Researchers identify 10 genes that may help diagnose, track ALS 

Researchers identified changes in 10 genes that may help diagnose or track amyotrophic lateral sclerosis (ALS), according to a computer analysis of thousands of genes.

The study compared whole-blood samples from ALS patients and healthy individuals, uncovering nearly 1,000 genes with altered gene activity, along with related molecular pathways and potential drug candidates.

The study, “Identification of marker genes and signaling pathways associated with amyotrophic lateral sclerosis using bioinformatics analysis of RNA sequencing data,” was published in BMC Neurology.

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While the underlying cause of ALS remains largely unknown, several factors are thought to increase the risk of developing the neurodegenerative condition, including genetics, environmental exposures, and lifestyle habits.

Despite ongoing research, the underlying molecular mechanisms of ALS remain unclear in many cases, which has limited the development of effective treatments. In addition, few validated biomarkers can help identify those who are at risk of developing ALS, diagnose ALS, track disease progression, or assess treatment effectiveness.

To explore molecular mechanisms and identify potential biomarkers, researchers in India analyzed gene activity data from whole-blood samples of 42 people with ALS and 42 healthy controls. The data were examined using computer methods called bioinformatics.

The analysis identified 479 genes that were more active and 479 that were less active in ALS patients compared with controls.

The researchers then built a protein-protein interaction network, a map showing how proteins encoded by these genes interact with one another. This network contained 8,576 interconnected nodes (proteins) and 19,922 edges (interactions). From this network, using four different computer algorithms, the team identified 10 hub genes, or the most highly connected genes.

Two smaller, more tightly connected gene clusters, called modules, were also identified within the larger network. Module 1 was associated with the extracellular matrix, a network of proteins and molecules that provide cells with structure and support. Module 2 was linked to the nervous system and synapses, the connections that allow nerve cells to send signals to one another.

[The bioinformatics analysis] may promote the understanding of molecular mechanisms and clinically related molecular targets for prognosis in ALS and provide new insight into the occurrence and advancement of ALS.

The researchers then assessed whether the 10 hub genes could distinguish ALS patients from healthy controls using the area under the curve measure, with values above 0.8 indicating excellent diagnostic performance.

All ten hub genes (EGFR, FN1, CAV1, BCAR1, YAP1, POU5F1, TTYH1, ACTA1, GPR17, and TF) met this threshold.

Using the DrugBank database, the team identified drug molecules that could potentially target these genes. These included lormetazepam (insomnia), Tabrecta (capmatinib, used to treat lung cancer), Fycompa (perampanel, an anti-seizure medication), cabergoline (Parkinson’s disease), and medazepam (anxiety). Two dietary supplements were also noted: taurine and ferrous succinate, a form of iron.

The researchers also constructed additional networks connecting the hub genes to microRNAs (small molecules that can control gene activity) and transcription factors (proteins that regulate gene activity). Data showed that the microRNAs hsa-miR-6874-5p and hsa-miR-561-5p, along with the transcription factors GATA2 and SRF, may play important roles in ALS.

Among the study’s limitations, the team noted that the results were computer-generated and that the findings need to be confirmed in lab experiments.

The author concluded that their bioinformatics analysis “may promote the understanding of molecular mechanisms and clinically related molecular targets for prognosis in ALS and provide new insight into the occurrence and advancement of ALS.”