Up-Regulated Proteins Have More Protein-Protein Interactions than Down-Regulated Proteins.

Microarray technology has been successfully used in many biology studies to solve the protein-protein interaction (PPI) prediction computationally. For normal tissue, the cell regulation process begins with transcription and ends with the translation process. However, when cell regulation activity goes wrong, cancer occurs. Microarray data can precisely give high accuracy expression levels at normal and cancer-affected cells, which can be useful for the identification of disease-related genes. First, the differentially expressed genes (DEGs) are extracted from the cancer microarray dataset in order to identify the genes that are up-regulated and down-regulated during cancer progression in the human body. Then, proteins corresponding to these genes are collected from NCBI, and then the STRING web server is used to build the PPI network of these proteins. Interestingly, up-regulated proteins have always a higher number of PPIs compared to down-regulated proteins, although, in most of the datasets, the majority of these DEGs are down-regulated. We hope this study will help to build a relevant model to analyze the process of cancer progression in the human body.

Introduction

Understanding the biological and molecular processes connected to various disease networks, such as cancer, depends heavily on the study of protein–protein interaction (PPI). Genes produce proteins as their end result. Consequently, PPI can benefit from the information that a microarray gene expression dataset can provide [1]. It provides the expression levels of thousands of genes in the tumor as well as in normal cells at a specific time and condition. Microarrays have been used extensively in the study of biological mechanisms, the discovery of new therapeutic targets, and the assessment of medication responses [2]. Many research papers published to date have primarily concentrated on identifying differentially expressed genes(DEGs) between tumors and normal cells, which does not directly reveal gene-gene connections [3]. However, PPI is a critical methodology for identifying gene-gene interactions [4], which can help us better comprehend complex biological mechanisms. It has been established by many researchers that PPI can identify key genes and pathways in a variety of human malignancies [5].

Data and Methods

Datasets

Four independent cancer gene expression dataset, namely, Gastric Cancer, Lung Cancer (Squamous cell carcinoma and Adenocarcinoma), Prostate Cancer and Hypopharyngeal Cancer, are downloaded from (http://www.biolab.si/supp/bi-cancer/projections). The microarray dataset contains the gene expression data of genes and samples. These samples consist of cancer samples, and normal tissue samples. Detailed information of datasets are listed in Table 1.

Table 1

Characteristics of datasets in this study

Dataset	Cancer type	Platform	Sample		Number of genes
			Tumor	Normal
GSE1987	Lung cancer	Affymetrix GeneChip Human Genome U95 Version [1 or 2] Set HG-U95A	25	9	10541
GSE2685	Gastric cancer	Affymetrix GeneChip Human Full Length Array HuGeneFL	22	8	4522
Singh et al.	Prostate cancer	Affymetrix Human Genome U95Av2 Array	52	50	12533
GSE2379	Hypophary-ngeal cancer	Affymetrix GeneChip Human Genome U95 Version [1 or 2] Set HG-U95A	34	4	9021

Differential Gene Expression Dataset

The microarray data contains the expression patterns of thousands of distinct genes under different conditions. A diagrammatic representation of microarray gene expression data is depicted in Fig. 1 below. When a statistically significant difference in expression levels between two experimental circumstances, such as a disease state and a healthy state, is seen, the differential gene expressions are determined. Finding the differentially expressed genes (DEGs) is crucial to determining which genes are activated or deactivated as a result of a specific disease’s invasion of the human body. A very effective technique to comprehend the roles of the genes and their potential regulatory mechanisms for disease onset and development is to investigate the DEGs linked to cancer. In the present research, we used the R packages edgeR [6] and limma package [7] to evaluate the DEGs between lung cancer and healthy tissues. To identify the significant DEGs, the criterion of log2|FC| with a threshold of 1.5 and p = 0.05 was utilized.

Fig. 1

The diagrammatic representation of the microarray gene expression data

Construction of Protein–Protein Interaction Network

The probes are first translated into the Entrez gene ID using R programming. The probes that had no Entrez ID are repositioned. Then the PPI network is created from the Search Tool for the Retrieval of Interacting Genes (STRING) by mapping the set of up-regulated and down-regulated genes to related proteins using R programming. The PPI networks were built using text mining, experiments, and databases, and species limited to “Homo sapiens”.

Results

Identification of DEGs

All the cancer samples and normal samples of different dataset are analyzed in our study [8]. According to our cutoff criteria (adjust value<0.05), the up-regulated and the down-regulated DEGs are identified between the cancer group and the normal group. The complete list is mentioned in Table 2 and its graphical representation is shown in Fig. 2.

Table 2

Number of differentially expressed genes in different cancer dataset

Cancer type	Up-regulated	Down-regulated
Lung	461	738
Gastric	621	691
Prostate	1338	1876
Hypopharyngeal	394	473

Fig. 2

Up-regulated vs. Down-regulated DEGs

PPI Network

The up-regulated and down-regulated proteins’ respective DGEs are used to build the PPI network. There are 3 types of thresholds offered by STRING to create a PPI network: low(value 0.4), medium (value 0.7), and high (value 0.9). We have considered these three interaction scores as the threshold to build the PPI network. We have noted that the number of down-regulated proteins is higher than the up-regulated proteins in each and every case (Table 2). However, the up-regulated proteins have more PPIs compared to the down-regulated ones in all cancer microarray datasets we have considered in this study Table 3, 4, 5, 6, and Figs. 3, 4, 5, and 6 show that the down-regulated proteins have many fewer PPIs compared to the up-regulated proteins.

Table 3

Number of PPIs of up-regulated and down-regulated proteins of lung cancer considering three thresholds 0.4, 0.7 and 0.9

Lung cancer
Threshold	Up	Down
0.9	146	53
0.7	999	161
0.4	1311	361

Table 4

Number of PPIs of up-regulated and down-regulated proteins of gastric cancer considering three thresholds 0.4, 0.7 and 0.9

Gastric cancer
Threshold	Up	Down
0.9	453	22
0.7	809	137
0.4	1449	357

Table 5

Number of PPIs of up-regulated and down-regulated proteins of prostate cancer considering three thresholds 0.4, 0.7 and 0.9

Prostate cancer
Threshold	Up	Down
0.9	340	48
0.7	553	152
0.4	1775	455

Table 6

Number of PPIs of up-regulated and down-regulated proteins of hypo-pharyngeal cancer considering three thresholds 0.4, 0.7 and 0.9

Hypo-pharyngeal cancer
Threshold	Up	Down
0.9	27	5
0.7	52	17
0.4	257	95

Fig. 3

Up-regulated and down-regulated proteins of lung cancer considering three thresholds 0.4, 0.7 and 0.9. Green color represents up-regulated and grey color represents down-regulated proteins

Fig. 4

Up-regulated and down-regulated proteins of gastric cancer considering three thresholds 0.4, 0.7 and 0.9. Green color represents up-regulated and grey color represents down-regulated proteins

Fig. 5

Number of PPIs of up-regulated and down-regulated proteins of prostate cancer considering three thresholds 0.4, 0.7 and 0.9. Green color represents up-regulated and grey color represents down-regulated proteins

Fig. 6

Number of PPIs of up-regulated and down-regulated proteins of hypo-pharyngeal cancer considering three thresholds 0.4, 0.7 and 0.9. Green color represents up-regulated and grey color represents down-regulated proteins

Biological Significance

Gene expression profiling using microarrays has been recognized as a valuable method for the physiological processes involved in response to a specific stimulus. Therefore, understanding the up-regulated and down-regulated proteins helps to handle metabolism-related and pathogen-responsive functions. It is interesting to note that, up-regulated proteins have more PPIs compared to down-regulated proteins. These PPI network helps to identify biomarkers and pathways of several human tumors. In order to understand human diseases, it is necessary to comprehend the network that forms these major biological processes, which are mediated by protein interactions. Although, both the PPI networks build from up and down-regulated proteins are important, up-regulated proteins have much more significance compared to down-regulated ones as up-regulated proteins interact with more human proteins. We have performed a case study on the PPI network of Gastric cancer dataset considering a threshold of 0.7. The up- and down-regulated proteins and their corresponding PPIs are given in supplementary files S1 and S2. We have performed KEGG pathway analysis using David (https://david.ncifcrf.gov/) on the proteins present in the up- and down-regulated PPI network separately and the results are shown in Tables 7 and 8. It can be noted that up-regulated PPI network is mainly involved in DNA replication, Alzheimer disease, Proteasome, Spinocerebellar ataxia, Ribosome, Amyotrophic lateral sclerosis, Mismatch repair etc., where the down-regulated PPI network exhibits Chemokine signaling pathway, Prion disease, Thyroid hormone signaling pathway, Diabetic cardiomyopathy, Human immunodeficiency virus 1 infection etc. In [9] the authors reveal that proteasome function suppression in gastric cancer cells causes apoptosis and these proteasomal inhibitors may be useful as novel anticancer medications in the treatment of gastric cancer. Otabor et al. described some cases patient with ataxia-telangiectasia who had a gastric adenocarcinoma that manifested as a total obstruction of the gastric outlet in [10]. Many studies had shown that Gastric cancer is linked to diabetes mellitus (DM), which has been deemed a risk factor [11].

Table 7

KEGG enrichment analysis for up-regulated PPI network

KEGG term	Count	p value
Proteasome	16	2.20E−13
DNA replication	12	8.70E−10
Cell cycle	19	2.90E−09
Spinocerebellar ataxia	18	1.30E−07
Alzheimer disease	29	5.10E−07
Ribosome	18	5.60E−07
Amyotrophic lateral sclerosis	28	6.00E−07
Mismatch repair	8	1.20E−06
Prion disease	23	1.80E−06
Human papillomavirus infection	25	4.00E−06
Pathways of neurodegeneration—multiple diseases	31	4.10E−06
Parkinson disease	22	4.40E−06
Spliceosome	16	5.20E−06
Viral carcinogenesis	18	1.90E−05
Coronavirus disease—COVID-19	19	2.80E−05

Table 8

KEGG enrichment analysis for down-regulated PPI network

KEGG term	Count	p value
Chemokine signaling pathway	13	4.60E−07
Prion disease	14	3.20E−06
Thyroid hormone signaling pathway	10	3.40E−06
Diabetic cardiomyopathy	12	5.80E−06
Human immunodeficiency virus 1 infection	12	8.80E−06
Serotonergic synapse	9	2.00E−05
Carbon metabolism	9	2.00E−05
MAPK signaling pathway	13	3.80E−05
AGE-RAGE signaling pathway in diabetic complications	8	6.50E−05

Apart from that, we have also calculated the degrees of the proteins present in up- and down-regulated PPI network with respect to HPRD database release 9. The Table 9 shows the top 20 hub proteins with their degree and regulation type. Apart from PPIs, identification of hub genes is one of the important use of the microarray datasets. The proteins with the highest degree of connectedness is referred to as the hub gene. Due to the hub genes’ significant connectivity within the disease network, they are involved in crucial biological processes and have high clinical importance. It can be noted that out of 20 hub proteins, 13 proteins are from up-regulated and 7 proteins are from down-regulated PPI network.

Table 9

The top 20 hub proteins with their degree and regulation

Regulation type	Protein name	Degree
Up	CREBBP	200
Up	CTNNB1	135
Down	CASP3	132
Up	YWHAB	126
Down	YWHAZ	124
Up	EWSR1	119
Down	MAPK3	119
Up	RELA	115
Down	LCK	107
Down	PRKCD	104
Up	ACTB	103
Up	HSP90AA1	92
Up	YWHAQ	79
Up	STAT1	78
Up	CDK2	76
Up	PCNA	76
Down	BCL2	76
Up	FN1	74
Down	PTK2B	71
UP	XRCC6	70

Conclusion

In this paper, we have conducted an integrative analysis of large-scale microarray gene expression to generate the PPIs of differentially expressed genes. The vast majority of differentially expressed genes are, however, down-regulated. This means that rather than switching on the expression of novel genes, the main route to malignancy is to toggle genes off. In spite of that, when the PPI network is generated from these genes, it has been seen that the up-regulated proteins have more PPIs compared to the down-regulated ones. Therefore, the majority of potential key genes and tumor pathways can be generated from the up-regulated proteins as they interact more with the other human proteins in the PPI network. As the main molecular targets for drugs are proteins, these up-regulated proteins will surely help biologists to create anti-cancer drugs.

Below is the link to the electronic supplementary material.

Supplementary file 1 (xlsx 209 KB)

Supplementary file 2 (xlsx 46 KB)