The Identification of Hub Genes Associated with Pure Ground Glass Nodules Using Weighted Gene Co-Expression Network Analysis

doi:10.21203/rs.3.rs-3607498/v1

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Research Article

The Identification of Hub Genes Associated with Pure Ground Glass Nodules Using Weighted Gene Co-Expression Network Analysis

https://doi.org/10.21203/rs.3.rs-3607498/v1

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Journal Publication

published 10 Jun, 2024

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Background

Whether there are invasive components in pure ground glass nodules(pGGNs) in the lungs is still a huge challenge to forecast. The objective of our study is to investigate and identify the potential biomarker genes for pure ground glass nodule(pGGN) based on the method of bioinformatics analysis.

Methods

To investigate differentially expressed genes (DEGs), firstly we used the data obtained from the gene expression omnibus (GEO) database.Next Weighted gene co-expression network analysis (WGCNA) was used to investigate the co-expression network of DEGs and seven key modules were found. We chose the black key module as the key one in correlation with pGGN. SangerBox software was used for analyses of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Then we used STRING to create a protein-protein interaction (PPI) network, and the chosen module genes were analyzed by Cytoscape software. At last the polymerase chain reaction (PCR) was used to evaluate the value of these hub genes in pGGN patients’ tumor tissues compared to controls.

Results

There are a total of 4475 DEGs were screened out from GSE193725, then 225 DEGs were identified in black key module. And these DEGs were found to be enriched for various functions and pathways, such as extracellular exosome, vesicle, protein processing in endoplasmic reticulum, metabolic pathways, ribosome, and so on. Among these DEGs, 6 overlapped hub genes with high degrees of stress method were selected. These hub genes include RPL4, RPL8, RPLP0, RPS16, RPS2 and CCT3.And relative expression levels of CCT3 and RPL8 mRNA were both regulated in pGGN patients’ tumor tissues compared to controls. To summarize, the determined DEGs, pathways, modules, and overlapped hub genes can throw light on the potential molecular mechanisms of pGGN.

pure ground glass nodules(pGGNs)

hub genes

WGCNA

ribosome

With the widely application of computed tomography with low dose,a gradually increasing number of pGGNs have been detected^[1].Known as ground glass nodules (GGNs), these nodules mainly exists in bronchial and vascular margins in the lung ^[2].GGN can be further divided into part-solid GGN and pGGN,according to whether solid components are existed in the lesion ^[3]. If an invasive pGGN persist for a long term, an early malignant tumor may be associated with this condition, so it is crucial to distinguish the invasiveness of pGGNs ^[4]. Although a large number of pGGNs may be benign lesions,however, there are still some invasive malignancies, which are detected through surgical resection, and the proportion of which vary from 1.7–24.3% ^[5]. The best treatment time to intervene pGGN is to remove high-risk pGGN before it spreads to metastatic sites, however,it is limited due to clinical examination's sensitivity^[6].AND, up to now, there are few studies that have used radiomic feature analysis,which is to predict histologic subtypes in a cohort that there are only pGGNs composed ^[5].

WGCNA is an efficient and convenient technique to process transcriptome data, because it can not only cluster the tightly connected genes into various modules, but also explore module correlations, and Identify traits that interest you ^[7]. The Search Tool for Retrieval interacting Genes (STRING) ^[8] online databases were employed to build a network of protein–protein interactions (PPIs).

A profile of GSE gene expression was reanalyzed in this study using 23 patients with pGGN and 10 controls. Then we used the ‘limma’ on SangerBox network to obtain DEGs,and WGCNA conducted this study to create a network of gene co-expression, black key module was chose. This module analyzed gene ontologies and KEGG pathways for enrichment.PPI networks were constructed in the black module using the STRING database. At last by cytoscape software using the method of different calculations,the top 10 hub genes in the black module were identified, and six overlapped hub genes were screened out.

2.1. sources of data

In Fig. 1, you can see the workflow of the study.An expression profile microarray GSE of mRNA transcripts, deposited by Nasser K. Altorki et al. ^[9], was retrieved from a free public database named Gene Expression Omnibus (GEO).It includes lung tissue from pGGN patients and controls.And on platform GPL24676 the data were got, including 54 samples, 23 from pGGN patients, 10 from controls and 21 from solid nodule patients, and the date of 21 from solid nodule patients were canceled.

2.2. DEGs identification

Firstly, data quality checks and log transformations were performed in order to cancel batches.Based on generalized linear models,Limma ^[10] is a method for screening differential expression data from microarrays, here we use limma (version 3.40.6) on SangerBOX to identify DEGs between the control and pGGN groups. For statistical significance, an adjusted p-value of 0.05 was used. For visualization of the top 50 DEGs, the Heatmap package was used to plot the volcano map.

2.3. key module identified by WGCNA

Based on the scale-free topology criteria,WGCNA ^[11] was employed to build the co-expression networks in DEGs. Firstly, with the soft thresholding power determined,all DEGs were analyzed. Secondly, A weighted co-expression network was created, and seven DEG clusters were labeled according to their different colors. A correlation was then explored between the modules and pGGNs or controls.The black module is chosen for the most correlated with pGGN,and the chosen module was considered a key component to do further more enrichment analysis.

2.4. analysis of gene ontologies and pathways

With Metascape^[12] transcriptome and genome data are efficiently invested in biological processes.In the black module both of GO^[13] and KEGG ^[14] pathway analysis were dealt with. In order to visualize the terms for the functions and pathways, they were retrieved.

2.5. PPI network and potential hub genes identification

The STRING^[8] online database was used to create the PPI network in the black module.And the black key module employed an online database to build the PPI network,which was processed by Cytoscape software ^[15].Then we used Cytohubba ^[16] to get the hub genes,and we used calculate and draw custom vegn diagrams to get six overlapped hub genes.

2.6. gene set enrichment analysis(GSEA)

In order to identify whether the KEGG pathway is statistically significantly enriched in pGGN and control subjects,the GSEA^[17] was conducted,whose desk application was used to import transcriptome data according to instructions on the website. The significant gene sets were determined by both p < 0.05 and FDR < 0.25.

2.7. hub gene-mediated signaling pathways

‘GeneMania’ ^[18] is used to predicate and identify the function of chosen hub genes. For the establishment of a putative PPI, we imported the 10 top hub genes into a database called 'Genemania'. We processed and visualized the results .

2.8. case collection

A total of 9 pGGN cases from Tianjin Medical University General Hospital were enrolled in this study, and patients were included if the following criteria were met: 1) preoperative chest HRCT examinations performed within 3 weeks; 2) tumor and control tissue specimens were complete and reviewable;3) P-pGGNs located next to the pleura on the preoperative HRCT, defined as pGGNs. Normal corresponding adjacent lung tumor tissues whose distance from the edge of tumor was more than 3cm were regard as the controls. All samples were immediately frozen and saved in liquid nitrogen. Enrolled patients had not received neoadjuvant chemotherapy or radiotherapy. Patients underwent emergency surgery were excluded.

2.9. Quantitative real-time polymerase chain reaction (qRT-PCR)

The total RNA in tissues was isolated using RNAzol® RT according to the instructions (GeneCopoeia, Rockville, MD, USA). And RNA was reverse transcribed into complementary DNA (cDNA) using reverse transcription kit (Thermo, USA). The real-time quantitative PCR was performed according to the instructions of SYBR GREEN kit (TaKaRa, Japan) with Roche LightCycler480® Probes Master reagent (Roche, Basel, Switzerland). GAPDH served as internal reference control. The relative expression levels of genes were calculated by the 2^−ΔΔCt method. All primers used were synthesized by Sangon Biotech (Shanghai, China) and the sequences were listed in Table 2.

2.10. Statistical Analysis

All experiments were performed in triplicate, and data were pooled from three independent experiments. Data of gene expression analysis was expressed as mean ± standard deviation (SD) and estimated using Mann–Whitney nonparametric test. All the rest of the experiments were used paired t-test or one-way ANOVA test. T-test was used for the comparison of composition ratio between groups. SPSS 22.0 software (SPSS, Chicago, IL, USA) and Graphpad Prism version 8.0 (GraphPad Software Inc, La Jolla, Calif) was used for statistical analysis. P༜0.05 was considered statistically significant.

3.1. identifications of DEGs

There were totally 4475 DEGs identified between pGGN patients and controls, with an adjusted p-value of < 0.05. In pGGN, 2415 DEGs were upregulated and 2060 were downregulated. Figure 2A shows the volcano map of almost all DEGs,and Fig. 2B displays the heatmap of the top 50 DEGs.

3.2. WGCNA Analysis

Further processing was performed on the 4475 DEGs identified with the WGCNA package in Rsoftware on SangerBOX.As part of the subsequent analysis,a soft thresholding power of 6 was chosen.And the scale showed in Fig. 3A had a relatively good average connectivity.We found these DEGs were totally clustered into seven different modules(Fig. 4A),black,brown,blue, greenyellow,pink,purple, andsalmon, Using a minimal module size of 30. Figure 3B showed the cluster dendrogram of these DEGs.And Fig. 4B showed the correlation between pGGN and each module. The results indicated that black(0.66, p < 0.0001) model was one of the most positive module and brown (-0.90, p < 0.0001) was the most negative module. Then we chose the upregulated black module, which included a total of 590 DEGs, as the key module related to pGGN.

Using Metascape, 590 DEGs in the black module were analyzed for GO and pathway enrichment.The result showed that all DEGs in the black module were found enriched in 52 molecular functions (MF), 169 cellular components (CC), and 422 biological processes (BP). Figure 5A–C showed the top 10 BPs, CCs, and MFs. Figure 6A-B showed the result of Metascape tool pathway enrichment analysis. The GO category showed that ‘endomembrane system,’ ‘vesicle,’ and ‘extracellular space’ were enriched markedly.And KEGG analysis revealed that the black module was enriched in 24 pathways,such as ‘Lysosome,’ ‘Ribosome,’ ‘Protein processing in endoplasmic reticulum’ (Fig. 5D),and so on.

3.3. GSEA Analysis

With GSEA software, we explored how pathway gene sets were distributed among the pGGN patients and controls.The results demonstrated that ‘VASCULAR_SMOOTH_MUSCLE_CONTRACTION’ was the most remarkably enriched in the pGGN group, and Fig. 5A showed its enrichment score is 0.55, suggesting that ‘VASCULAR_SMOOTH_MUSCLE_CONTRACTION’ may play an important role in the pathophysiology of pGGN patients.And Fig. 7A–F showed the top up-regulated and down-regulated gene sets .

3.4. PPI network construction and overlapped hub Gene analysis

We used STRING database in order to explore the interaction between these genes in the black module. And the STRING PPI network was constructed by setting 0.4 as the minimum interaction score. Figure 8A showed that the PPI network included totally 587 nodes and 3810 edges. The top ten hub genes (Table 1)were screened out by twelve different calculation algorithms,such as MNC, MCC,EPC,DMNC,Degree,and so on,Using the cytoHubba plug-in in Cytoscape. The top 10 genes ranked by Degree calculation algorithms were analyzed in GeneMANIA (Fig. 8B-C).And six overlapped hub genes(Fig. 8D), including CCT3, RPL4, RPL8, RPLP0, RPS16, RPS2 were identified, using calculate and draw custom vegn diagrams,which were among the five algorithms (MNC, Degree, EPC, Closeness, Radiality).

3.5. validation of the hub genes

The transcriptional changes of six overlapped hub genes were detected in the lung tumor tissues and controls from the pGGN patients by quantitative RT-PCR. The results indicated that CCT3 and RPL8 were more highly expressed in pGGN tissues than those in adjacent tissues (Fig. 9A-F), showing that the identified hub genes CCT3 and RPL8 demonstrated a powerful discrimination capability as potential biomarkers for pGGN.

Table 1

Top ten hub genes obtained by eleven algorithms of Cytohubba.
DMNC	MNC	Degree	EPC	BottleNeck	EcCentricity	Closeness	Radiality	Betweenness	Stress	ClusteringCoefficient
EIF3M	RPLP0	RPLP0	RPL4	CDH1	XBP1	RPLP0	HSPA5	CDH1	HSPA5	HSPA5
RPS27L	RPL4	RPL4	RPLP0	XBP1	HSPA5	GNB2L1	GNB2L1	HSPA5	XBP1	XBP1
RPLP1	RPS16	RPL8	RPS16	GNB2L1	HSP90B1	HSPA5	HSPD1	GNB2L1	GNB2L1	GNB2L1
EIF6	RPL8	RPS16	RPL8	KDM1A	SSR3	RPL8	RPLP0	HSPD1	CDH1	CDH1
EIF3H	RPS2	GNB2L1	RPS2	CLTC	SRPRB	HSPD1	RPL8	XBP1	HSPD1	HSPD1
PSMD11	GNB2L1	RPS2	CCT5	CCT3	KRTCAP2	RPL4	RPL4	CLTC	SEC61A1	SEC61A1
EIF3I	RPL5	CCT3	RPL5	RPL23	PKM	CCT3	CCT3	SEC61A1	RPLP0	RPLP0
RPL17	RPL23	RPL5	CCT7	SNRPG	GPX4	RPS16	RPL23	CCT3	CCT3	CCT3
IFRD2	CCT7	RPL23	CCT3	HSP90B1	MRPL24	RPS2	CCT7	TUFM	TUFM	TUFM
RPLP2	CCT3	CCT7	GNB2L1	RPL8	WDR12	RPL23	RPS2	ALDH18A1	RPL8	RPL8

Table 2

Primer sequences for quantitative real-time PCR.
Gene	Primer Sequence (5’→3’)
GAPDH	Forward	GGAAGCTTGTCATCAATGGAAATC
	Reverse	TGATGACCCTTTTGGCTCCC
CCT3	Forward	TCAGTCGGTGGTCATCTTTGG
	Reverse	CCTCCAGGTATCTTTTCCACTCT
RPL4	Forward	GCCAGGGTGCTTTTGGAAAC
	Reverse	GGGCAGAACAGATGGCGTAT
RPL8	Forward	AGAAGACCCGTGTGAAGCTG
	Reverse	GGTTTGTCAATTCGGCCACC
RPLP0	Forward	TGGGCAAGAACACCATGATG
	Reverse	CGGATATGAGGCAGCAGTTTC
RPS16	Forward	GTGTCCGTGTAAAGGGTGG
	Reverse	ACTGGATGAGGATGTCTTTGATC
RPS2	Forward	GCCAAGCTCTCCATCGTC
	Reverse	GTGCAGGGATGAGGCGTA

Nowadays adenocarcinoma,one subtype of lung cancers, is the most common histologic type,which always appears as localized GGOs on CT, and a pure ground glass nodule is likely a preinvasive lesion ^[19]. Although there are several types of pGGNs, all of which are slow-progressing diseases, but they can show differing growth patterns and degrees of invasion, such as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and even invasive adenocarcinoma (IAC)^[20].IF the pGGN is AIS or MIA, a small wide wedge dissection would be enough without lymph node, but a small peripheral IAC need segmentectomy, which may be more suitable ^[5].So it is crucial to elucidate the effective diagnostic markers of pGGN.

Our study used the GEO database to download mRNA expression profile GSE193725 and then perform a WGCNA on it. Using the novel approach, all 4475 DEGs were obtained by the ‘limma’ on SangerBox network,all these DEGs were then clustered into seven modules. And the black module,which included 590 genes,was chosen to be the most upregulated module closely related to the pGGN(whose correlation score is 0.66,and p < 0.0001). All of these identified genes were enriched in ‘Lysosome,’ ‘Ribosome,’ and ‘metabolic pathways’ mainly. GSEA showed that ‘VASCULAR_SMOOTH_MUSCLE_CONTRACTION’ was markedly in the pGGN group. Six overlapped hub genes, CCT3, RPL4, RPL8, RPLP0, RPS16, RPS2, were identified by cytoscape software. And we found the identified hub genes CCT3 and RPL8 demonstrated a powerful discrimination capability as potential biomarkers for pGGN.

using mRNA as a template, and using amino acids as raw materials to synthesize proteins is the ribosome's primary function ^[21]. Previous studies have showed that the ribosome has a great deal of functions, including affecting protein synthesis and taking a significant role in cell differentiation, proliferation,transformation and apoptosis ^[22]. The ribosome biogenesis dysregulation was important in cancer because it identifies nucleoli with irregular shapes and numbers ^[23]. And ribosome biogenesis hyperactivation addiction in cancer cells is clear, by illustrating its numerous molecules as cancer treatments through impairing ribosome production^[24].In normal cells,ribosomes are involved in proliferating by synthesizing proteins, but in cancer cells,ribosomes are needed according to metabolic requirements. And what’s more, previous study has reported that cancer cells express about nearly 10,000 different proteins^[25]. Ribosome biogenesis can be a target in cancer cells because of three main reasons,firstly in certain cancer types and its cell populations,this process is highly active and essential,secondly several chemotherapy drugs have been already proven to exert their some pharmacological effects partly,through impairing ribosome biogenesis, and last but not the least blocking ribosome biogenesis can lead in a lot of cases to the activation of p53 ^[26].

RPL4 could be bound by PRDX2, being reduced the interaction between MDM2, resulting in promoting the proliferation of colorectal cancer cells ^[27].RPL8 is reported that with pancreatic tumor grades it alters significantly, which may predict a differential expression prognosis of pancreatic cancer^[28]. RPLP0 suppressed by the upregulated level of miR-4731-5p, can be a target in non-small-cell lung cancer ^[29]. And for advanced hepatocellular carcinoma cells,RPS16 may be a potential novel target^[30].PRS2 identified by hub genes was lowly expressed in rats with pulmonary arterial hypertension ^[31]. To sum up, RPL4, RPL8, RPLP0, RPS16, RPS2 and CCT3, have the powerful ability to screen out pGGN from the controls. And CCT3 and RPL8 demonstrated a powerful discrimination capability as potential biomarkers for pGGN. But it is still required that more sample size is needed to validate the efficacy of these overlapped genes as biomarkers for pGGN in the immediate future.

To conclude, we re-analyzed the expression profile GSE193725 with WGCNA. And we screened out six overlapped hub genes in pGGN, including RPL4, RPL8, RPLP0, RPS16, RPS2 and CCT3. However, research on the role of these hub genes in pGGN remains limited. The results show that RPL4, RPL8, RPLP0, RPS16, RPS2 and CCT3 can be served as potential target biomarker genes for diagnosis for pGGN. And CCT3 and RPL8 demonstrated a powerful discrimination capability as potential biomarkers for pGGN.

Acknowledgements：The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.

Ethics approval and consent to participate：All tissue specimens were used after approval from the Ethics Committee of Tianjin Medical University General Hospital (Ethical No. IRB2021-WZ-055). The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author Contributions: YC contributed to write the manuscript, performed data selection and data analysis, methodology, write original draft preparation. ZS contributed to visualize, conceptualization, writing review and editing, supervision and design of the research. All authors contributed to the article and approved the submitted version. All authors have read and agreed to the published version of the manuscript.

Funding: The present study was funded by the National Natural Science Foundation of China (82172776), Tianjin Science and Technology Plan Project (19ZXDBSY00060), Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-061B) and (TJSYXZDXK043), and Diversified Input Project of Tianjin National Natural Science Foundation (21JCYBJC01770).

Data Availability Statement: The mRNA expression dataset used in our study was downloaded from the Gene Expression Omnibus (GEO) under accession number GSE193725 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE, accessed on 22 March 2023).

Conflicts of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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The Identification of Hub Genes Associated with Pure Ground Glass Nodules Using Weighted Gene Co-Expression Network Analysis

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Figures

1. Introduction

2. Methods and Materials

2.1. sources of data

2.2. DEGs identification

2.3. key module identified by WGCNA

2.4. analysis of gene ontologies and pathways

2.5. PPI network and potential hub genes identification

2.6. gene set enrichment analysis(GSEA)

2.7. hub gene-mediated signaling pathways

2.8. case collection

2.9. Quantitative real-time polymerase chain reaction (qRT-PCR)

2.10. Statistical Analysis

3. Results

3.1. identifications of DEGs

3.2. WGCNA Analysis

3.3. GSEA Analysis

3.4. PPI network construction and overlapped hub Gene analysis

3.5. validation of the hub genes

4. discussion

5. Conclusions

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1