Molecular analysis of spinal nerve sheath tumors has identified CSH1 as a candidate gene for differentiating between Schwannomatosis and Spinal schwannoma

doi:10.21203/rs.3.rs-4816088/v1

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Research Article

Molecular analysis of spinal nerve sheath tumors has identified CSH1 as a candidate gene for differentiating between Schwannomatosis and Spinal schwannoma

https://doi.org/10.21203/rs.3.rs-4816088/v1

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Purpose

The most common form of schwannomatosis is characterized by multiple and recurrent spinal schwannomas, exhibiting symptoms similar to those of spinal schwannomas. The molecular factors responsible for tumor multiplicity in schwannomatosis are not yet understood. This study aims to identify differentially expressed genes (DEGs) between intradural spinal schwannomas and schwannomatosis, and explore the underlying mechanisms of schwannomatosis pathogenesis

Methods

We used high-throughput sequencing technology to sequence 30 tumor samples(15 conventional spinal schwannomas and 15 schwannomatosis cases), and identified DEGs between the two sample groups using the limma R package. Using machine learning algorithms to identify more significant biomarkers from the DEGs.We evaluated the diagnostic value of these biomarkers for schwannomatosis using receiver operating characteristic (ROC) curve analysis and an Alignment Diagram. Gene set enrichment analysis (GSEA) was employed to explore relevant pathways.

Result

Our findings revealed 249 DEGs, with OXTR, CSH1, SNCAIP, and DPP4 emerging as candidate genes.Subsequent q-PCR experiments validated these biomarkers' reliability. Validation showed that CSH1 expression was elevated in schwannomatosis samples, while DPP4, OXTR, and SNCAIP expressions were decreased compared to solitary schwannoma cells. ROC curves and the Alignment Diagram confirmed the stability and accuracy of our diagnostic model.GO functional annotation and KEGG pathway enrichment analysis indicated that these DEGs might be involved in biological activities related to immunity, inflammation regulation, and tumorigenesis.

Conclusion

We have developed a novel diagnostic model for distinguishing between schwannomatosis and spinal schwannomas. Moreover,These transcriptional changes enhance our understanding of the pathogenesis of schwannomatosis, identify risk factors, and provide insights into new therapeutic targets.

Spinal Tumors

Schwannomatosis

Transcriptomics

Regulatory Network

genomics

Spinal schwannomas are benign neurogenic tumors originating from Schwann cells, making them the most common primary benign tumors within the adult spinal canal, accounting for nearly one-third of all spinal tumors pically(Takahashi 2023;El-Hajj 2022), spinal schwannomas are solitary, firm, oval, and lobulated tumors that originate from the neurological root༈Kurtkaya-Yapicie 2003༛Scheithauer 2010༛Traul 2007༉.

In contrast, the most common form of Schwannomatosis involves multiple and recurrent spinal schwannomas(Jacoby 1997). Due to limited research, Schwannomatosis is often misdiagnosed and treated as spinal schwannoma༈MacCollin 2005༉. However, advancements in diagnostic and treatment technologies have increased the reported incidence of Schwannomatosis, underscoring its significant public health implications. The multilevel manifestation and uncertain recurrence patterns have not received adequate attention, leading to increased disability and economic burden for patients as the disease progresses [1; 2]༈Thomas 2016;Kehrer-Sawatzki 2017༉.

In 1973, Niimura first described and named Schwannomatosis, characterized by multiple schwannomas throughout the body without bilateral vestibular schwannomas or skin neurofibromas. In 1997, the National Institutes of Health classified Schwannomatosis as the third subtype of neurofibromatosis (NF3). The initial diagnostic criteria established in 1997 required two or more pathologically confirmed schwannomas at different body sites and no evidence of vestibular tumors in an enhanced head MRI of individuals older than 18 years (Jacoby) More stringent diagnostic criteria were introduced in 2005 and 2006 ༈MacCollin 2005;M.E. Baser 2006༉. Schwannomatosis differs significantly from NF1 and NF2 in genetics, pathology, and clinical features, with an incidence of 0.58 cases per million people per year; only 15–25% of cases are familial, the rest are sporadic (Evans et 2020༛Tamura et 2021).

In Schwannomatosis, central nervous system tumors, especially multiple and recurrent spinal schwannomas, are most common, occurring in up to 74% of patients. These tumors grow at different segments and locations within the spinal column, either simultaneously or at different time(Thomas et 2016; L.B. Jacoby et 1999). Due to their similar pathology to conventional solitary schwannomas, distinguishing the two through pathology is challenging; diagnoses currently rely primarily on imaging(Tamura 2021). Clinically, misdiagnosing multilevel tumors within the spinal column poses a significant risk, potentially leading to irreversible neurological damage. Surgical removal is the gold standard for treatment but may cause permanent and severe neurological deficits due to the involvement of multiple nerves, with up to 30% of patients experiencing postoperative neurological deterioration (V.L. Merker et 2012)

Unfortunately, no drug therapies specifically target Schwannomatosis, highlighting the urgent need for safe and effective treatments(BushML et 2011). Investigating the mechanisms of Schwannomatosis to predict, identify, and explore new drug applications is critical. Our study selected tumor samples from solitary spinal schwannomas and Schwannomatosis for large-scale sequencing to observe gene expression related to the multiplicity of spinal schwannomas. We aimed to explore the diagnostic value of these genes in differentiating solitary from multiple schwannomas and discuss the mechanisms leading to the multiplicity of schwannomas within the spinal canal.

Sample Sources

This study complies with the ethical principles outlined in the 1975 Declaration of Helsinki. Approval was obtained from the Ethics Committee of the Third Hospital of Hebei Medical University, and all participants provided written informed consent upon registration and were informed about the study details. Tumor tissue samples were collected from patients who had undergone surgery for spinal canal neurofibromas at the Third Hospital of Hebei Medical University, totaling 30 cases (15 with Schwannomatosis and 15 with Spinal Schwannoma).

Inclusion criteria:

Age over 18 years.

No tumors in locations other than the spinal schwannoma.

For Schwannomatosis samples, none met the existing diagnostic criteria for NF2 and had two or more pathologically confirmed schwannomas in different spinal segments within the spinal canal (M.J. Smith et 2013; S.R. Plotkin et 2013)

For spinal schwannoma samples, solitary tumors of the spinal cord with the pathological type of nerve sheath tumor and no recurrence within three years.

All cases were treated surgically, with complete tumor resection, no visible tumor residue in the surgical field, and postoperative imaging confirming complete resection (V.L. Merker et 2012).

No significant differences in age or gender between the two groups, and none of the patients had received drug treatment for neurofibromas.

The collected samples were categorized as follows: the spinal neurofibroma group as the case group and the neurofibromatosis group as the control group.

mRNA Library Sequencing, Preparation, and Screening

Total RNA was extracted from samples using Trizol reagent, and mRNA was isolated. The concentration and purity of the extracted RNA were assessed using a Nanodrop 2000 spectrophotometer, and RNA integrity was checked using agarose gel electrophoresis and an Agilent 2100 for RIN value determination. RNA was fragmented using ultrasonication, and the concentration and purity were reassessed using Nanodrop 2000. RNA integrity was re-evaluated with agarose gel electrophoresis, and the RIN value was measured again using Agilent 2100.

For library preparation, a total RNA amount of 5 µg with a concentration of ≥ 200 ng/µL and an OD260/280 ratio between 1.8 and 2.2 was required. The fragmented mRNA was reverse transcribed to produce cDNA. The cDNA fragments underwent end repair and PCR amplification to construct the library, which was then sequenced on a HiSeq 4000 (Illumina, USA). Data filtering was performed using trim_galore (version 0.6.6) and cutadapt (v3.4 with Python 3.9.4); data quality control was conducted with FastQC (v0.11.5) and MultiQC (v1.10.1).

Using the human gene annotation file GRCh38 (hg38) as a reference genome, RNA-seq analysis was performed with the Hisat2 + FeatureCounts pipeline to obtain a transcript expression matrix. Two-dimensional principal component analysis and hierarchical clustering were conducted to compare similarities and differences between the experimental and control groups. The RNA-seq data were normalized using DESeq2 software.

The Over-Representation Analysis of DEGS

To perform GO and KEGG enrichment analysis of differentially expressed genes (DEGs), Over-Representation Analysis (ORA) was used. Functional annotation of DEGs was conducted using online databases for annotation, visualization, and integrated discovery, including DAVID (version 6.8) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Gene Ontology (GO) analysis was performed to explore the biological processes (BP), cellular components (CC), and molecular functions (MF) associated with the predicted targets.

The expression Constructing a Protein-Protein Interaction (PPI) Network

To study interactions between proteins and evaluate genes within the protein-protein interaction (PPI) network, we constructed the network and performed correlation analysis. Overlapping target detection between candidate target genes and differentially expressed genes (DEGs) was conducted using Venny 2.0 to identify critical target genes. The PPI network was built using the STRING database, and various network parameters, including betweenness centrality (BC), closeness centrality (CC), degree (De), and topological coefficient (TC), were calculated. Critical genes were selected as key targets based on the highest degree values.Identification of Potential Diagnostic Biomarkers for Schwannomatosis

To further assess the accuracy of the genes identified from the PPI analysis, we performed LASSO logistic regression on the candidate genes selected from the PPI results across all sequencing samples. Feature selection was conducted using the glmnet package in R software. Additionally, we constructed a machine learning model using the genes from the PPI analysis, with the entire sequencing dataset as input. The SVM-RFE method was employed to rank features within the dataset. By intersecting the results from both algorithms, we identified more accurate biomarkers.

Identification of Potential Diagnostic Biomarkers

Next, we employed machine learning methods to construct a diagnostic model and evaluated its accuracy using an Alignment Diagram. The model's goodness-of-fit was assessed with the Hosmer-Lemeshow test, and its diagnostic value was determined through the area under the receiver operating characteristic (AUC-ROC) curve. Genes with AUCs greater than 0.7 were further explored as potential diagnostic biomarkers for neurofibroma recurrence.

qPCR

Total RNA was extracted following the Trizol protocol, and complementary DNA (cDNA) was synthesized using the PrimeScript RT Master Mix kit (Takara, RR036A). Each cDNA sample was diluted 20-fold and used as a template for quantitative real-time PCR (qRT-PCR) with SYBR Green Master Mix (Thermo Fisher Scientific, A25742). The qRT-PCR was performed in a 10 µl reaction volume using the Applied Biosystems® ViiA7 Real-Time PCR System (Thermo Fisher Scientific, USA). Each reaction was conducted in triplicate.

The qRT-PCR procedure was as follows: initial denaturation at 95°C for 30 seconds, followed by 40 cycles of 95°C for 10 seconds, 60°C for 20 seconds, and 72°C for 20 seconds. The relative expression levels of the selected genes were calculated using the 2 − ΔΔCT method.

Gene set enrichment analysis

Enrichment analysis was conducted using GSEA software (version 4.1.0) to identify biological processes associated with these genes. Genes were ranked based on their differential expression between the two sample types, typically using Fold Change. Predefined gene sets (h.all.v7.5.1.symbols.gmt and c5.all.v7.5.1.symbols.gmt) were tested for enrichment at the top or bottom of this ranked list.

Clinical Characteristics and Outcomes of the Patients

The clinical characteristics of 15 patients with Schwannomatosis are summarized in Table 1. The cohort comprised 9 males and 6 females, with an average age of 50.6 years (median 54 years, range 23–67 years). The most common symptoms included muscular weakness, pain, and numbness. Two patients (Case 9 and Case 14) had recurrent spinal neurofibromas, while the remaining 13 patients had multiple spinal neurofibromas. All patients underwent complete tumor resection and spinal reconstruction surgery. Follow-up showed that 3 patients experienced worsening symptoms, whereas 10 patients demonstrated improvement. As of July 2024, all patients are alive.

Table 1

Clinical Characteristics of 15 Patients with Spinal Schwannomatosis
No.	Sex	Location	Primary/ recurrent	Age	Adjacent relations to spinal canal	Shape	Bone structure destruction	Initial symptoms	Current status
1	F	T8 T11	Primary	28	Extramedullary and subdural	Ellipse and dumbbell	No	Pain	Amelioration
2	F	C7 L4	Primary	53	Extramedullary and subdural	Dumbbell	No	Insensitive	Amelioration
3	M	T1 L1	Primary	45	Extramedullary and subdural	Ellipse	No	Pain	Analogy
4	F	T2 T4	Primary	66	Extramedullary and subdural	Ellipse	No	Muscular weakness	Amelioration
5	M	L1 L4	Primary	59	Extramedullary and subdural	Dumbbell	No	Muscular weakness	Deterioration
6	F	C2 L2	Primary	33	Extramedullary and subdural	Irregular and round	No	Muscular weakness	Amelioration
7	M	L3 S1	Primary	53	Extramedullary and subdural	Irregular	Yes	Pain	Amelioration
8	F	T10 L1	Primary	59	Extramedullary and subdural	Irregular	No	Muscular weakness	Amelioration
9	M	L2	recurrent	67	Extramedullary and subdural	Dumbbell	No	Insensitive	analogy
10	M	T2 L3	Primary	23	Extramedullary and subdural	Ellipse	Yes	Insensitive	deterioration
11	F	C4 C7	Primary	55	Extramedullary and subdural	Irregular	No		Amelioration
12	F	C6 T2	Primary	45	Extramedullary and subdural	Ellipse	No	Muscular weakness	Amelioration
13	F	T1 L2	Primary	60	Extramedullary and subdural	Ellipse and irregular	No	Pain	Amelioration
14	F	T5	recurrent	63	Extramedullary and subdural	Ellipse	Yes	Insensitive	Deterioration
15	M	L1 S2	Primary	51	Extramedullary and subdural	Round	No	Muscular weakness	Amelioration
Current status: Follow-up Results 18–20 Months Post-Surgery

Selection of Differentially Expressed Genes and Construction of the PPI Network

Based on the criteria, a total of 249 differentially expressed genes (DEGs) were identified between the experimental and control groups. Among these, 179 genes were upregulated in the Case group, and 70 genes were downregulated. To visualize these DEGs, we created a volcano plot using ggplot2 to display the top 10 upregulated and downregulated DEGs (Fig. 1) and used ComplexHeatmap to show the expression patterns of these genes (Fig. 2).

The STRING database results were imported into Cytoscape to construct a protein-protein interaction (PPI) network, which comprised 114 nodes and 143 edges. To ensure that biomarkers were expressed in all sequencing samples, we filtered the PPI results to select genes with non-zero expression across all samples. This filtering yielded 8 genes: PRG4, FABP7, OXTR, CSH1, SNCAIP, CD24, GJB2, and DPP4. Compared to the control group, CSH1 and CD24 were upregulated, while PRG4, FABP7, OXTR, SNCAIP, GJB2, and DPP4 were downregulated. These 8 genes were defined as key targets (Fig. 3).

The x-axis represents the fold change (Case/Control, log base 2), while the y-axis indicates the significance level of the test (-log10(P.Value)). Each point in the plot represents a gene. Green and red points denote significantly differentially expressed genes. Red points indicate genes with upregulated expression, green points indicate genes with downregulated expression, and gray points represent genes with no significant difference.

Each small square represents a gene, with the color indicating the gene's expression level: higher expression is shown in red, while lower expression is shown in green. The color bar at the top of the figure indicates the sample grouping, with blue representing Control and red representing Case. Each row shows the expression levels of a single gene across different samples, while each column represents the expression levels of all differentially expressed genes in a single sample.

Associated with Inflammation and Tumor Progression Pathways

Over-Representation Analysis show: The GO enrichment results revealed a total of 2,660 enriched GO pathways, including 2,124 in Biological Process (BP), such as forebrain development (GO:0030900), multi-organism reproductive process (GO:0044703), multicellular organismal process (GO:0044706), embryonic organ development (GO:0048568)et.(Figuer4)

KEGG enrichment analysis showed that DEGs were primarily enriched in pathways related to neuroactive ligand-receptor interaction (hsa04080), PI3K-Akt signaling pathway (hsa04151), ECM-receptor interaction (hsa04512), cytokine-cytokine receptor interaction (hsa04060), and cAMP signaling pathway (hsa04024). These pathways are involved in regulating immune and inflammatory functions (Fig. 5).

The outermost circle represents the IDs of the pathways. The second circle shows the number of enriched genes, with the color intensity representing the significance of the enrichment. The third circle indicates the number of upregulated and downregulated genes, with pink representing upregulated genes and green representing downregulated genes. The fourth circle is a bar chart where the y-axis represents the number of enriched genes, and different colors correspond to the three parts of the GO analysis.

Machine Learning Methods for Screening Candidate Biomarkers

To identify key feature genes, we performed LASSO logistic regression analysis on the 8 candidate genes obtained from the PPI screening of all sequenced samples. Using the glmnet package in R with the family parameter set to binomial, we generated two graphs: one displaying the gene coefficients and another showing the cross-validation error (Fig. 6). The optimal model was identified with log(lambda.min) = -2.038655 and log(lambda.1se) = -1.66652, which revealed 4 feature genes: OXTR, CSH1, SNCAIP, and DPP4.

Similarly, we constructed a machine learning model using the 8 genes from the PPI screening and the full dataset of sequenced samples. The SVM-RFE method was used to rank features, and the model's accuracy was highest when the number of feature variables was 7. The top 7 candidate biomarkers identified were DPP4, SNCAIP, CD24, CSH1, OXTR, GJB2, and FABP7 (Fig. 8).

To determine the final biomarkers, we intersected the feature genes identified by both machine learning methods. This analysis identified 4 biomarkers: OXTR, CSH1, SNCAIP, and DPP4. The Venn diagram illustrates the similarities and differences between the two algorithms, enhancing the credibility of the biomarker screening (Fig. 6). For visual representation, box plots of the biomarker expression levels in the experimental and control groups were created using the ggplot2 package in R (Fig. 9).

Alignment Diagram and Receiver Operating Characteristic

In this study, we used the expression levels of 4 biomarkers (OXTR, CSH1, SNCAIP, and DPP4) across all sequenced samples to construct a nomogram model for predicting the risk of solitary spinal schwannoma. The nomogram was developed using the R package "rms" and is designed to aid in diagnosing and forecasting the likelihood of developing solitary spinal schwannoma (Fig. 10).

Figure 10

Figure 10A: Each segment on the line corresponding to the variables OXTR, CSH1, SNCAIP, and DPP4 is labeled with a scale that represents the range of possible scores for each gene. The length of each segment reflects the gene’s contribution to the outcome event. The "Total Points" in the graph denote the cumulative score derived from adding up the individual scores for all gene values. The "Risk of Case" indicates the probability of developing solitary spinal schwannoma based on the total score. Figure 10B: The x-axis shows the predicted probability of experiencing solitary spinal schwannoma, while the y-axis represents the actual probability. The black dashed line indicates perfect prediction. The blue solid line represents the entire dataset, and the black solid line, adjusted for bias through bootstrapping (1,000 repetitions), depicts the observed performance of the nomogram. Figure 10C: The x-axis displays the false positive rate (1-specificity), and the y-axis shows the true positive rate (sensitivity). The Area Under the Curve (AUC) value reflects the overall performance of the ROC curve.

q-PCR

We take 3 samples from each of the experimental group and the control group for q-pcr testing, q-PCR validation of DEGs in the experimental and control groups (Fig. 11): The expression of CSH1 gene is upregulated in the schwannomatosis group.The expression of DPP4 and SNCNIP is as decrease in he schwannomatosis group. This manifestation is consistent with our sequencing results.But the expression of OXTR showed opposite results in the qPCR experiments compared to the sequencing results.

Quantitative q-PCR of the expression of CSH-1/DPP4/SNCNIP in control group and experimental group.Housekeeping gene GAPDH as an internal reference gene. Asterisks indicate statistically significant changes compared to the parental line control group by paired-sample Student’s t test(**P ≤ 0.01).

The pathogenesis of schwannomatosis remains unresolved (S.R. Plotkin et 2018). Previous research has identified mutations in the LZTR1 or SMARCB1 genes in 86% of familial cases and 40% of sporadic cases of schwannomatosis (Kehrer-Sawatzki et 2017; S.R. Plotkin et 2013; C. Louvrier,2018). Tumor development in schwannomatosis often involves mutations in multiple tumor suppressor genes, frequently mediated by large heterozygous deletions on chromosome 22q. These deletions typically encompass SMARCB1, LZTR1, and the NF2 gene (L.B. Jacoby et 1999; S. Mansouri et 2021). The combined loss of these tumor suppressor genes leads to variations in 22q fragments, which are associated with the development of schwannomatosis (J. Antinheimo et 2000; A. Piotrowski et 2022; R. Caltabiano et 2017)However, clear pathogenic mutations are often absent in most sporadic cases of schwannomatosis(R. Sestini et 2008; J. Swensen et 2009)

This study aims to elucidate the molecular differences between schwannomatosis and spinal schwannomas, assess the diagnostic value of candidate genes for schwannomatosis, and analyze potential pathophysiological mechanisms underlying the multiplicity and recurrence of spinal schwannomas. Using sequencing on the Illumina platform, we identified 249 differentially expressed genes (DEGs) between the experimental and control groups, with 70 genes upregulated and 179 downregulated. Through PPI analysis and other machine learning techniques, we highlighted OXTR, CSH1, SNCAIP, and DPP4 as key genes associated with the disease. The diagnostic model constructed with Alignment Diagram and ROC analysis validated these genes, suggesting their collective role in the development of multiple intramedullary schwannomas.

Quantitative PCR (qPCR) results revealed elevated expression of CSH1 in schwannomatosis. CSH1 encodes chorionic somatomammotropin hormone 1 (CSH1), a hormone secreted by placental cells during pregnancy. This protein, produced in the placenta of higher primates, including humans, regulates various metabolic processes during pregnancy (Adu-Gyamfi et 2024). The CSH1 gene is located on human chromosome 17 and is part of the same family as growth hormone (GH) and prolactin (PRL). These hormones influence breast growth, milk secretion, and response to other hormones༈C.-W. Chang et 2022; M.-F. Hivert et 2024༉. While CSH1 plays a crucial role during normal pregnancy by regulating maternal metabolism and stimulating insulin production, its expression is generally low or absent in non-pregnant states. Data from the National Center for Biotechnology Information (NCBI) (PRJEB2445) show that CSH1 expression is minimal in the normal human nervous system (Fig. 12). However, CSH1 has been found to be overexpressed in various tumor cells, including choriocarcinoma(B. Marsh et 2022). The increased expression of CSH1 in schwannomatosis may provide insights into its role in the multiplicity of intramedullary schwannomas.

DEGs were analyzed using KEGG and GO enrichment analyses, which highlighted potential links to immune regulation and inflammatory responses. Key pathways such as Neuroactive Ligand-Receptor Interaction and the PI3K-Akt signaling pathway were identified as central to signal transduction, immune-inflammatory responses, and tumor development. Gene Set Enrichment Analysis (GSEA) further underscored the importance of pathways like PI3K/Akt and mTOR. The PI3K/Akt signaling pathway is critical in numerous physiological processes and plays a pivotal role in disease development, particularly tumors. mTOR, a downstream protein of this pathway, exists in two complexes, mTORC1 and mTORC2, and regulates cellular functions such as transcription, translation, and proliferation through downstream targets like 4E-BP1 and p70S6K. Aberrant activation of mTOR is associated with tumor progression, as seen in gliomas and breast cancer (G. Choe et 2003; M.J. Smith et 2015)

GSEA results for the biomarkers OXTR, CSH1, SNCAIP, and DPP4 revealed significant associations with immune and inflammatory pathways. Notably, the CSH1 gene, which is highly expressed in schwannomatosis, appears to influence the PI3K-Akt-mTOR pathway. This gene may contribute to abnormal mTOR activation, potentially serving as a common risk factor for schwannomatosis (V.L. Merker 2012; Z. Chen et 2020). Therefore, the occurrence of multiple intradural schwannomas in schwannomatosis may be driven by factors associated with the PI3K-Akt signaling pathway and neuroactive ligand-receptor interactions, among other inflammatory responses.

Currently, the treatment of schwannomatosis primarily involves surgical intervention (R.N. Hernandez et 2021). Surgical resection is indicated for cases with spinal cord compression or symptoms related to schwannomas, such as pain or sensory-motor abnormalities, with the goal of controlling symptoms and preserving neurological function (X. Su et 2012; A. Gonzalvo et 2011). However, the surgical management of nerve tumors often risks nerve function damage. There are no established guidelines for radiotherapy, chemotherapy, or targeted therapy in schwannomatosis patients (O. BushML 2011). For patients with extensive spinal schwannomas or very rare malignant schwannomas that are not amenable to surgery(D. Miricescu et 2020; Z. Zou et 2020), targeted therapies are still under investigation. Research into candidate genes and associated pathways like PI3K-Akt may offer insights into potential therapeutic targets. Drugs such as mTOR inhibitors, including Everolimus (RAD001), MLN0128, and AZD014, have been extensively studied for their effects in hematological cancers and solid tumors(H. Li et 2021) However, there is no precedent for their use in schwannomatosis, and further research is needed.

This study aims to elucidate the molecular relationships between spinal schwannomas and schwannomatosis, develop diagnostic models, and identify candidate genes while exploring the mechanisms underlying multiple schwannomas. Clinical application and measurement of these biomarkers require further investigation. Future research should validate the roles of these genes and related pathways through RNA interference, overexpression studies, and pathway inhibition at both cellular and animal levels. Targeting relevant pathways could potentially provide new therapeutic strategies for patients with multiple schwannomas who are not suitable candidates for surgical treatment.

Competing interests

The authors declare that they have no conflict of interest.

Funding:

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author Contribution

Guosheng Shi conducted the study, performed statistical analysis, and wrote the manuscript; Huilin Ren collected and analyzed the data; Dawei Zhao provided technical support; Hequn Chen provided technical support; Xuanbo Luo performed data collection; Jingnan Zhao performed data collection ;Suwei Yan performed statistical analysis; Wei Bu conceived, designed the study, critically reviewed and revised the manuscript.

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Molecular analysis of spinal nerve sheath tumors has identified CSH1 as a candidate gene for differentiating between Schwannomatosis and Spinal schwannoma

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Version 1

Abstract

Purpose

Methods

Result

Conclusion

Figures

Background

MATERIALS AND METHOD

Sample Sources

mRNA Library Sequencing, Preparation, and Screening

The Over-Representation Analysis of DEGS

The expression Constructing a Protein-Protein Interaction (PPI) Network

Identification of Potential Diagnostic Biomarkers

qPCR

Gene set enrichment analysis

Conclusion

Clinical Characteristics and Outcomes of the Patients

Selection of Differentially Expressed Genes and Construction of the PPI Network

Associated with Inflammation and Tumor Progression Pathways

Machine Learning Methods for Screening Candidate Biomarkers

Alignment Diagram and Receiver Operating Characteristic

q-PCR

Discussion

Declarations

Competing interests

Funding:

Author Contribution

References

Additional Declarations

Status:

Version 1