Gene Panel, method, device and application for detecting somatic variation of SLC35A2 gene

Abstract

The invention relates to the technical field of gene detection, in particular to a gene Panel for detecting somatic variation of an SLC35A2 gene as well as a method, a device and application of the gene Panel. The invention provides a gene Panel, which can capture a whole SLC35A2 gene related region in a targeted manner, comprises an exon, an intron, a promoter and an enhancer, and performs somatic variation detection, and the detected variation comprises SNVs, small Indels and SVs. When the Panel is used for detecting intractable epilepsy surgical brain tissue specimens, mild cortical development malformation with oligodendrocyte hyperplasia epilepsy (MOGHE) can be genetically diagnosed, and the disease is a new subtype which is newest brought into focal cortical development malformation by international anti-epilepsy alliance in 2022. The Panel variation detection rate is high, and variation detection types are more comprehensive.

Description

Technical Field

[0001] The present invention relates to the technical field of gene detection, and particularly relates to a gene Panel, a method, a device and uses for detecting somatic variations of the SLC35A2 gene. Background Art

[0002] Cortical dysplasia is the most common cause of childhood cortical epilepsy seizures and is usually insensitive to anti-epileptic drugs. In recent years, a new histopathological entity of cortical dysplasia, mild oligodendrogliosis with cortical dysplasia (MOGHE), has been recognized. This subtype was first characterized and described histopathologically in 2017 and was officially recognized as a new subtype in the FCD classification by the International League Against Epilepsy (ILAE) in 2022. Patients with MOGHE usually present in infancy and early childhood, with frequent epileptic seizures, mostly spastic seizures, accompanied by moderate or severe late-onset epilepsy, and the lesions often involve the frontal lobe. MRI examination shows blurred gray-white matter junction, cortical thickening, and increased white matter T2 / T2-FLAIR signals, which are helpful for early identification of MOGHE. However, the diagnostic criterion is still based on surgical pathological diagnosis, and its main features are abnormally increased oligodendrocytes and ectopic neurons in the white matter. Current studies have found that somatic variations of the SLC35A2 gene exist in the brain tissue of MOGHE lesions. SLC35A2 encodes UDP-galactose transporter protein, which is crucial for the processes of protein and sphingolipid glycosylation.

[0003] In previous studies, the SLC35A2 variations detected by whole exome sequencing (WES) or targeted gene exon sequencing mainly concentrated in the exon region or its adjacent regions, presenting as single nucleotide variations (SNVs) or small insertions and deletions (Indels). However, it is not yet clear whether other types of variations, such as variations affecting splicing in deep intron regions, regulatory region variations, or structural variations (SVs) affecting the integrity of SLC35A2, also contribute to MOGHE. Further research, extending to these broader genomic regions and variation types, may reveal these potential variations and provide a more comprehensive understanding of the genetic background of MOGHE. If a more comprehensive detection method can be developed, it will help to deeply understand this gene, improve the detection rate of somatic variations, thus playing a role in preoperative and pathological evaluations, and having an important impact on subsequent gene precision therapy and the optimization of pathological diagnostic criteria. Summary of the Invention

[0004] Therefore, the technical problem to be solved by the present invention is to provide a gene panel, a method, a device and a use for detecting somatic mutations of the SLC35A2 gene, a gene detection panel for high-depth sequencing of SLC35A2, which can comprehensively detect somatic mutations on the SLC35A2 gene, with high detection rate and more comprehensiveness.

[0005] For this purpose, the present invention provides the following technical solutions:

[0006] The present invention provides a gene panel, and the genomic regions captured by the gene panel are selected from at least one of the following tables:

[0007]

[0008]

[0009] Optionally, the genomic region coordinates of the above gene panel refer to GRCh38 / hg38.

[0010] The present invention provides a probe set, which is a probe designed for the above gene panel.

[0011] The present invention provides a kit, which includes the above gene panel or the above probe set.

[0012] Use of the above gene panel, the above probe set or the above kit in detecting somatic mutations of the SLC35A2 gene or in preparing a product for detecting somatic mutations of the SLC35A2 gene.

[0013] The somatic mutations of the SLC35A2 gene include, but are not limited to, single nucleotide variations, insertion variations and / or deletion variations.

[0014] The present invention provides a method for detecting somatic mutations of the SLC35A2 gene, including:

[0015] S1. Obtain genomic DNA of a sample to be tested;

[0016] S2. Construct a library based on the genomic DNA;

[0017] S3. Use the above gene panel, the above probe set or the above kit to perform target region capture on the library;

[0018] S4. Sequence the captured target region, then perform data quality control, align with the reference genome, and perform variant detection.

[0019] Optionally, the sample to be tested includes brain tissue or blood.

[0020] Optionally, the sequencing platform includes the Illumina next-generation sequencing platform and the BGI next-generation sequencing platform.

[0021] Optionally, when the sample to be tested is not a blood sample, the sequencing depth is ≥1000x, generating ≥0.15 Gb of raw data;

[0022] Or, when the sample to be tested is a brain tissue sample, the sequencing depth is ≥3000x, generating ≥0.45 Gb of raw data.

[0023] Optionally, in the data quality control, Fastqc (v0.11.3) and Trimmomatic (v0.36) are selected to filter low-quality reads and bases.

[0024] Optionally, the low-quality reads and bases include at least one of the following: sequencing adapter sequences; sequences with an average base quality threshold lower than 15 within a 4-base-length sliding window; reads with a length lower than 60 bp after removing the sequencing adapter sequences and low-quality bases.

[0025] Optionally, Fastqc (v0.11.3) is selected for Fastqc; Trimmomatic (v0.36) is selected for Trimmomatic.

[0026] Optionally, in the variant detection, somatic cell variants are detected based on GATK v4.5 Mutect2, including SNVs and Indels; somatic structural variants are detected based on Manta. The SNVs refer to single nucleotide variants, and the Indels refer to insertion and deletion variants.

[0027] The SNVs and Indels variants are filtered to remove false-positive variants caused by sequencing strand orientation bias and sample contamination. The method used is FilterMutectCalls in the GATK v4.5 toolkit, where the filtering conditions are generated by the CreateSomaticPanelOfNormals, LearnReadOrientationModel, GetPileupSummaries, and CalculateContamination tools in the GATK v4.5 toolkit.

[0028] Optionally, somatic cell variants are detected based on GATK v4.5 Mutect2, including SNVs and Indels; somatic structural variants are detected based on Manta.

[0029] Optionally, filter the SNVs and Indels mutations among them, and filter out false-positive mutations caused by sequencing strand orientation bias and sample contamination. The method used is FilterMutectCalls in the GATK v4.5 toolkit, and the filtering conditions are generated by the CreateSomaticPanelOfNormals, LearnReadOrientationModel, GetPileupSummaries, and CalculateContamination tools in the GATK v4.5 toolkit.

[0030] The present invention provides a device for detecting somatic mutations of the SLC35A2 gene, comprising:

[0031] A library construction unit for constructing a library of the DNA genome of a sample to be tested;

[0032] A target region capture unit for capturing a target region of the library using the gene Panel, the probe set, or the kit;

[0033] A sequencing unit for sequencing the obtained target region;

[0034] A data quality control unit for performing quality control on the sequencing data;

[0035] An alignment unit for aligning the reads after data quality control with a reference genome;

[0036] A mutation detection unit for detecting mutations in the aligned data.

[0037] A computer-readable medium storing a program that can run the method for detecting somatic mutations of the SLC35A2 gene.

[0038] The technical solution of the present invention has the following advantages:

[0039] 1. The present invention provides a gene Panel, which is selected from at least one of the following gene fragments; the above gene Panel is a high-depth sequencing gene detection panel for SLC35A2, which can comprehensively detect somatic mutations on the SLC35A2 gene. The goal is to cover the entire SLC35A2 gene, including exons, introns, promoters, and enhancers. The panel contains the full length of the gene and non-coding regulatory regions, and can comprehensively analyze somatic SNV and Indel mutations that may affect the function and expression of the SLC35A2 gene. At the same time, it also has the ability to identify relevant somatic SVs, can perform somatic mutation detection on the SLC35A2 gene from a genetic perspective, and has a higher detection rate and a wider coverage range of mutation types.

[0040] Furthermore, the SLC35A2 gene panel of the present invention is small, economical, and efficient. Due to the small targeted region of the gene panel, even with a sequencing depth of more than 3000×, its sequencing cost is much lower than that of ordinary 100× WES; the original sequencing data is also smaller and more uniform, facilitating analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0042] Figure 1 Sanger verification of three GAG / CTC deletions in sample PP41, one of the previously WES-negative samples in Example 7, and three samples NP53, NP63, and NP66 of newly diagnosed MOGHE patients in Example 6.

[0043] Figure 2 Gel electrophoresis results of the PCR amplification fragment of the 3233bp deletion at chrX:48909151 - 48912383 of patient PP34 in 28 corresponding samples in Example 5;

[0044] Figure 3 Results obtained after sequencing the PCR product amplified for 3233bp in Example 5;

[0045] Figure 4 Visualization results of the structural variation genome in Example 5; Mapping diagram of the 3233bp structural variant (SV), sequence visualization diagram of the sequence around the SV, including the comparison of the panel sequencing of the present invention, the existing WES sequencing, and Sanger sequencing;

[0046] Figure 5 Genomic location of the 3233bp deletion SV and surrounding repetitive sequences in Example 5; Genomic localization of the SV and its nearby repetitive sequences, and primers F1R1 and F2R2 are used to amplify this region for SV verification;

[0047] Figure 6 Comparison of clinical data of positive and negative patients in 25 samples detected by previous WES in Example 7;

[0048] Figure 7 Comparison of clinical data of positive and negative patients in 25 samples detected by the gene panel of the present invention in Example 7;

[0049] Figure 8 Comparison of clinical data of positive and negative patients in 47 samples detected by the gene panel of the present invention in Example 7;

[0050] Figure 9 Flow chart of the detection method for somatic variants of the SLC35A2 gene in Example 4. Detailed implementation manners

[0051] The following embodiments are provided to better further understand the present invention. They are not limited to the described optimal implementation manner and do not limit the content and protection scope of the present invention. Any product identical or similar to the present invention obtained by anyone under the inspiration of the present invention or by combining the features of the present invention with those of other existing technologies falls within the protection scope of the present invention.

[0052] For those embodiments where specific experimental steps or conditions are not indicated, the operations or conditions of the conventional experimental steps described in the literature in this field can be followed. For reagents or instruments whose manufacturers are not indicated, they are all conventional reagent products that can be obtained through commercial purchase.

[0053] The present invention provides a gene panel, and the probe capture target region of the gene panel is the following interval of the genome:

[0054]

[0055] Optionally, the genomic interval coordinates of the above gene panel refer to GRCh38 / hg38, which includes the full length of the SLC35A2 gene and its upstream promoter, as well as the enhancer region of the SLC35A2 gene on the genome.

[0056] The present invention provides a probe set, which is a probe designed for the above gene panel.

[0057] The present invention provides a kit, which includes the above gene panel or the above probe set.

[0058] Use of the above gene panel, the above probe set or the above kit in detecting somatic variants of the SLC35A2 gene or in preparing a product for detecting somatic variants of the SLC35A2 gene.

[0059] In some implementation manners, the detection of somatic variants of the SLC35A2 gene can be used to detect somatic variants of the SLC35A2 gene in MOGHE patients. Detecting somatic variants of the SLC35A2 gene in MOGHE patients can play a role in pathological evaluation, including but not limited to objectively evaluating gene variants in surgical samples, providing reference and guidance for pathological typing, and improving the accuracy of pathological typing.

[0060] In some embodiments, detecting somatic variations of the SLC35A2 gene can guide the use of anti-epileptic drugs for MOGHE patients with somatic variations of the SLC35A2 gene after surgery. If the patient has a recurrence, it can guide subsequent precision treatment plans.

[0061] The somatic variations of the SLC35A2 gene include, but are not limited to, single nucleotide variations, insertion variations, and / or deletion variations.

[0062] Single nucleotide variation: A single nucleotide variant (SNV) refers to the variation of a single nucleotide (A, T, C, or G) at a certain position in the genome. It is one of the most common types of variations in the genome and usually manifests as a base substitution at a specific position.

[0063] Insertion variation: An insertion variant refers to the addition of extra nucleotides or nucleotide sequences in the DNA sequence, which changes the original structure of the gene. Insertion variations can involve a single nucleotide or a segment of nucleotide sequences.

[0064] Deletion variation: A deletion variant refers to the loss or deletion of nucleotides or nucleotide sequences at a certain position in the genome. This type of variation can involve one or more base pairs and may even involve longer genes or gene regions.

[0065] The present invention provides a method for detecting somatic variations of the SLC35A2 gene, including:

[0066] S1. Obtain genomic DNA of the sample to be tested;

[0067] S2. Construct a library based on the genomic DNA;

[0068] S3. Use the gene Panel, the probe set, or the kit to perform target region capture on the library;

[0069] S4. Sequence the captured target region, then perform data quality control, align with the reference genome, and perform variant detection.

[0070] In some embodiments, the sample to be tested includes brain tissue or blood.

[0071] In some embodiments, the sequencing platform includes, but is not limited to, the Illumina next-generation sequencing platform and the BGI next-generation sequencing platform.

[0072] In some embodiments, when the sample to be tested is a blood sample, the sequencing depth is ≥1000x, generating ≥0.15 Gb of raw data.

[0073] In some embodiments, when the sample to be tested is a brain tissue sample, the sequencing depth is ≥3000x, generating ≥0.45 Gb of raw data.

[0074] In some embodiments, Fastqc (v0.11.3) and Trimmomatic (v0.36) are selected in the data quality control to filter low-quality reads and bases.

[0075] In some embodiments, the low-quality reads and bases include at least one of the following: sequencing adapter sequences; sequences with an average base quality threshold lower than 15 within a 4-base length sliding window; reads with a length lower than 60 bp after removing sequencing adapter sequences and low-quality bases.

[0076] In some embodiments, Fastqc (v0.11.3) is selected for Fastqc; Trimmomatic (v0.36) is selected for Trimmomatic.

[0077] In some embodiments, in the variant detection, small somatic variant detection including SNVs and Indels is performed based on GATK v4.5 Mutect2; structural variant somatic detection is performed based on Manta. The SNVs refer to single nucleotide variants, and the Indels refer to insertion and deletion variants.

[0078] The SNVs and Indels variants are filtered to remove false positive variants caused by sequencing strand orientation bias and sample contamination. The method used is FilterMutectCalls in the GATK v4.5 toolkit, where the filtering conditions are generated by the CreateSomaticPanelOfNormals, LearnReadOrientationModel, GetPileupSummaries, and CalculateContamination tools in the GATK v4.5 toolkit.

[0079] In some embodiments, small somatic variant detection including SNVs and Indels is performed based on GATK v4.5 Mutect2; structural variant somatic detection is performed based on Manta.

[0080] In some embodiments, SNVs and Indels mutations are filtered to remove false positive mutations caused by sequencing strand orientation bias and sample contamination. The method used is FilterMutectCalls in the GATK v4.5 toolkit, where the filtering conditions are generated by the CreateSomaticPanelOfNormals, LearnReadOrientationModel, GetPileupSummaries, and CalculateContamination tools in the GATK v4.5 toolkit.

[0081] The present invention provides a device for detecting somatic mutations of the SLC35A2 gene, comprising:

[0082] A library construction unit for constructing a library from the DNA genome of a sample to be tested;

[0083] A target region capture unit for capturing the target region of the library using the gene Panel, the probe set, or the kit;

[0084] A sequencing unit for sequencing the obtained target region;

[0085] A data quality control unit for performing quality control on the sequencing data;

[0086] An alignment unit for aligning the reads after data quality control with the reference genome;

[0087] A mutation detection unit for detecting mutations in the aligned data.

[0088] A computer-readable medium storing a program that can run the method for detecting somatic mutations of the SLC35A2 gene.

[0089] Example 1 Design of Gene Panel

[0090] The present invention's research findings show that intronic mutations of genes can affect splicing, mutations in the promoter and enhancer regions of genes may also regulate gene expression, and structural variations with breakpoints in introns or promoter regions also require full-length coverage sequencing. Therefore, in this example, a full-coverage SLC35A2 gene panel including the full length of the gene and its promoter and enhancer is designed.

[0091] This example combined all transcripts of the SLC35A2 gene and used the farthest start and end sites to define its complete gene body. The 1 kb region upstream of the start site was designated as the promoter region. For the enhancer region, enhancer-gene interaction data for human ESC_neuron, Astrocyte, Mesendoderm, Fetal_spinal_cord, H1, H9, and GM12878 cell lines were downloaded from EnhancerAtlas 2.0, and tissues or cell lines related to the brain or stem cells were selected. The enhancer regions of SLC35A2 were extracted from these interaction files, and intervals with a distance less than 500 bp were merged. The resulting continuous intervals were classified as enhancers, and a total of 30 enhancers were generated. Finally, the target regions of the SLC35A2 full-region panel included an 8.4 kb gene body, a 1.0 kb promoter, and a 40.3 kb enhancer. The final gene panel is shown in the following table.

[0092] Table 1. Gene panel of SLC35A2 full region (GRCh38 / hg38)

[0093]

[0094]

[0095] Example 2 Probe set

[0096] This example provides a probe set for target region capture, which is designed for the gene Panel described in Example 1 and is designed and prepared by conventional methods in the art.

[0097] Example 3 Kit

[0098] This example provides a kit, including the gene Panel described in Example 1 and the probe set described in Example 2.

[0099] Example 4 Method for detecting somatic mutations of the SLC35A2 gene

[0100] This example provides a method for detecting somatic mutations of the SLC35A2 gene. The main process is as Figure 9 shown and includes the following steps:

[0101] (1) Obtain genomic DNA of the sample to be tested:

[0102] Extract genomic DNA from the excised brain tissue sample or blood sample according to conventional methods.

[0103] (2) Library construction:

[0104] Take 200 ng of the genomic DNA, fragment the genomic DNA, perform end repair, and ligate it to an Illumina adapter using EnzymePlus library Prep Kit V3 (iGeneTech, Beijing, China) to obtain a library.

[0105] (3) Sequencing

[0106] Use the probe set of Example 2 to perform target region capture on the library in step (2), and sequence the captured target region on an Illumina platform (Illumina, San Diego, CA) with a paired-end read length of 150 bp. To achieve the required sequencing depth, the sequencing depth of the blood sample is ≥1000x, generating ≥0.15 Gb of raw data, and the sequencing depth of the brain sample is ≥3000x, generating ≥0.45 Gb of raw data.

[0107] (4) Data quality control

[0108] Use Fastqc (v0.11.3) and trimmomatic (v0.36) to filter low-quality reads and bases. The filtered low-quality reads and bases include: sequencing adapter sequences; sequences with an average base quality threshold lower than 15 within a 4-base length sliding window; reads with a length less than 60 bp after removing the sequencing adapter sequences and low-quality bases. The filtering conditions are: ILLUMINACLIP:TruSeq3-PE-2.fa:2:30:10:1:true LEADING:3TRAILING:3SLIDINGWINDOW:4:15MINLEN:60.

[0109] (5) Data alignment

[0110] Use BWA to align the data after data quality control with the GRCh38 reference genome, and use markduplduplicate (GATK v4.5) to mark duplicate reads.

[0111] (6) Variant detection

[0112] For somatic short variants (single nucleotide variants (SNVs) and small insertions / deletions (indels)), Mutect2 is used for variant identification, and additional tools in GATK v4.5 are used for preprocessing, filtering, and annotation. The variant identification step: Use the CreateSomaticPanelOfNormals tool to identify variants in blood samples (when both blood and tissue samples are available) in tumor-only mode, and merge the variants in all blood samples into the control VCF file (pon.vcf.gz). Mutect2 performs somatic variant detection on paired brain-blood BAM files (if paired blood samples are available) or brain-only BAM files, and uses pon.vcf.gz as input. The preprocessing steps include using LearnReadOrientationModel to learn orientation bias, and using GetPileupSummaries and CalculateContamination tools to calculate the contamination level. The FilterMutectCalls tool is used to filter the raw variants detected by Mutect2, and the files obtained in the preprocessing steps are used as input parameters during the call. The specific parameters include: --orientation-bias-artifact-priors, --contamination-table, --tumor-segmentation. Funcotator is used to annotate the filtered somatic variants and evaluate their pathogenicity according to the ACMG guidelines. Depending on whether paired blood samples are available, Manta is used to identify structural variants (SVs) in tumor-normal (paired samples (when both blood and brain tissue samples are available)) or tumor-only (brain tissue samples only) mode.

[0113] Example 5 Application of a gene panel for detecting somatic variants of the SLC35A2 gene

[0114] Samples of 28 MOGHE patients before 2022 (provided by the Epilepsy Center of Peking University First Hospital) were selected for whole exome sequencing (WES) (depth: 300× for brain tissue, 100× for blood). Pathogenic somatic variants of SLC35A2 were identified in 10 of these patients, and the rest were identified as negative samples. The research of the present invention found that due to the limited sequencing depth and the absence of introns and the SLC35A2 regulatory region, there may be missed variant detections.

[0115] To test the performance and effect of the gene panel of the present invention, 2 samples with pathogenic somatic mutations of SLC35A2 identified by the above-mentioned WES and 15 negative samples identified as negative by WES were selected and detected according to the method in Example 4 (the samples were brain tissue samples).

[0116] The results were as follows: The results of detecting 2 positive samples using the gene panel of the present invention were consistent with those of WES, demonstrating the reproducibility of gene panel detection in positive samples. Among the negative samples, 4 somatic mutations of SLC35A2 were detected using the gene panel of the present invention, including a 3.2 kb deletion with the breakpoint located in the non-coding region in 1 case and 3 SNV / Indels. All 4 of these variations were confirmed. The 3.2 kb deletion was found to have 23.3% paired reads (197 / 845) and 11.7% split reads (222 / 1893) supporting the variation in the panel detection ( Figure 4 , 5), and later the variation was proven to exist based on the gel electrophoresis of the PCR amplified fragment and the first-generation sequencing of the fragment ( Figure 2 , 3). A 3-base deletion variation was verified by first-generation sequencing ( Figure 1 PP41). Looking back at the bam files after aligning the genomes of the WES data for the other 2 mutations, it was found that the variations were detected by a small number of reads, 0.6% (4 / 611) and 0.9% (3 / 336) of the data respectively, and were not correctly identified as variations based on the WES data variation identification because the detected reads were too few. This shows that the gene panel detection of the present invention can discover small variations and structural variations that may be missed.

[0117] The above shows that the method for detecting SLC35A2 somatic mutations based on the gene panel of the present invention can supplement and discover samples missed by WES. The positive rate of SLC35A2 somatic mutations in the original 28 MOGHE samples increased from 35.7% (10 / 28) to 50% (14 / 28).

[0118] Example 6 Variant Detection of Recently Diagnosed MOGHE Brain Tissue Samples Based on the SLC35A2 Panel

[0119] Twenty brain tissue samples of MOGHE diagnosed from January 2023 to December 2024 (provided by the Epilepsy Center of Peking University First Hospital) were subjected to high-depth sequencing and variant identification based on the gene panel of the present invention according to the method in Example 4. The positive proportion of SLC35A2 somatic mutations in the samples was statistically analyzed, and the gene detection rate of the panel for MOGHE patients was also statistically analyzed. At the same time, sanger verification was performed on three GAG / CTC deletions in three samples, NP53, NP63, and NP66 (seeFigure 1 , NP53, NP63, NP66).

[0120] The results showed that the detection rate of somatic mutations in SLC35A2 in 20 samples was 15 / 20 (75%). This mutation rate was significantly high, exceeding the results of the [1] MOGHE study (45%, 9 / 20) and our early MOGHE WES study (35.7%, 10 / 28) in previous literature. At the same time, the accuracy of the gene Panel detection of the present invention was verified by Sanger sequencing of three GAG / CTC deletion mutations that were not common in previous reports (see Figure 1 , NP53, NP63, NP66).

[0121] Example 7 Clinical Application

[0122] Forty-seven brain tissues of MOGHE that had been diagnosed (provided by the Epilepsy Center of Peking University First Hospital) were reserved.

[0123] For 25 of these samples, variant identification was performed, and the positive and negative patients with somatic mutations in SLC35A2 in the samples were counted.

[0124] The results of WES detection were as follows: The samples for clinical analysis included 10 patients with somatic mutations in SLC35A2 and 15 patients without somatic mutations in SLC35A2. According to the WES detection results, the clinical characteristics of the detected positive and negative patients were compared. The results showed that patients with somatic mutations in SLC35A2 showed more extensive interictal electroencephalogram discharges (P = 0.015, P < 0.05) ( Figure 6 ).

[0125] Based on the gene Panel of the present invention, high-depth sequencing and variant identification were performed according to the method in Example 4, and the positive and negative patients with somatic mutations in SLC35A2 in the samples were counted. The results were as follows: When the same samples were tested by the panel, 4 new positive patients were detected. The Sanger method existing was used to verify the somatic mutations in SLC35A2 in the above samples, and the verification results were consistent with the detection results of the present invention. Clinical data analysis was performed on 14 patients with somatic mutations in SLC35A2 and 11 patients without somatic mutations in SLC35A2. The results showed that patients with somatic mutations in SLC35A2 showed more extensive interictal electroencephalogram discharges (P = 0.015, P < 0.05) ( Figure 7 ), and although the incidence of more extensive multi-lobar lesions on cranial MRI in the forest plot had no statistical difference with P = 0.194, P > 0.05, it was related to Figure 6In contrast, more extensive multi-lobar brain lesions on cranial MRI were more likely to be found in positive patients. It may be due to the limitation of too small a sample size. Therefore, we subsequently compared the clinical data of a larger sample.

[0126] For 47 samples, based on the gene Panel of the present invention, high-depth sequencing and variant identification were performed according to the method in Example 4, and the positive and negative patients with somatic variants of SLC35A2 in the samples were counted.

[0127] The test results showed that the samples analyzed clinically included 29 patients with somatic variants of SLC35A2 and 18 patients without somatic variants of SLC35A2. The existing Sanger method was used to verify the somatic variants of SLC35A2 in the above samples, and the verification results were consistent with those of the present invention.

[0128] Subsequently, this example compared the clinical feature differences between the positive and negative patients detected to determine potential differences in other genetic causes that may exist in patients without detected SLC35A2 variants. The basic information of gender and age, age of onset of epilepsy, seizure type, seizure frequency, cranial MRI, electroencephalogram, surgery and postoperative results of patients with SLC35A2 variants and non-SLC35A2 variants were collected and compared. The information of some samples is shown in Figure 8。It was found that patients with somatic mutations in SLC35A2 showed more extensive interictal electroencephalogram discharges (P = 0.014, P < 0.05) and a higher incidence of more extensive multi-lobar lesions on cranial MRI (P = 0.035, P < 0.05), indicating that the brains of patients with somatic mutations in SLC35A2 were more extensively damaged. In clinical practice, for patients with drug-resistant epilepsy who require surgery, the preoperative assessment of the lesion range and surgical methods mainly rely on the clinical data of the patients. The most important information includes the location and range of epileptic wave discharges on the electroencephalogram of the patients, the location and range of lesions on cranial MRI, and the location and range of lesions on cranial PET / CT. This information helps the surgeon to pre-judge the surgical methods required for the patient and the location of the lesion to be removed. For example, if the patient's lesion is relatively limited and in a non-functional area, a lesion resection surgery is considered. If the lesion range is large and involves multiple brain regions or functional areas, a lobectomy is considered for surgery [2]. Whether the surgical method is correctly selected and whether the lesion is completely removed affect whether the patient can recover normally or still have epileptic seizures after surgery. If the surgical lesion is not completely removed, there is a possibility of secondary surgery, increasing the patient's medical expenses and damaging the patient's health. In our study, it was found that the brain lesions in patients positive for SLC35A2 were more extensive (i.e., extensive interictal electroencephalogram discharges and abnormal cranial MRI). Therefore, for subsequent patients who are found to clinically meet the MOGHE characteristics during preoperative evaluation, and those with extensive electroencephalogram discharges and a wide range of multi-lobar lesions on cranial MRI, a lobectomy is recommended instead of a lesion resection surgery. For patients who test positive for SLC35A2 mutations after surgery and still have epileptic seizures, precision treatment targeting this gene can be carried out to help the doctor judge the subsequent treatment direction and content. These findings enhance the understanding of the molecular and clinical landscape of MOGHE and help clinicians and researchers manage this newly emerging pathological entity.

[0129] Furthermore, as can be seen from the foregoing, by using the gene Panel of the present invention, a wider range of genomic regions and mutation types can be detected, such as mutations in introns. As the number of samples increases, the detection rate of positive samples is higher, indicating that the gene Panel of the present invention is suitable for large-scale sample detection. In addition, by using the gene Panel of the present invention for detection, there is no need to perform whole-genome detection, greatly reducing the cost.

[0130] Obviously, the above embodiments are merely examples for clear illustration and not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. And the obvious changes or modifications derived therefrom still fall within the protection scope of the present invention.

[0131] 1. Bonduelle T, Hartlieb T, Baldassari S, Sim NS, Kim SH, Kang H-C, et al. Frequent SLC35A2 brain mosaicism in mild malformation of cortical development with oligodendroglial hyperplasia in epilepsy (MOGHE). Acta Neuropathol Commun [Internet]. 2021;9:3. Available from: https: / / actaneurocomms.biomedcentral.com / articles / 10.1186 / s40478-020-01085-3

[0132] 2. Wang Y, Liu Q, Yu H, Liu C, Sun Y, Wang Y, et al. Frontal Disconnection for Treating Mild Malformation of Cortical Development with Oligodendroglial Hyperplasia in Epilepsy (MOGHE) in the Frontal Lobe. J Vis Exp [Internet]. 2024; Available from: https: / / app.jove.com / t / 66970 / frontal-disconnection-for-treating-mild-malformati on-cortical。

Claims

1. A gene Panel, characterized in that, The probe capture target region of the gene Panel is the following genomic intervals: 。 2. A probe set, characterized in that, Probes designed for the gene Panel described in claim 1.

3. A kit, characterized in that, Comprising the gene Panel described in claim 1 or the probe set described in claim 2.

4. Use of the gene Panel described in claim 1, the probe set described in claim 2 or the kit described in claim 3 in detecting somatic variations of the SLC35A2 gene or in preparing a product for detecting somatic variations of the SLC35A2 gene.

5. The use according to claim 4, characterized in that, The somatic variations of the SLC35A2 gene include, but are not limited to, single nucleotide variations, insertion variations and / or deletion variations.

6. A method for detecting somatic mutations of the SLC35A2 gene, characterized in that, Comprising: S1. Obtain genomic DNA of the sample to be tested; S2. Construct a library based on the genomic DNA; S3. Use the gene Panel described in claim 1, the probe set described in claim 2 or the kit described in claim 3 to perform target region capture on the library; S4. Sequence the captured target region, then perform data quality control, align with the reference genome, and perform variant detection.

7. The detection method of somatic variation of SLC35A2 gene according to claim 6, characterized in that The sample to be tested includes brain tissue or blood; and / or, the sequencing platform includes the Illumina next-generation sequencing platform, the BGI next-generation sequencing platform; and / or, when the sample to be tested is a blood sample, the sequencing depth ≥ 1000x, generating ≥ 0.15 Gb of raw data; and / or, when the sample to be tested is a brain tissue sample, the sequencing depth ≥ 3000x, generating ≥ 0.45 Gb of raw data; and / or, in the data quality control, Fastqc and / or Trimmomatic are selected to filter low-quality reads and bases; the low-quality reads and bases include at least one of the following: sequencing adapter sequences; sequences with an average base quality threshold lower than 15 in a 4-base length sliding window; reads with a length lower than 60 bp after removing sequencing adapter sequences and low-quality bases; and / or, in the variant detection, small somatic variant detection, including SNVs and Indels, is performed based on GATK v4.5 Mutect2; somatic structural variant detection is performed based on Manta.

8. The detection method of somatic variation of the SLC35A2 gene according to claim 7, characterized in that Filter the SNVs and Indels variations among them, filter out false positive variations caused by sequencing strand orientation bias and sample contamination, and the method used is FilterMutectCalls in the GATK v4.5 toolkit, where the filtering conditions are generated by the CreateSomaticPanelOfNormals, LearnReadOrientationModel, GetPileupSummaries, CalculateContamination tools in the GATK v4.5 toolkit.

9. A somatic variation detection device for SLC35A2 gene, characterized in that, Comprising: A library construction unit for constructing a library from the DNA genome of the sample to be tested; A target region capture unit for performing target region capture on the library using the gene Panel described in claim 1, the probe set described in claim 2 or the kit described in claim 3; A sequencing unit for sequencing the obtained target region; A data quality control unit for performing quality control on the sequencing data; An alignment unit for aligning the reads after data quality control with a reference genome; A variant detection unit for performing variant detection on the aligned data.

10. A computer-readable medium storing a program that can run the SLC35A2 gene somatic variant detection method according to any one of claims 6 to 8.

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