CCL2 gene ID’d as common biomarker in SSc, IPF in new study

Researchers used machine learning algorithms to analyze data
by Margarida Maia, PhD | February 4, 2025

​The CCL2 gene, which codes for a signaling protein of the same name, is “a common characteristic gene” of both systemic sclerosis (SSc) and idiopathic pulmonary fibrosis (IPF), a serious complication of SSc that causes the lungs to become inflamed and scarred, according to a study by researchers in China.

“We identified CCL2 as a common biomarker from IPF and SSc, revealing the common mechanism of these two diseases,” the researchers wrote in “Common biomarkers of idiopathic pulmonary fibrosis and systemic sclerosis based on WGCNA and machine learning,” published in the journal Scientific Reports.

According to the researchers, their study provides “clues for the study of the treatment and mechanism of these two diseases.” However, these findings need to be confirmed by lab and clinical testing, given that the study “used data from a public database and lacked support from clinical data,” in addition to other limitations, the team noted.
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The researchers used machine learning algorithms, a type of artificial intelligence, to analyze the data from that database.

CCL2 gene shown to play a role in SSc, IPF disease development
Interstitial lung disease is a serious complication of SSc, also known as scleroderma — a condition in which the connective tissue that holds the body’s structures together hardens and becomes stiff. IPF is the most common type of interstitial lung disease, but its exact causes remain unclear.

To find out if IPF overlaps with SSc, the researchers used bioinformatics — the science of collecting and analyzing complex biological data — to compare sets of genetic data and identify genes that are common to the molecular mechanisms of the two diseases. The data came from the Gene Expression Omnibus public database.

The sets included genetic data from 339 skin samples of 113 people with SSc and 44 healthy individuals (GSE181549). It also had samples from bronchoalveolar lavage, which collects fluid from deep in the lungs, of 212 people with IPF and 20 healthy individuals (GSE70866).

A total of 138 overlapping genes were identified by weighted correlation network analysis, a type of bioinformatics analysis that groups genes that work together in networks. The list was reduced to the 10 highest-scoring genes, which play central roles in those networks. All 10 genes are predictors of survival in IPF.

To further narrow down the list of genes, the researchers made use of machine learning, a branch of artificial intelligence that enables a computer to learn from the data it is given. Machine learning algorithms identified CCL2 as being strongly linked to the diagnosis of both SSc and IPF.

The CCL2 gene, which had been identified as a possible biomarker of SSc in a previous bioinformatics analysis, provides instructions for producing a signaling protein that recruits immune cells to sites of inflammation. Other genes that were more active along with CCL2 were also involved in immune signaling or fat metabolism.
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Genetic data from another set (GSE32537), which included lung samples from 167 people with idiopathic interstitial pneumonia, diffuse interstitial lung diseases of unknown cause, revealed that higher levels of CCL2 were linked to poorer lung function in people with IPF.

Mycophenolate mofetil or MMF is an immunosuppressant that’s sometimes used to treat SSc with interstitial lung disease. Analysis of yet another dataset (GSE97248) revealed lower levels of CCL2 after three months of treatment with MMF in people with diffuse SSc. That subtype of scleroderma increases the risk of damage to the body’s internal organs.

“The CCL2 gene was identified as an important biomarker that plays a key role in the pathogenesis [disease development] of IPF and SSc,” the team wrote.

According to the researchers, CCL2 is possibly “involved in the vicious cycle of skin fibrosis [scarring] in SSc,” suggesting the signaling protein may be a new target for treatment.