpression network analysis

PLOS ONE

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A related work using network modules (PMID: 24075989) should be discussed

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Reviewer #1: No

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #2: No

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Reviewer #1: The present manuscript titled as “Three novel prognostic biomarkers for seminoma identified by weighted gene coexpression network analysis” used a WGCNA method to analyze seminoma-correlated modules and core genes for identifying prognostic biomarkers for seminoma. Overall, the approach employed in this study is to straightforward and the results are not solid. Below are some comments:

1. “Three novel prognostic biomarkers” is not clear. I suggest to revise it as “Three-gene prognostic biomarkers”.

2. Abstract is not concise enough.

3. Many grammatic errors. English should be improved.

4. Table 1: Why “P value in Survival analysis” and “P value in Univariate Cox analysis” were different? The “P value in Univariate Cox analysis” for TYROBP is not less than 0.05, and the “P value in Survival analysis” for ITGAM is also not less than 0.05. So how to interpret these genes were significantly corelated with survival?

5. The authors claimed the three genes can be used as prognostic biomarker, but no intendent dataset was used for validation. They just analyzed the association of these genes to survival in the TCGA dataset.

Reviewer #2: The author used weighted gene coexpression network analysis and identified three novel prognostic biomarkers for seminoma. I hope the manuscript could be further strengthened by the following comments.

1. Please clearly state the major innovation of this work.

2. More details about the construction of weighted correlation network should be given. How was the correlation matrix constructed and transformed into weighted adjacency matrix? What is the blue and green modules?

3. Please add a flowchart of the construction of weighted correlation network.

4. Please give the reason for parameter selection (e.g., the soft threshold).

5. I want to know whether this network analysis can also be used for the analysis of the other cancers?

6. You should revise your English writing carefully and eliminate small errors in the paper to make the paper easier to understand.

7. Could you discuss the recent trend of developing computational model for identification of the non-coding RNA biomarker of human complex diseases as the future direction of your current research about biomarker identification of seminoma? Some important studies should be cited and discussed (PMIDs: 31927572, 29939227, 29045685, and 24002109).

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2020 Oct 26;15(10):e0240943. doi: 10.1371/journal.pone.0240943.r002

Author response to Decision Letter 0

15 Sep 2020

Dear editors,

Re: Manuscript reference PONE-D-20-17839

Please find attached a revised version of our manuscript “Three-gene prognostic biomarkers for seminoma identified by weighted gene coexpression network analysis”, which we would like to resubmit for publication in PLOS ONE.

The comments of the academic editor and reviewers were highly insightful and enabled us to greatly improve the quality of our manuscript. In the following pages are our point-by-point responses to each of the comments.

Revisions in the text are shown using the red highlight for additions, and strikethrough font [example: Three novel] for deletions. The revisions have been organized according to the journal requirements. Written permission from the copyright holder to publish Figure 5C under the CC BY 4.0 license is obtained and uploaded simultaneously. The specific text was added to the copyrighted figure caption in the revision. We have sought for English-language editing service for help, considering many grammatical errors in the original manuscript. And the service proof is uploaded with the revisions.

We look forward to hearing from you regarding our submission. We would be glad to respond to any further questions and comments that you may have.

We shall look forward to hearing from you at your earliest convenience.

Thank you and best regards.

Yours sincerely,

Hualin Chen

Corresponding author:

Name: Gang Chen

E-mail: chengang2308@163.com

Response to the comment of Academic Editor

1. A related work using network modules (PMID: 24075989) should be discussed

Response: Thank you for your comment and this nice work has been discussed in the revision. Zaman et al performed wonderful integrated network analysis and provided a unique insight into the underlying mechanisms of cancer cell survival and proliferation driven by genomic alterations. More importantly, drug targets could be identified through this network analysis, indicating the potential significant clinical value of this work.

Responses to the comments of Reviewer #1

1. “Three novel prognostic biomarkers” is not clear. I suggest to revise it as “Three-gene prognostic biomarkers”.

Response: Thanks for your advice and the title is revised.

2. Abstract is not concise enough.

Response: Thank you for your comment and the abstract is revised.

3. Many grammatic errors. English should be improved.

Response: I feel ashamed of my poor English and I am sorry for your unpleasant reading experience. I have asked the English language-editing service for help to revise the paper.

Response: Thanks for your comments. (1) “P-value in Survival analysis” indicated the P value calculated by log-rank test (Kaplan–Meier survival analysis), while “P-value in Univariate Cox analysis” indicated the P-value calculated by Cox proportional hazards regression model. Differences in statistical principles may cause different results. However, both methods were widely used in prognostic biomarkers identification. (2) The overall survival rate of seminoma patients was relatively high and fewer death events were observed in the TCGA datasets. If only one method was employed, some clinical valuable biomarkers may be ignored. For example, according to the KM survival analysis (log-rank test), gene ITGAM was not significantly correlated with prognosis. While univariate cox analysis and immunohistochemistry suggested the potential prognostic value of this biomarker. With the attempt to identify more valuable biomarkers of seminoma, we think that it may be a proper manner to integrate the results of both methods, instead of the intersection. (3) As we addressed in our paper, the lack of further experimental validation was the main drawback of our work. Therefore, further researches are urgently needed to validate our findings and explore the molecular mechanism of these biomarkers with tumor immunity in-depth.

Response: I am sorry for the limitation of our work. Seminoma is rare in the general population and the health testis is much hard to obtain. Lack of tumor samples prevents us from further validation. Unfortunately, other common electronic tumor databases like the International Cancer Genome Consortium (ICGC) do not provide seminoma cases for analysis. However, the three biomarkers were also DEGs in GSE8607.

I appreciate your great efforts in our work and your precious comments help us improve the quality of our manuscript.

Responses to the comments of Reviewer #2

1. Please clearly state the major innovation of this work.

Response: (1) This is the first study using WGCNA to identify prognostic biomarkers of seminoma and three prognostic biomarkers, which may be potential immunotherapy targets, are obtained; (2) Tumor immunity is a hot topic currently and previous studies have mentioned the critical role of the immune response in seminoma patients. Our findings further prove the roles of tumor immune in seminoma, which may provide evidence for future immunotherapy.

Response: Thanks for your suggestions. The official tutorials (available at https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/) provide the codes for analysis in detail. What we have to do is to adjust some thresholds according to our samples. (1) More details about the construction of the weighted correlation network are given in the revision. (2) The correlation matrix is constructed based on Pearson correlation analysis (more details are given in the revision). Then, a soft‑thresholding function is used to transform the correlation matrix into a weighted and scale-free adjacency matrix. In this part, identification of a soft-thresholding β (a soft-thresholding parameter enhancing strong correlations between genes and penalizing weak correlations) is of importance. (3) The blue and green modules represent different modules, and the colors of the modules are just used to mark and distinguish these different modules. The colors have no relation to the genes. Genes in the same module have strong correlation with each.

3. Please add a flowchart of the construction of weighted correlation network.

Response: Thank you for your advice. Actually, Figure 2 A-E represents the main workflow of the whole WGCNA. However, we generate a flowchart of the construction of a weighted correlation network according to your suggestion and it is uploaded as a supplementary figure.

4. Please give the reason for parameter selection (e.g., the soft threshold).

Response: Thanks for your comment. (1) Soft threshold: a suitable soft threshold is determined by two factors: scale-independence and connectivity. In our study, scale-independence >0.85 with maximum average connectivity is considered as the criteria for soft threshold selection. And as Figure 2A demonstrated, only power 9 meets the criteria and is selected for further analysis. As reported in the official guidelines, scale-independence >0.80 is acceptable in soft threshold selection. Under certain special conditions, it is less likely to obtain proper power. And power 6 is often empirically used. (2) Adjust P-value < 0.01 and |logFC| ≥2: Actually, both 0.01 and 0.05 can be used as the thresholds of Adjust P-value. As for |logFC|, 1, 2 or even 0.5 is acceptable. There is no strict restriction in the thresholds of Adjust P-value and |logFC|. In our study, the reason we choose a stricter threshold is that a large number of DEGs in GSE8607 are identified through initial analysis and we only want to find out more significant genes with lower Adjust P-value and higher |logFC|.

5. I want to know whether this network analysis can also be used for the analysis of the other cancers?

Response: Thanks for your question and the answer is yes.

6. You should revise your English writing carefully and eliminate small errors in the paper to make the paper easier to understand.

Response: I am extremely sorry for the grammar errors in our original paper. I have sought for language-editing service for help.

Response: Your comments and advice are filled with wisdom. These researches contribute a lot to the development of computational biology and these computational models provide new methods to find novel and more significant disease-related non-coding RNA biomarkers. More excellent works, I believe, could be carried out soon based on these models. These works have been cited and discussed in the revision.

Attachment

Submitted filename: Rebuttal letter.docx

Click here for additional data file.^{(26.6KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0240943.r003

Decision Letter 1

Edwin Wang

6 Oct 2020

Three-gene prognostic biomarkers for seminoma identified by weighted gene co-expression network analysis

PONE-D-20-17839R1

Dear Dr. Chen,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Edwin Wang

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: No

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: Please provide the source code of the analysis in this study publically available, which is necessary for reproducing the results.

Reviewer #2: It could be accepted now

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

PLoS One. doi: 10.1371/journal.pone.0240943.r004

Acceptance letter

Edwin Wang

16 Oct 2020

PONE-D-20-17839R1

Three-gene prognostic biomarkers for seminoma identified by weighted gene co-expression network analysis

Dear Dr. Chen:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Edwin Wang

Academic Editor

PLOS ONE

Associated Data

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

Supplementary Materials

S1 Fig. Flowchart of the construction of weighted correlation network.

(TIF)

Click here for additional data file.^{(92.4KB, tif)}

Attachment

Submitted filename: Rebuttal letter.docx

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Data Availability Statement

The analyzed GEO dataset with 40 samples of seminoma and 3 samples of controls, can be found with accession number GSE8607 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE8607).

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PERMALINK

Three-gene prognostic biomarkers for seminoma identified by weighted gene co-expression network analysis

Hualin Chen

Gang Chen

Yang Pan

Xiaoxiang Jin

Roles

Abstract

Introduction

Materials and methods

Data acquisition and pre-processing

Fig 1. Flow chart indicating the workflow used for prognostic biomarkers selection in the analysis.

Construction of a weighted correlation network and identification of modules associated with seminoma

Identification of DEGs in GSE8607

Identification of common genes, PPI network construction and functional annotation

Identification of prognostic biomarkers

Immune infiltrate analysis in seminoma

Results

Construction of the co-expression network and identification of seminoma-correlated modules

Fig 2. Identification of hub seminoma-correlated module.

Identification of DEGs in GSE8607

Fig 3. Identification of DEGs in GSE8607.

Identification of common genes, PPI network construction, and enrichment analysis

Fig 4. Identification of hub biomarkers.

Investigation of the prognostic significance of hub genes

Fig 5. Prognostic significance of hub biomarkers.

Table 1. The results of survival analyses and univariate cox analyses of the top 10 genes in the PPI network.

Correlations of prognostic biomarkers with lymphocyte infiltration levels in seminoma

Fig 6. Correlation between expressions of TYROBP (A), CD68 (B) and ITGAM (C) and immune infiltration in seminoma according to the TIMER database.

Table 2. Correlation analysis between three prognostic biomarkers and immune cell type markers in the TIMER database.

Discussion

Conclusion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Edwin Wang

Roles

Author response to Decision Letter 0

Decision Letter 1

Edwin Wang

Roles

Acceptance letter

Edwin Wang

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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