Tumor microenvironment and immune function analysis and chemotherapy prediction of SDHB in PPGL (pheochromocytoma/ paraganglioma)

doi:10.21203/rs.3.rs-3909099/v1

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Tumor microenvironment and immune function analysis and chemotherapy prediction of SDHB in PPGL (pheochromocytoma/ paraganglioma)

https://doi.org/10.21203/rs.3.rs-3909099/v1

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Background

SDHB mutations are risk factors for PPGL metastasis and poor prognosis. This study aimed to identify the SDHB gene signature and mechanisms in PPGL, and investigate its association with immunotherapy response.

Method

PPGL transcriptome, clinical, and single nucleotide mutation data were obtained from TCGA database. Univariate, LASSO, and multivariate Cox regression analysis was applied to construct the prognostic signature. Survival analysis, ROC curve, Cox regression analysis, and nomoplot were utilized to evaluate accuracy of the model. GO and KEGG enrichment of differentially expressed genes between risk groups were used to explore potential action mechanisms. Prognostic lncRNA co-expressed with risk signature genes were also identified. The CIBERSORT, ssGSEA, GSVA, and ESTIMATE algorithms were employed to assess the association between risk score and variations of tumor microenvironment, immune cell infiltration, immune checkpoints, and immune responses. The maftools and pRRophetic packages were enrolled to predict tumor mutation burden and drug sensitivity.

Result

A signature of SDHB genes were identified immune checkpoint and alternative splicing, which showed great value of mechanisms for PPGL. Functional enrichment implied the variation of immune pathways and metallopeptidase activity between expression groups. High- expression group exhibited higher immune score, but lower tumor purity. Finally, we screened sensitive drugs for different risk groups.

Conclusion

The novel prognostic signature of cuproptosis genes could help risk stratification, immunotherapy response prediction, and individualized treatment strategy-making for glioma patients.

single-cell RNA sequencing

immune response

pheochromocytoma/ paraganglioma

prognosis

drug sensitivity

Pheochromocytoma/paraganglioma ( generally referred to as the PPGL) is originated in the adrenal medulla or outside adrenal chromaffin cell neuroendocrine tumor^[1], the annual incidence of about 6 in 1 million people^[2], prevalence of 1/6500-1/2500^[3]. PPGL is the most heritable tumor known, with about 40% of patients carrying inherited embryonic mutations^[4]. At least 30 PPGL susceptibility/pathogenic genes have been identified^[5]. Twelve genetic syndromes whose clinical phenotypes include systemic lesions of multiple tissues or organs in addition to PPGL^[6].

The mutation of SDHB gene is most common in extradrenal accessory neuroma, and the prognosis of patients with SDHB mutation is poor^[7]. The SDHB gene encodes the electron transport chain involved in the oxidation of ubiquitin to ubiquitinol resulting in overexpression of hypoxia-inducing factor (HIF) proteins, increased transcription of HIF-1A regulatory genes, and decreased DNA demethylation^[8]. The pathogenic mutation of SDHB is mainly manifested as single PGL outside the adrenal gland. But it can also develop into multiple tumors^{[9, 10]}. Once a tumor has formed, the risk of metastasis is about 23–25%^[11].

After diagnosis, UBPGL is usually treated with surgery, including transurethral electrocystectomy, partial cystectomy or radical total cystectomy. For patients with recurrence, secondary resection is feasible. In addition to resection of metastases, patients with metastasis also need combined targeted therapy/chemotherapy^{[12, 13]}. At present, the best chemotherapy regimen is the combination of cyclophosphamide, choncinine and dacarbazine (Averbuch regimen)^[14]. The effectiveness of the new targeted therapies is unclear. The initial results of everolimus were unsatisfactory^[15], while a minority of patients treated with Sunitinib, asitinib, and capozzatinib had better results^{[16, 17]}. Despite active treatment, the survival rate of patients with metastasis is still low^[18]. The most common sites of metastasis in SDHB-mutated PPGL patients were bone (64%), lung (47%), lymph nodes (36%), and liver (32%). The 5-year survival rate for patients with metastatic PPGL ranges from 34 to 74%. Currently, in addition to resection of metastases, metastatic PPGL can be treated with interiobenzidine (131I-MethaIdoBenzoGuanidine, 131I-MIBG) or systemic chemotherapy (cyclophosphamide, vincristine, and dacarbazine) as adjuvant therapy. However, the Objective response rate (ORR) is lower than 40%^[19].

In the PPGL (Pheochromocytomas/Paragangliomas) related literature, SDHB mutations are risk factors for PPGL metastasis and poor prognosis^[20–24]. Currently up to two-thirds of PPGL patients affected by SDHB mutations develop metastatic disease^[25]. So far, advances in genomics, immunology, and various other disciplines have spawned many experimental treatments (targeted therapy and immunotherapy), which offer new treatments for PPGL. However, the efficacy of these therapies is still unsatisfactory^[26–28].Therefore, it is of great significance to further explore the mechanism of SDHB in PPGL and explore new therapeutic targets and pathways. Single-cell sequencing is able to examine the sequence information of individual cells, probe cellular differences at higher resolution, and better understand the function of individual cells in their microenvironment^[29].Resolving genomic information in single cells can help reveal the role of genetic mosaicism and intratumoral genetic heterogeneity in tumor development or response to therapy^[30].

Given the heterogeneity of PPGL, single-cell resolution analysis of genomic alterations can help dissect the tumor microenvironment and provide insights into the role of tumor-infiltrating immune cells, including their heterogeneity and potential role in disease progression and immunotherapy. Patient cluster analysis and immune-correlation analysis were performed. Tumor cells grow in contact with the tumor microenvironment, and the interaction between tumor cells and the microenvironment leads to mutual metabolic reprogramming, which provides favorable conditions for tumor growth and metastasis^[31]. Some researchers have found that the transcriptome heterogeneity of neuroendocrine tumor cells is high among patients, and the tumor microenvironment is also heterogeneous^{[32, 33]}, mainly contributed by macrophages in the immune cell cluster and Schwann cells in the stroma. The active transcriptional programs of SDHB in PPGL include neuronal development, hormone synthesis and secretion, and DNA replication^[32]. VD et al. demonstrated that SDHB silencing itself increases tumor cell migration/invasion, and the microenvironment represented by CAFs playsa key role in enhancing the collective migration/invasion of SDHB silenced tumor cells^[25].

Alternative splicing can produce mrnas with different untranslated regions (UTRs) or coding sequences. The mechanisms include exon skipping (deletion of specific exons, called cassette exons), selection between mutually exclusive exons, use of alternative splicing sites (affecting the boundary between introns and exons in this alternative splicing, which contributes to transcript diversity), and intron retention, and the use of alternative splicing sites (affecting the boundary between introns and exons in this alternative splicing, which helps transcript diversity). These differences may affect mRNA stability, localization, and translation^[34]. Cancer cells have cancer type - or subtype-specific alterations in splicing that have prognostic value and help to be a marker of cancer progression^[35]. As regulators of the immune system, immune checkpoints are essential for self-tolerance and preventing the immune system from attacking cells. These splicing alterations are often associated with the occurrence of cancer driver mutations that encode core components of the splicing machinery or regulatory genes. Of therapeutic relevance, the transcriptome structure of cancer cells makes them particularly vulnerable to pharmacological inhibition of splicing, and small-molecule splicing modulators are currently in clinical trials that offer novel and personalized approaches to cancer therapy in addition to splice-site switching antisense oligonucleodes^[35].

Long non-coding RNA (lncRNA) is a kind of non-coding RNA with a molecular weight of more than 200 nucleotides^[36].Nowadays, more and more lncRNAs are considered as prognostic biomarkers or for immunotherapy response prediction^{[37, 38]}. JOB S et al. demonstrated that an lncRNA (BC063866) can be obtained in the presence of SDHx metastatic and non-metastatic tumors are distinguished in patients with mutations, and this transcript is also not associated with SDHx carriers, independent risk factors associated with good clinical outcomes^[39].

As regulators of the immune system, immune checkpoints are essential for self-tolerance and preventing the immune system from attacking cells^[40]. However, this mechanism can also be used by cancer cells to protect themselves from immune attack^[41]. Of all the ICGs under study, CTLA4 and PD-L1 (CD274) are the most commonly studied, and CTLA4 acts by competitive binding to CD80/86 ligands in dendritic cells to moderate CD4 + helper T cell activation while promoting cellular modulation leading to a pro-tumor immunosuppressive phenotype^[42].PD-L1 and PD-L2 can be expressed in tumor cells and interact with PD-1 in immune cells to attenuate immune responses^{[43, 44]}.

In summary, exploring the immune and tumor microenvironment mechanisms related to SDHB in PPGL is of certain value for the discovery of potential therapeutic targets for PPGL.

2.1 Data acquisition

Used in the study of PPGL transcriptome expression data and clinical data are derived from the TCGA database (https://cancergenome.nih.gov).All relevant regulations regarding access to TCGA data and the protection of patient privacy were adhered to.

2.2 Data Processing

R software (version 4.2.0) and a specific R package (https://svn.r-project.org/R-dev-web/trunk/index.html) process and analyze the relevant data. The PPGL transcriptome was divided into SDHB high expression group and SDHB low expression group. The differences in tumor microenvironment and immune cell infiltration between the two groups were analyzed, and the immune function enrichment analysis was performed. The significantly different immune cells were screened as immune checkpoints. Finally, the difference of transcriptome alternative splicing between the high expression group and the low expression group was analyzed.

2.3 Tumor microenvironment

For the tumor microenvironment score (immune-score, stromal score, and tumor purity), the authors calculated the relative abundance of 22 immune cell types to illustrate the relationship between the risk score and immune-cell infiltration.Single-sample gene set enrichment analysis (ssGSEA) and gene set variation analysis (GSVA) were used to study the enrichment changes of 29 immune-related functional gene sets between the high and low expression groups of "limma", "GSEAbase" and "GSVA" packages."ggpubr", "ggExtra", "corrplot" and "ggplot2" packages were used for the presentation of the results.Meanwhile, the correlation between risk scores and key immune checkpoint genes (ICGs) was analyzed using the "limma" package and the "ggpubr" and "ggplot2" packages.

2.4 Characterization of WGCNA and SDHB scores

Weighted correlation network analysis (WGCNA) is a systems biology method to find highly correlated gene modules, summarize these modules by using module intrinsic genes, and relate the relationships between modules and external sample traits.In this study, WGCNA was used to identify the key modules related to SDHB expression.

2.5 Screening of lncRNA related to SDHB expression

Significant co-expression of lncRNA and SDHB gene (p < 0.0001, |R²| > 0.5) through the Pearson correlation analysis to screen TCGAtranscriptome data.Through the continuous single variable, LASSO and multivariate cox regression analysis to further determine SDHB lncRNA.The correlation between SDHB and lncRNA is shown in the heatmap with the "pheatmap" package.

2.6 Functional Enrichment Analysis

The differentially expressed genes (DEGs) between high-risk and low-risk groups were identified with limma package and used for functional enrichment using gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathways enrichment, with |logFC| > 1 and p < 0.05 as significance threshold. R packages used for results visualization include “scatterplot3d”, “ggplot2”, “circlize”, “ggpubr”, “colorspace”, “stringi”, and “RColorBrewer”.

2.7 Prediction of therapy response

The IC50 of various drugs for each risk group was predicted with “pRRophetic” package and illustrated in bubble plot, scatter plot and box plot with “ggplot2” package.

2.8 Statistical analysis

Wilcoxon test was employed for differential analysis and co-expression analysis. The Spearman method was applied for the analysis of correlation between risk score and immune scores. All hypothesis tests were two-sided, with *p < 0.05, **p < 0.01 and ***p < 0.001. All other statistical analysis and data visualizations were carried out in R software (R version 4.1.2). Adobe Illustrator (CC 2020) was used for image processing.

TCGA transcriptome and clinical data were collected, and 147 PPGL tissue samples and 2 normal tissues were finally included in this study.

3.1 The microenvironment of pheochromocytoma with high SDHB and low SDHB expression

The SDHB high expression group obtained higher stromal cell score and microenvironment score, lower tumor purity score, and no significant difference in immune cell score (Fig. 1A-D). There were significant differences in eosinophil expression in immunocell infiltration in PPGL groups with high and low SDHB expression(Fig. 1E). Mast cells, tumor infiltrating lymphocytes and interleukin Ⅱ were significantly different in SDHB scores, suggesting that the expression level of SDHB in PPGL may be related to the state of immune activation(Fig. 1F).

SDHB high expression group and the low expression of the tumor microenvironment of pheochromocytoma. High SDHB expression group obtained higher stromal cell scores (A) and microenvironment scores (B), lower tumor purity scores (C), but no significant difference in immune cell scores (D). The difference of immune cell infiltration in the high-low SDHB expression group was boxplot (E), SDHB expression and immune cell content correlation (F), dendritic cell content and SDHB expression correlation (G), NK cell content and SDHB expression correlation (H), SDHB expression and immune cell content heat map (I).

In this study, PPGL patients were divided into SDHB high expression group and SDHB low expression group for analysis. The SDHB high expression group obtained higher stromal cell score and microenvironment score, lower tumor purity score, while there was nosignificant difference in immune cell score. This indicates that the tumor composition is more complex and heterogeneous, which may be associated with its poor prognosis, which needs to be further verified.

3.2 PPGL immune cell checkpoint

Having found that SDHB scores were significantly associated with the immune activation status of PPGL, we went on to investigate whether SDHB scores correlated with the expression of key ICGs. Three of the seven key ICGs tested (CD274, CTLA4, HAVCR2, IDO1, BTLA, PDCD1, PDCD1LG2) showed a strong positive correlation with SDHB score (Fig. 2A). Scatter plots of significant associations between PDCD1LG2, BTLA, and HAVCR2 are shown (Figs. 2C-E). In addition, we also examined the expression differences of 10 major ICGs, and the results showed that most ICGs were highly expressed in the SDHB high expression group (Fig. 2B). These results suggest that SDHB score is positively correlated with ICGs expression and may be valuable in predicting the response to immunotherapy in PPGL.

Figure A shows the bubble map of major immune checkpoint differences. Red color indicates a positive correlation, while blue color represents a negative correlation. The darker color intensity and larger circle represent a stronger correlation. *: p < 0.05. Figure B shows the boxplot of major immune checkpoint differences. Figure C - E respectively shows scatter diagram of PDCD1LG2, HAVCR2, BTLA.

3.3 Alternative splicing analysis and network affecting SDHB expression

A total of 40,130 pheochromocytoma related variable shear items were downloaded from TCGA database, and the specific proportion of variable shear types was shown in the up-set plot (Fig. 3A). After using the "limma" package to analyze the differential alternative splicing of SDHB high and low expression groups, 1294 significantly up-regulated and 2547 significantly down-regulated, a total of 3841 significantly different RNA alternative splicing (p. Adjust < 0.05 was the significance threshold) (Fig. 3B). After being divided into 7 groups according to the specific variable splicing category, the top 20 variable splicing with the most significant difference between the high/low SDHB expression samples in each classification group were visualized by bubble plot (Fig. 3C-E, Fig. 4A-D).

Taking the absolute value of correlation coefficient greater than 0.85 and p value less than 0.00001 as the threshold of significant correlation, the co-expression analysis of classical alternative splicing enzymes and 3841 significantly different alternative splicing enzymes obtained in the above study obtained 11 key alternative splicing enzymes with direct significant correlation with each other. Sixteen significantly up-regulated and 43 significantly down-regulated alternative splicing (Fig. 5A). The identified key splicing enzymes included PRMT5, CBP1, HSPA8, EIF3A, DHX9, DDX46, CHERP, ZFR, SRSF9, RNF40 and RAVER1. This splicing enzyme alternative splicing regulatory network may have some effects on the progression and prognosis of pheochromocytoma.

3.4 Co-expression relationship between SDHB and related genes

To identify SDHB-related lncrnas, we performed correlation analysis between all lncrnas and SDHB genes. According to the p < 0.0001 and | | R2 > 0.5 into standard, identified 715 SDHB related lncRNA (Fig. 5B). These results may provide clues as to how the SDHB expression process can be regulated by targeting lncrnas.

3.5 GO/KEGG functional enrichment of SDHB differentially expressed RNA and tumor burden

To explore the potential biological processes and pathways affected by PCGs, we performed functional enrichment analysis with both KEGG and GO terms. KEGG results showed that the differentially expressed genes were closely related to apoptosis and a variety of immune-related pathways (Fig. 6A). Meanwhile, GO analysis showed that differentially expressed genes were involved in vascular smooth muscle proliferation, transcriptional activity, and regulation of oxygen content in PPGL (Fig. 6B, C). These results suggest that SDHB may affect the biological behavior of PPGL by altering the immune response, transcriptional activity, and apoptosis.

In addition, correlation analysis of tumor burden showed that there was no significant difference in tumor burden between the high SDHB expression group and the low SDHB expression group(Fig. 7A).

3.6 Drug prediction analysis of SDHB-related PPGL

There was no significant difference in immune escape between the high and low SDHB expression groups (Fig. 7B).

Previous studies have shown that SDHB levels correlate with treatment response and sensitivity. Therefore, we further used the "pRRophetic" algorithm to compare the sensitivity of commonly used drugs between different risk groups. Some drugs were more sensitive to samples from the low expression group, while others were more effective to samples from the high expression group. The top 10 sensitive drugs of the low and high expression groups are shown in bubble plots (Figs. 8A), and the sensitivity correlation and IC50 differences of the top 3 sensitive drugs are also shown in scatter plots and boxplots (Figs. 8B, C). The samples in the low expression group were more sensitive to NPK-76-Ⅱ-72-1, cyclopamine and sunitinib, and the samples in the high expression group were relatively sensitive to crizotinib, YM155 and JW7-24-1. In addition, given that pazopanib is the most commonly used anti-metastatic PPGL agent, we specifically examined its sensitivity to each risk group. As shown in Figs. 8D, PPGL samples from the high SDHB expression group showed lower IC50 to pazopanib, suggesting that they may be more sensitive to pazopanib. In conclusion, these results can provide some clues for the development of therapeutic strategies for patients with metastatic PPGL.

In this study, PPGL patients were divided into SDHB high expression group and SDHB low expression group for analysis.The SDHB high expression group obtained higher stromal cell score and microenvironment score, lower tumor purity score, while there was no significant difference in immune cell score. This indicates that the tumor composition is more complex and heterogeneous, which may be associated with its poor prognosis, which needs to be further verified.

The results of PDCD1LG2, BTLA, and HAVCR2 suggest that SDHB score is positively correlated with ICGs expression and may be valuable in predicting the response to immunotherapy in PPGL. Among them, PDCD1LG2 is a programmed death ligand 2, which can be used as an immune checkpoint for Hodgkin's lymphoma, primary testicular lymphoma and gastric cancer in the previous literature^[45–47].B and T lymphocyte attenuator BTLA can induce immune suppression by inhibiting the activation and proliferation of B and T cells to mediate the related immune effects^[48].In recent years, a large number of studies have found that BTLA is involved in a variety of physiological and pathological processes, such as tumors, inflammatory diseases, autoimmune diseases, infectious diseases, and transplant rejection^[48–50].HAVCR2 is the hepatitis A virus receptor 2 and is commonly known as T cell immunoglobulin and mucin domain-containing protein 3 TIM3 in terms of immune checkpoints. It was originally identified as a receptor expressed on CD4 + and CD8 + T cells that produce interferon-γ^[51].However, recent studies have shown that TIM3 inhibition enhances the anti-tumor effect of PD1 blockade^[52].

Differential alternative splicing enzymes can regulate the expression of SDHB and provide potential therapeutic targets for treatment.Among them, researchers have found that PRMT5 can structurally and functionally interact with Menin in hereditary multiple endocrine neoplasia type 1 (MEN1) syndrome, acting as a scaffold protein to regulate the cellular homeostasis of endocrine organs^[53].The other alternative splicing enzymes (CBP1, HSPA8, EIF3A, DHX9, DDX46, CHERP, ZFR, SRSF9, RNF40, RAVER1) have not been reported and need further verification.

The differentially expressed genes were involved in vascular smooth muscle proliferation, transcriptional activity and regulation of oxygen content in PPGL. At the same time, KEGG results showed that the differentially expressed genes were closely related to cell apoptosis and a variety of immune-related pathways.These results suggest that SDHB may affect the biological behavior of PPGL by altering the immune response, transcriptional activity and apoptosis.In addition, correlation analysis of tumor burden showed that there was no significant difference in tumor burden between the SDHB high expression group and the SDHB low expression group.

For the potential mechanism of SDHB in PPGL, functional enrichment suggested that the differential expression of SDHB could affect the biological behavior of PPGL through immune response, transcriptional activity and apoptosis.Immune evasion, genomic instability, and tumor promoted inflammation are also hallmarks of cancer.Similarly, the correlation between SDHB genes and lncrnas has been reported in a variety of tumors such as colon adenocarcinoma and osteosarcoma.Similar to these studies, our results provide clues to the potential functional mechanism of the SDHB gene in PPGL. Functional enrichment analysis suggested that altered immune response might be one of the potential mechanisms of SDHB gene in pheochromocytoma/paraganglioma.However, how the differential expression of SDHB gene affects the immune response of PPGL remains unclear.Our immunological correlation analysis showed that samples with high SDHB expression had higher immune scores, promoted macrophage infiltration, widely increased immune function scores, and generally upregulated ICG expression.Interestingly, in addition to tumor-promoting GAM and Treg cells, most other immune functions were also activated in the high SDHB expression group.Perhaps their function is masked by GAM and Treg activation.Alternatively, perhaps these common tumor suppressor immune components are reprogrammed in PPGL to play different biological roles.But all these hypotheses need to be further verified by follow-up experiments.Tumor heterogeneity, including genomic heterogeneity, largely affects the SDHB expression of PPGL.

TMB, the total mutation frequency per tumor sample, is a fundamental type of genomic heterogeneity.Theoretically, higher TMB is associated with a better immunotherapy response and prognosis in patients, in large part due to the potential for immunogenic neoantigens to be generated by tumor mutations^{[54, 55]}.However, our results showed no significant difference in TMB between the high expression group and the low expression group, which seems to contradict previous studies.The main reason for this may be the heterogeneity among different tumor types.The above correlation between TMB and immunotherapy response was mainly observed in lung cancer.However, a pan-cancer study published in Annals of Oncology showed that high TMB was associated with greater immunotherapy sensitivity only in cancers in which CD8 T-cell abundance was positively correlated with neoantigen load, such as melanoma, lung cancer, and bladder cancer.For cancers in which there was no correlation between CD8 T cell levels and neoantigen load, such as breast cancer, prostate cancer, and glioma, higher TMB was instead associated with immune response and tolerance to immunotherapy.

Since TMB did not predict the immune response to PPGL, to explore new treatment options and screen potential synergistic drugs for PPGL, we further attempted to predict the preferential sensitivity of various drugs in different risk groups to PPGL samples.Based on our results, SDHB low expression group tumors may be more sensitive to NPK-76-Ⅱ-72-1, cyclopamine and sunitinib, while SDHB high expression group tumors may be more sensitive to crizotinib, YM155 and JW7-24-1.In addition, pazopanib, a commonly used anti-PPGL drug, also showed preferential sensitivity to SDHB-low expression samples^[56],which is in line with our expectation that low SDHB expression is associated with a worse prognosis^{[22, 23, 57–59]}.NPK-76-Ⅱ-72-1 is a minichromosome maintenance protein 2, which is believed to regulate the expression of PLK3CA, thereby regulating cell proliferation.Cyclopamine can specifically inhibit the Hedgehog pathway and also play an important role as an antagonist of SMO^[60].However, the prospects of cyclopamine as a therapeutic drug are limited, not only because of the acid instability of this small molecule, but also because of the strict concentration requirements of this small molecule^[61].Sunitinib is a selective inhibitor of multiple receptor tyrosine kinases associated with tumor growth and angiogenesis^[62].Sunitinib has been shown to play an important role in the treatment of patients with metastatic PPGL, especially in patients with SDHB mutations^[63].Crizotinib inhibits ROS1, MET and ALK and is considered a first-line treatment for lung cancer^[64].There are no studies on this drug and PPGL.YM155(Sepatronium Bromide) is an inhibitor of Survivin, an anti-apoptotic protein that is overexpressed in cancer cells. It can inhibit apoptosis, promote proliferation and enhance invasion, and is a logical target for potential cancer therapy^{[65, 66]}.By overexpressing survivin, cancer cells can avoid apoptosis and often become resistant to treatment^[67].There are no studies on this drug and PPGL.It is possible that YM 155 is also a DNA damage inducer, introducing another anticancer mechanism, and that the concentration of DNA damage induced is much lower than that required to inhibit survivin^[65].JW7-24-1 can regulate lymphocyte-specific protein tyrosine kinase LCK, which plays a key role in the activation and differentiation of T lymphocytes, and under certain conditions, Lck is also involved in the induction of apoptosis ^[68].Pazopanib is a tyrosine kinase inhibitor that exerts anticancer effects by inhibiting angiogenesis and oncogenic signaling pathways ^[69].Chemotherapy with temozolomide or cyclophosphamide + vincristine + dacarbazine is the first-line treatment option for metastatic PPGL, and other treatments (sunitinib, cabozantinib, everolimus, and PD-1/PDL-1 inhibitors) have shown moderate efficacy^[70].In our study, temozolomide showed no significant difference between SDHB high and low expression groups.PPGL needs individualized treatment, and further research and differentiated treatment methods are needed.Taken together, our findings reveal the possibility of using these drugs in synergy with SDHB to inhibit PPGL growth.

The current study also has some limitations: First, this study is based on bioinformatics analysis, and prospective analysis of real world data is needed to verify the clinical utility of the risk model.In addition, the potential mechanism of SDHB-related PPGL was only explored at the correlation level, and no experimental verification of causality was performed.Further rigorous experimental examination of molecular biology is necessary to elucidate the specific mechanism.

Conflict of Interest

The authors declare no conflicts of interest.

Funding

This study was supported by the Applied Basic Research joint project of Science and Technology Department of Yunnan Province and Kunming Medical University (No. 202364).

Author Contribution

Conceptualization, D.C. and J.F.; software, D.C. and Y.H.; validation, D.C.; formal analysis, Y.H.; investigation, D.C.; resources, Y.H.; data curation, Y.H.; writing—original draft preparation, D.C. and Y.H.; writing—review and editing, D.C. and Y.H.; visualization, D.C.; supervision, D.C. and Y.H.; project administration, J.F.; and funding acquisition, J.F. All authors have read and agreed to the published version of the manuscript.

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No competing interests reported.

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Tumor microenvironment and immune function analysis and chemotherapy prediction of SDHB in PPGL (pheochromocytoma/ paraganglioma)

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Abstract

Background

Method

Result

Conclusion

Figures

1. Background

2. Materials and Methods

2.1 Data acquisition

2.2 Data Processing

2.3 Tumor microenvironment

2.4 Characterization of WGCNA and SDHB scores

2.5 Screening of lncRNA related to SDHB expression

2.6 Functional Enrichment Analysis

2.7 Prediction of therapy response

2.8 Statistical analysis

3. Results

3.1 The microenvironment of pheochromocytoma with high SDHB and low SDHB expression

3.2 PPGL immune cell checkpoint

3.3 Alternative splicing analysis and network affecting SDHB expression

3.4 Co-expression relationship between SDHB and related genes

3.5 GO/KEGG functional enrichment of SDHB differentially expressed RNA and tumor burden

3.6 Drug prediction analysis of SDHB-related PPGL

4. Discussion

5. Conclusion

Declarations

Conflict of Interest

Funding

Author Contribution

References

Additional Declarations

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