Bioinformatics of Brain Diseases

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Darmanis and colleagues performed single cell RNA-seq on tumor core and

surrounding tissue of four patients with GBM [68]. A total of 3,589 cells were

analyzed. In the tissue encircling the GBM tumor core, they were able to iden-

tify and characterize specific invading tumor cells. Additionally, they inferred

minor genomic variants like insertions or deletions as well as genomic vari-

ation at the level of severe chromosomal abnormalities. Wan and colleagues

examined the tumor immune environment of glioma and normal tissue sam-

ples using both RNA-seq and single cell RNA-seq [69]. The high necroptosis-

related signature glioma patient group had a poor prognosis and a significant

involvement of immunosuppressive cells, according to their research. Addition-

ally, glioma showed elevated expression of the necroptosis suppressor CASP8,

which was linked to a bad prognosis.

Brastionas and colleagues analyzed meningioma tissue samples using whole

genome and whole exome sequencing [70]. AKT1 and SMO mutations that

are frequently oncogenic were found in a fraction of meningiomas that did not

have NF2 changes, and these meningiomas also showed immunohistochem-

istry evidence that their pathways had been activated. Using RNA-seq, Abe-

dalthagafi and colleagues found that PI3K mutations were similarly frequent

in meningiomas in a different study [71].

Zhou and collegues analyzed tissue samples of primary CNSL patients

using next generation sequencing [72]. According to their study, recurring al-

terations in the NF-B pathway’s KMT2D and CD79B components comprised

65 % of all mutations in PCNSL cases. In another RNA-seq study, Zhang

et al. analyzed tumor and adjacent normal tissues and PBMCs of Chinese

primary PCNSL patients using whole exome sequencing [73]. They revealed

that MYD88 had the highest alteration rate, which had an impact on the

NF-B pathway’s activity. Furthermore, compared to samples with wild-type

LRP1B, PCNSL samples with LRP1B mutations exhibited a greater mutation

rate.

RNA-seq studies on AD are also in rise. Guennewig and colleagues used

RNA-seq to uncover differentially elevated genes in post-mortem brains from

AD patients and healthy controls in the primary visual cortex and precuneus

[74]. Shigemizu and collegues performed analysis using whole genome sequenc-

ing data using blood of AD patients and their healthy counterparts [75]. They

discovered a missense mutation in OR51G1 and a stop-gain variant in MLKL

as potential candidates for AD link. Furthermore, through gene-based associ-

ation analyses of uncommon variations, they also discovered additional can-

didate genes for AD. Using these candidates, they identified NCOR2, PLEC,

DMD, and NEDD4 as functionally significant hub genes.

Eitan and colleagues recently carried out a whole genome sequencing inves-

tigation using microglia of ALS patients and their healthy counterparts [76].

Their findings highlight the value of non-coding genetic association studies

and showed that different genetic variations protect against ALS via lowering

neuroinflammation. In another study Brusati and coworkers performed whole

genome sequencing on patients with ALS [77]. They detected 86 uncommon