Table 11.

Potential biomarkers from multiomics studies in various MH-related disorders

Author name or study ID	Disorder	Molecular features/biological functions affected	Study summary/key findings	Source
Pang et al. [163]	Alzheimer’s disease (AD)	Measured genes and microRNAs expression, systems biology analysis	Identification of potential AD biomarkers, better understand AD pathogenesis	Entorhinal cortex, hippocampus and blood
Song et al. [178]	AD	Summarized studies and results based on the genome, transcriptome and epigenome, curated data into a database called AlzBase	Advancements towards candidate biomarkers and new hypotheses	Various
De Yager et al. [179]	AD	Multiomics analysis of the frontal cortex regions. The data had come from over 3000 patients that included 1179 samples from whole genome sequencing (WGS), 740 samples from DNA methylation, 712 samples from chromatin immunoprecipitation with sequencing (Chip-seq), 638 samples from RNA sequencing (RNA-seq) and 702 samples from microRNA expression profiling. The patients profiled were part of the Religious Orders Study (ROS) or the Rush Memory and Aging Project (MAP) [179, 180]	This dataset includes controls well and hence allows users to repurpose and offers opportunities for new findings	Various
Wang et al. [181]	AD	Generated WGS, whole exome sequencing (WES), RNA-seq and proteomic data from 258 AD brains along with clinical and pathophysiological data called the Mount Sinai cohort [181, 182]	This large-scale study of matched multiomics data in AD and control brains servers as an important resource for further analyses. The raw and processed data are publicly available.	Brain
Zhang et al. [105]	MDD	Studied the brains of chronic unpredictable mild stressed rat models by application of both metabolomics and proteomics. Significant changes were found in 30 metabolites and 170 proteins, related to these biological processes including impairment in amino acid metabolism and protein synthesis/degradation; dysregulation of glutamate and glycine metabolism; disturbances in fatty acid and glycerophospholipid metabolism; abnormal expression of synapse-associated proteins	Such multiomics studies could improve our understanding of the biology behind MDD and enable better treatments	Gas chromatography/mass spectrometry (GC–MS)
Narla et al. [106]	SCZ	Applied multiomics analysis including RNA-seq, microRNA and ChiPseq to find dysregulation of nuclear FGFR1 signaling in SCZ patients	Potential as a therapeutic target for SCZ	Plasmids expressing FGFR1 constructs and Human induced pluripotent stem cell lines, neuron committed cells
Goes et al. [156]	BD	Performed a large-scale meta-analysis using whole-exome sequencing (WES)and found three genes affected: MLK4, APPL2 and HSP90AA1	Identification of BD-affected genes	Various
Pineda-Cirera et al. [108]	ADHD	Studied genetic variation that influences brain methylation. They found that genetic variants for ADHD were correlated with higher gene expression and lower methylation of ARTN and PIDD1. On the other hand, Genetic variants for ADHD were correlated with a lower gene expression and higher methylation of C2orf82 [108]	Interplay of gene expression and methylation changes in ADHD	Brain
Hubers et al. [183]	ADHD	Performed an integrative analysis of genomics, epigenomics and metabolomics data from in 596 twins (cases and controls) from the Netherlands Twin Register (NTR) and looked for associations with ADHD. The top differentially changed features included TMEM, STAP2 and DNA methylation in MAD1L1 [183]	Identification of differentially changed features related to ADHD	Urine, buccal cell swabs
Nomura et al. [107]	ASD versus SCZ	Performed a multiomics analysis to compare ASD and SCZ and found the several biological processed affected in both disorders including neural development, synaptic dysfunctions and neural network. The authors also found chromatin modification process to be enriched only in ASD samples	Shared and distinct biological processes in ASD and SCZ	Various
Dean et al. [184]	PTSD	Studied warzone-related PTSD using multiomics technologies including genetics, DNA methylation, proteomics, metabolomics, immune cell counts, cell aging, endocrine markers, microRNAs and cytokines. They applied multistep ML models to identify candidate biomarkers for PTSD. At the end of their multi step analysis, 10 top performing candidate biomarkers were identified as most relevant to PTSD, including methylation markers cg01208318, cg20578780, cg15687973 (PDE9A) and 75,938,326 C2orf3; microRNA markers hsa-mir-133a-1-3p, hsa-mir-192-5p, hsa-miR-9-1-5p, metabolite marker gamma glutamyltyrosine; clinical labs insulin and mean platelet volume and physiological marker heart rate	Identification of top-performing candidate biomarkers for PTSD	Blood