Identification of SHC2 as a prognostic biomarker: from pan-cancer analysis to colon adenocarcinoma validation

Identification of SHC2 as a prognostic biomarker: from pan-cancer analysis to colon adenocarcinoma validation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of SHC2 as a prognostic biomarker: from pan-cancer analysis to colon adenocarcinoma validation Huimin Zhang, Ran Tian, Mengzhen Wu, Jianheng Peng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8169608/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Colon adenocarcinoma (COAD) is a malignant neoplasm of the digestive system characterized by a high incidence and significant mortality on a global scale. The majority of patients are diagnosed at intermediate to advanced stages of the disease. SHC-transforming protein 2 (SHC2), a member of the SHC family, has little known about its function in a variety of cancers, including COAD. This investigation thoroughly examined the expression characteristics, prognostic value, genomic variation and methylation patterns of SHC2 in pancarcinoma and discussed its correlation with tumor immune cell infiltration. Furthermore, we verified that SHC2 expression was elevated in COAD cell lines and that lowering SHC2 expression levels significantly reduced the invasion, migration and proliferation ability of COAD cell lines. Our results revealed that SHC2 is significantly dysregulated in COAD and that its expression level is closely related to patient prognosis, immune microenvironment remodeling, and drug sensitivity and affects tumor progression by regulating key signaling pathways. This study systematically reveals the value of SHC2 as a therapeutic target and prognostic indicator for COAD, offering a theoretical foundation for a thorough investigation of its molecular mechanism. Colon adenocarcinoma SHC2 prognosis and treatment biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1 Introduction Colon adenocarcinoma (COAD) is among the most prevalent malignant tumors of the digestive system worldwide and poses a significant threat to patient morbidity and mortality ( 1 ). Globally, it is the third most prevalent cancer and the second leading cause of death ( 2 ), and the 5-year relative survival rate of patients with colon adenocarcinoma is only 65% ( 3 ). The occurrence of colon adenocarcinoma is affected by multiple factors, such as genetics, the environment and lifestyle ( 4 ). The tumor microenvironment, immune cell infiltration and epigenetic changes play key roles in tumor progression and treatment ( 5 , 6 ). Consequently, it is crucial to discover new biomarkers to improve the prognosis of patients with colon adenocarcinoma and refine treatment choices. SHC-transforming protein 2 (SHC2) is a gene product composed of 582 amino acids ( 7 ) and belongs to the SHC family. In addition to SHC2, the SHC family also includes SHC1, SHC3 and SHC4. Their main function is to serve as adaptor proteins to conduct upstream signals and further activate downstream pathways. They can participate in the pathways of growth factors, especially in the MAPK and PI3K/Akt pathways ( 8 ). Among them, SHC1 and SHC3 have been found to have a significant impact on various cancers, such as hepatocellular carcinoma ( 9 ), breast cancer ( 10 ) and lung cancer ( 11 ). Therefore, the SHC family can serve as an effective target for tumor prognosis and treatment. However, the function of SHC2 in various cancers, including colorectal adenocarcinoma, remains largely unexplored. This research involved extracting data from the TCGA databases to conduct a detailed analysis of tumor and normal tissues: ( 1 ) A comparison of the expression of SHC2 in tumor tissue and normal tissues was performed; ( 2 ) a thorough prognostic analysis was conducted using survival and expression data from each tumor patient in the TCGA database; ( 3 ) a detailed analysis of the copy number variation (CNV), single nucleotide site variation (SNV), methylation levels, and prognostic levels of tumor patients under different expression patterns in the TCGA database was performed; ( 4 ) an extensive study was performed to examine the relationship between SHC2 expression in entire cancer and the infiltration of immune cells in tumor tissues; ( 5 ) the GDSC and CTRP drug sensitivity data were obtained from the GSCA, and the relationship between SHC2 expression and drug sensitivity across all cancer types was examined; ( 6 ) Performing spatial transcriptomics analysis on COAD tissue sections using the Sparkle database to investigate the relationship between SHC2 expression and tumor cell distribution; ( 7 ) GO and KEGG enrichment analyses were used to identify the functional changes and pathway effects caused by differential genes related to SHC2; and ( 8 ) using These data provide a theoretical basis for exploring the nature of SHC2 in tumors and provide new ideas for further study of the mechanism of action of SHC2 in COAD. 2 Methods 2.1 SHC2 expression in human cancer SHC2 mRNA expression data and all clinical data were downloaded from The Cancer Genome Atlas (TCGA, https://www.cancer.gov/)database s. We analyzed SHC2 mRNA expression in human cancer tissues and their corresponding paraneoplastic healthy tissues in the TCGA database via the TIMER2.0 database ( http://timer.comp-genomics.org/ ) ( 12 ). In addition, by integrating the TCGA datasets, we further explored the expression profiles of SHC2 across cancer types in depth. All tumor abbreviations are listed in Table 1 . Table 1 Abbreviations of the tumors from TCGA database. Tumor name Abbreviations Adrenocortical Cancer ACC Bladder Cancer BLCA Breast Cancer BRCA Cervical Cancer CESC Bile Duct Cancer CHOL Large B-cell lymphoma DLBC Esophageal Cancer ESCA Glioblastoma GBM Head and neck cancer HNSC Kidney chromophobe KICH Kidney clear cell carcinoma KIRC Kidney papillary cell carcinoma KIRP Acute Myeloid Leukemia LAML Lower Grade Glioma LGG Liver cancer LIHC Lung adenocarcinoma LUAD Lung squamous cell carcinoma LUSC Mesothelioma MESO Ovarian cancer OV Pancreatic Cancer PAAD Prostate Cancer PRAD Pheochromocytoma and paraganglioma PCPG Rectal cancer READ Sarcoma SARC Melanoma SKCM Stomach cancer STAD Testicular Cancer TGCT Thyroid Cancer THCA Thymoma THYM Endometrioid cancer UCEC Uterine Carcinosarcoma UCS Ocular melanomas UVM 2.2 Clinical data and prognostic significance of SHC2 in human cancer We analyzed the TCGA database to explore how SHC2 expression levels are related to clinicopathological characteristics in pancancer patients and further investigated the relationships between SHC2 expression and overall survival (OS), disease-specific survival (DSS) and the progression-free interval (PFI) of pancancer patients ( 13 ). 2.3 Mutation and methylation analysis of SHC2 SHC2 genetic variations across cancers were identified via cBioPortal ( https://www.cbioportal.org/ ) ( 14 ). We examined CNVs, SNVs and methylation of the SHC2 gene across cancers and their correlations with gene expression and survival via the GSCA database ( 15 ). 2.4 Drug sensitivity analysis of SHC2 We explored the associations between SHC2 gene expression levels and the IC50, an indicator of drug sensitivity, in the Genomics Database for Cancer Drug Sensitivity (GDSC) and Cancer Treatment Response Portal (CTRP) databases at the pancancer level via the GSCA ( http://bioinfo.life.hust.edu.cn/GSCA/#/drug ) platform ( 16 ). 2.5 Differential expression analysis and gene set enrichment analysis We analyzed the differential expression of SHC2 via DESeq2 and edgeR differential analysis tools in R software and screened the differentially expressed molecules on the basis of the significance thresholds and expression fold difference thresholds. Using the TCGA database, the relationships between SHC2 and other mRNAs and lncRNAs were examined. GO and KEGG pathway enrichment analyses were subsequently performed on the significantly coexpressed genes via the clusterProfiler functional annotation toolkit in R software. 2.6 Transcriptional landscape of COAD This study used the Sparkle database ( https://www.grswsci.top ) to conduct spatial transcriptome analysis of colon cancer tissue sections. The Sparkle database integrates 10xVisium sequencing data and builds a pan-cancer spatial transcriptome map based on previous research( 17 , 18 ), providing comprehensive data support for the analysis of the colon cancer microenvironment. During the analysis, the spatial transcriptome data was first visualized through the SpatialPlot function in the Seurat package to display the largest cell types in each microspot and their spatial distribution. Subsequently, Spearman correlation analysis method was used to calculate the correlation between cell content and cell content in all spots, as well as the correlation between cell content and gene expression. In order to present the correlation results more intuitively, the study used the linkET package for visualization, and comprehensively displayed the intensity, direction and significance of the correlation through multi-dimensional information such as color coding and graphic size. 2.7 Cell lines and cell culture The HCT116 and RKO cell lines were purchased from the cell bank of the Chinese Academy of Sciences and cultured in RPMI 1640 medium (Thermo Fisher, 12633020) supplemented with 10% fetal bovine serum (Gibco, 16010159) and 1% penicillin/streptomycin double antibody solution. The cells were grown in a thermostat incubator at 37°C with saturated humidity of 5% CO 2 . 2.8 Transfection The cells were inoculated in six-well plates, the serum-free medium was replaced when the cell density was moderate, the lip3000 (Thermo Fisher, L3000015) and siRNA complexes were added dropwise to the six-well dishes, the dishes were gently shaken, and subsequent experiments were carried out after 24 h of incubation. si-SHC2 (si-SHC2#1:5- GCACAAUUACUACAACAGCAUCCCG-3; si-SHC2#2:5-CUCAAGACAGUGGAUAUCGAGGCA − 3) was purchased from GenePharma. 2.9 Western blot assays Collect cell samples and extract total protein. After determining the protein concentration, load the samples into the gel wells and run the gel under constant voltage or constant current conditions. Subsequently, transfer the electrophoretically separated proteins from the gel onto a PVDF membrane under an electric field. Then, block the protein bands at room temperature using 5% skim milk powder in blocking buffer. The PVDF membrane regions corresponding to the target protein and the reference protein were excised separately. The excised membrane strips were incubated overnight with primary antibodies specific to the target protein: SHC2 (Abcam, ab243385) and beta-actin (Abcam, ab8227). After washing, the membranes were incubated with a secondary antibody diluent (Abcam, ab205718). Following the removal of unbound secondary antibody, color development and imaging were performed. 2.10 CCK-8 assays We digested and counted the cells, inoculated them into 96-well plates at a density of approximately 1000 cells per well, and incubated them in an incubator for 4 h. After the cells had adhered to the wall, we introduced 10 µL of CCK-8 reagent (Yeasen, 40203ES76) into each well and allowed them to incubate for 1 h, protected them from light, and then measured the absorbance values of the wells at 450 nm using an enzyme marker. 2.11 Colony formation experiment The cells were digested, counted, inoculated into 6-well plates at a density of approximately 500 cells per well, and then cultured in an incubator for 14 days. Once the incubation was complete, the medium was discarded, and the plates were washed with PBS. Then, the cells were fixed with 4% paraformaldehyde. Following fixation, the cells were treated with crystal violet solution and then rinsed to eliminate any surplus dye. Finally, the formed cell clones were photographed and counted. 2.12 Transwell assays We digested and counted the cells and inoculated them into Transwell chambers (Corning, 3460) filled with serum-free medium. The chambers were subsequently placed in 24-well plates, and 500 µL of complete medium was added to the lower chambers before they were incubated for 24 or 48 hours. At the end of the incubation, the chambers were removed, washed to remove nonmigrating cells, fixed with 4% formaldehyde, and stained with 0.1% crystal violet. Finally, the chambers were rinsed, and the cells that had migrated to the undersurface of the membrane were photographed and counted under a light microscope. 2.13 Statistical analysis The differences between groups were statistically analyzed via Student's t test, with a P value of less than 0.05 indicating statistical significance. 3 Results 3.1 SHC2 expression in human cancer We analyzed the TCGA database to investigate the expression levels of SHC2 in various human cancers. Our results revealed that SHC2 expression was upregulated in seven human cancers, namely, CHOL, COAD, KIRC, KIRP, LIHC, PCPG, and PRAD, and that SHC2 expression was downregulated in five human cancers, namely, BLCA, BRCA, CESC, KICH, and LUSC, as shown in Fig. 1 A. In addition, compared with that in healthy tissues, the expression of SHC2 was markedly greater in paired samples from five cancers: CHOL, COAD, KIRC, KIRP, and PRAD (Fig. 1 B). These findings suggest that SHC2 may play different roles in different tumor types. 3.2 Correlations between SHC2 expression and the clinical characteristics of patients with human cancer We subsequently investigated how SHC2 is related to the clinical characteristics of pancancer, such as T stage, N stage and M stage. SHC2 expression was significantly correlated with the T stage of several cancers, and in COAD, the expression level of SHC2 increased with increasing T stage; however, in LUAD, BRCA, and PAAD, the expression level of SHC2 decreased with increasing T stage (Fig. 2 A). In addition, SHC2 expression was significantly correlated with N stage in COAD, BRCA, KIRP and READ, and the expression level of SHC2 increased with increasing N stage (Fig. 2 B). Similarly, there was a notable correlation between SHC2 expression and M stage across various cancers. In THCA, TGCA and BLCA, the expression level of SHC2 increased with increasing M stage; however, in KIRC, the expression level of SHC2 decreased with increasing M stage (Fig. 2 C). Our results suggest that the expression level of SHC2 is correlated differently with the clinical characteristics of different tumors. 3.3 SHC2 expression correlates with prognosis in human cancer To determine the role of SHC2 expression in cancer patient prognosis, we accessed TCGA clinical data to analyze its correlation with OS, DSS, and PFI in 33 types of cancer. Our research indicated that elevated SHC2 expression was associated with improved overall survival in patients with ACC, LGG, and PAAD, whereas it was linked to poorer overall survival in those with COAD (Fig. 3 A). To exclude bias caused by nontumor events, we further assessed the effects of SHC2 expression levels on DSS and PFI. Our findings indicated that elevated SHC2 expression was linked to poorer DSS in COAD, STAD, and UVM patients, whereas it was associated with better DSS in ACC patients (Fig. 3 B). In addition, elevated SHC2 expression was negatively correlated with patient PFI in COAD, PRAD, and STAD. Conversely, in patients with LGG and PAAD, high SHC2 expression was positively correlated with the PFI (Fig. 3 C). These findings indicate a strong correlation between SHC2 expression levels and patient prognosis across various tumor types, with high SHC2 expression generally linked to poor outcomes in COAD patients. 3.4 Genetic variation in SHC2 and differences in survival To study the correlation between SHC2 genetic variants in various cancers, the cBioPortal tool was used to identify genetic alterations in the data extracted from the TCGA dataset. The types of SHC2 mutations, loci, and number of cases are shown in Fig. 4 A. The major type of genetic alteration in SHC2 is missense mutation. Next, we analyzed the heatmap of the percentage of SNVs in SHC2 in different cancers, and the results revealed that SNVs in SHC2 occurred mainly in UCEC, SKCM, COAD, LUSC, and HNSC and were more prevalent in KIRP, CESC, BLCA, LUAD, STAD, READ, PAAD, BRCA, LIHC, KIRC, GBM, PRAD and LGG, with few SNVs, and no SNVs were found in ESCA (Fig. 4 B). The CNVs of SHC2 across cancers were subsequently evaluated, and we detected differential deletion and amplification of SHC2 in various cancers, with a predominance of amplification mutations in ACC, GMB, KICH, LGG, PCPG, SARC, DLBC, KIRC, MESO, COAD, THYM, and THCA but with varying levels of deletion mutations. In LAML, UVM, READ, CHOL, KIRP, PRAD, TGCT, LIHC, PAAD, SKCM, BRCA, BLCA, UCEC, CESC, STAD, HNSC, LUSC, ESCA, LUAD, UCS, and OV, deletion mutations were predominant, but amplifying mutations were still present to varying degrees (Fig. 4 C). In addition, we examined the survival disparities between the SHC2 genomic mutation group and the WT group in these cancers via progression-free survival (PFS), disease-free interval (DFI), OS, and DSS analyses (Fig. 4 D). Finally, the results of CNV and survival analyses of SHC2 showed significant differences in OS for KIRC, UCEC, CESC, KIRP, KICH, and HNSC; significant differences in PFS for PRAD, KIRC, UCEC, CESC, KIRP, KICH, COAD, LUSC, and SARC; and significant differences in DSS for KIRC, UCEC, CESC, KIRP, KICH, and THCA; and significant differences in DFI for KIRP, DLBC, UCEC, UCEC, and COAD (Fig. 4 E). These findings emphasize that genetic alterations may influence the expression pattern of SHC2 in various tumor types as well as the prognosis of patients with tumors. 3.5 DNA methylation of SHC2 in human cancer DNA methylation, a vital epigenetic alteration ( 19 ) influences gene expression and is strongly correlated with tumor prognosis and the effectiveness of treatment ( 20 ). Our findings indicated that the level of DNA methylation aggregation in SHC2 was notably lower in the majority of cancer tissues than in normal tissues (Fig. 5 A). In KIRC and KIRP tumor tissues, the CpG aggregation methylation level of CASZ 1 was notably greater than that in normal tissues. To explore further the connection between the SHC2 methylation level and SHC2 mRNA expression, our results from the GSCA revealed that the RNA expression of SHC2 was associated with methylation in most cancers except LUSC, GBM, ESCA, READ, UCS and OV, especially in SARC and KIRP, where the SHC2 DNA methylation level was strongly negatively correlated with SHC2 mRNA expression (Fig. 5 B). After that, we categorized 33 tumor patients on the basis of the methylation status of SHC2 and analyzed the differences in DSS and OS among the tumor patients, which revealed that SHC2 hypermethylated patients were significantly different from hypomethylated patients in terms of DSS in SKCM, HNSC and KIRP, and SHC2 hypermethylated patients were significantly different from hypomethylated patients in terms of OS in SKCM (Fig. 5 C), suggesting that SHC2 methylation is associated with a survival advantage in cancer patients. 3.6 Analysis of the immune correlation between SHC2 and human cancer We utilized the xCell and TIMER algorithms to investigate the relationship between SHC2 expression and immune cell infiltration across various cancers, focusing on the correlation between SHC2 expression and immune cell presence in tumor tissues. The xCell algorithm was used to explore the relationships between SHC2 expression and different types of infiltrating immune cells. The figure indicates a significant correlation between SHC2 expression and the level of infiltrating immune cells across most cancer types, especially showing a strong positive correlation with most immune cells in CHOL, SARC, SKCM, TGCT, THCA and THYM. In most cancer cases, there was a positive correlation between SHC2 expression and both T-cell CD 4+ Th2 and common lymphoid progenitor cells, whereas T-cell CD 4+ central memory cells were negatively correlated (Fig. 6 A). We employed the TIMER algorithm to explore the relationships between SHC2 expression and different types of immune cells. The results revealed a strong link between SHC2 expression and the infiltration of immune cells such as B cells, macrophages, neutrophils, myeloid dendritic cells, CD 8+ T cells, and CD 4+ T cells. Among these genes, SHC2 was significantly and positively correlated with the infiltration of various immune cell types in LGG, PRAD, SARC, TGCT and THCA tissues (Fig. 6 B). To investigate the correlation between the expression of specific gene SHC2 and immune-related characteristics, we used Spearman correlation analysis method. We found significant positive correlations between SHC2 gene expression and multiple immune-related characteristics. Heat map results showed that the expression of a large number of immune-related characteristics was highly positively correlated with the expression of SHC2 gene, indicating that SHC2 may play an important role in the process of immune regulation. These findings help further explore the potential of SHC2 as an immunotherapy target and provide new ideas for the treatment of related diseases (Fig. 7 ). 3.7 Cancer drug sensitivity analysis of SHC2 The sensitivity of tumor cells to chemotherapeutic drugs may play a key role in drug resistance in tumors, so we analyzed the correlation between SHC2 expression and drug sensitivity across all cancers. For GSCA, we gathered GDSC and CTRP drug sensitivity data and evaluated their relationships with SHC2 expression. According to the CTRP database, SHC2 expression was positively correlated with the following drugs: clofarabine, dasatinib, BRD-K63431240, bosutinib, and zebularine but negatively correlated with SB-525334 (Fig. 8 A). According to the GDSC database, there was a positive correlation between SHC2 expression and the drugs z-LLNle-CHO, sunitinib, bosutinib, AICAR and AZD7762 but a negative correlation with SB52334 (Fig. 8 B). These findings indicate that SHC2 is strongly linked to drug sensitivity across various tumor cell lines and could be a promising target for cancer treatment. 3.8 Spatial transcriptomic sections analysis of SHC2 in COAD To further study the expression of the SHC2 gene in COAD, we used the Sparkle database ( https://www.grswsci.top ) to perform spatial transcriptome analysis on colon cancer tissue sections (Fig. 9 A-C). Through the visualization results of the Spatial Feature Plot function, it was found that the expression of SHC2 gene was significantly similar to the distribution of tumor cells. Specifically, the high-expression region of the SHC2 gene highly overlaps with the microdomains where tumor cells are located, while the expression of SHC2 in normal tissue regions is relatively low (Fig. 9 D-E). In addition, the expression of SHC2 gene was significantly positively correlated with the content of tumor cells in the spot, which was highly consistent with previous cell localization analysis (Fig. 9 F). This result suggests that in COAD, the SHC2 gene may be mainly expressed by tumor cells, and its expression level may be closely related to the biological behavior of the tumor. 3.9 Biological functions of SHC2 in COAD To explore potential therapeutic strategies for different SHC2 expression patterns in COAD, we functionally annotated SHC2-associated differentially expressed genes (DEGs) and identified a total of 3535 DEGs (mRNAs and lncRNAs) (Fig. 10 A-B). KEGG analysis revealed that the DEGs were chiefly involved in olfactory transduction, neuroactive ligand‒receptor interaction, taste transduction, protein digestion and absorption, salivary secretion, steroid hormone biosynthesis, mucin type 0-glycan biosynthesis, the renin‒angiotensin system, the early-onset diabetes of the young, and ascorbate and aldarate metabolism (Fig. 10 C). According to the GO enrichment results, the DEGs were chiefly involved in the following processes: the system process, G protein-coupled receptor signaling pathway, detection of stimulus, sensory perception of chemical stimulus, detection of chemical stimulus, detection of stimulus involved in sensory perception, G protein-coupled receptor activity, detection of chemical stimulus involved in sensory perception, and the nucleosome and DNA packaging complex (Fig. 10 D). 3.10 Downregulation of SHC2 suppresses the malignant phenotype of COAD To explore and validate the biological function of SHC2 in the development of COAD, we constructed an SHC2 knockdown cell model to downregulate the expression of SHC2 in HCT116 and RKO cells and verified the knockdown effect via western blotting (Fig. 11 A). We determined that the downregulation of SHC2 decreased the proliferative capacity of HCT116 and RKO cells, as determined by a CCK-8 assay (Fig. 11 B). We further found that a decrease in SHC2 inhibited the colony formation ability of HCT116 and RKO cells (Fig. 11 C). Transwell assays revealed that decreased SHC2 expression inhibited the migration and invasion ability of HCT116 and RKO cells (Fig. 11 D). These results imply that SHC2 functions as an oncogene in COAD. 4 Discussion With progress in tumor molecular biology, molecular targeted therapy has become a new means and method of tumor treatment because of its advantages of strong specificity, obvious efficacy, and minimal damage to normal tissues. In the treatment of cancer patients, targeted therapy can improve their prognosis ( 21 , 22 ). Currently, the morbidity and mortality of colon adenocarcinoma patients are still very high. Therefore, it is important to find novel target molecules for the treatment of patients with colon adenocarcinoma to improve their prognosis. In our study, SHC2 expression was found to be upregulated in seven human cancers, namely, CHOL, COAD, KIRC, KIRP, LIHC, PCPG and PRAD. The expression of SHC2 in paired samples was significantly greater in five cancers, CHOL, COAD, KIRC, KIRP and PRAD, than in healthy tissues. In addition, clinicopathologic data revealed that in COAD patients, the expression level of SHC2 increased with increasing T stage. The expression level of SHC2 increased with increasing N stage in COAD, BRCA, KIRP and READ patients. In THCA, TGCA and BLCA patients, the expression level of SHC2 increased with increasing M stage. Analysis of patient prognostic data revealed that in COAD, OS decreased in patients with high SHC2 expression. In COAD, STAD and UVM, DSS decreased in patients with high SHC2 expression. In addition, in COAD, PRAD and STAD, the PFI decreased in patients with high SHC2 expression. In summary, SHC2 is highly expressed specifically in COAD, and its expression increases with increasing severity of tumor invasion (T stage) and lymph node metastasis (N stage). SHC2 is an independent risk factor for shorter OS, DSS and PFI in patients. This evidence strongly suggests that SHC2 is a key promoter of the development of COAD, invasion and metastasis, is an important poor prognostic biomarker in tumors, and has the potential to be a therapeutic target. Genomic variants are characteristic of cancer and accumulate with cancer progression, providing important clues to the cause and prognosis of cancer ( 23 ). Our study revealed a correlation between SHC2 expression and genomic variants such as CNV, SNV and methylation. This research examined the survival differences between SHC2 mutants and wild-type SHC2, the CNV of SHC2 and its impact on survival, the relationship between SHC2 methylation and mRNA expression levels, and the survival differences between SHC2 hypomethylation and hypermethylation in human cancer. Our study confirmed that SHC2 is an important molecule with high-frequency genetic alterations in a wide range of cancers that are regulated by DNA methylation and that its molecular status (genetic variation, methylation level) can significantly affect the survival prognosis of cancer patients. It is both a potential oncogenic driver and a valuable biomarker for prognostic assessment. The tumor microenvironment is vital for tumor growth and spread, and its ability to suppress the immune system is a key factor in the ineffectiveness of antitumor treatments ( 24 ). Immune cells infiltrating tumors have a major impact on the genesis and progression of cancer ( 25 ), and choosing suitable and individualized immunotherapy methods requires a thorough understanding of the immune infiltration status of patients with tumors. Currently, data concerning the role of SHC2 in the immune system of cancer patients are scarce, and more research into its function in tumor immunity is needed. In our study, in various tumor types, notable links were found between SHC2 expression and the levels of various infiltrating immune cells and their subtypes, such as B cells, macrophages, DCs, neutrophils, CD 8+ T cells, and CD 4+ T cells. These findings suggest that SHC2 may play an important role in immune regulation, suggesting that SHC2 may be an important node of the immune regulatory pathway and a potential immunotherapeutic target in cancer. The low 5-year survival rate of cancer patients is largely due to drug resistance ( 26 ), making the enhancement of drug sensitivity a crucial topic in oncology. Our research indicated that the expression of the SHC2 gene was linked to chemotherapy sensitivity, and in the CTRP database, SHC2 expression was positively correlated with several drugs, especially clofarabine, dasatinib, BRD-K63431240 and zebularine, but was negatively correlated with SB-525334. In the GDSC database, there was a positive correlation between SHC2 expression and several drugs, especially Z-LLNle-CHO, but a negative correlation with SB52334. Owing to the variability among tumor cells, the relationship between SHC2 and various drugs across different tumors differs slightly, potentially affecting the treatment of these tumors. These findings indicate that SHC2 is strongly correlated with drug sensitivity across various tumor cell lines and could be a potential target for cancer treatment. These findings indicate that SHC2 is crucial in tumor progression because it influences genetic changes, tumor immunity, and the tumor microenvironment. Simultaneously, SHC2 can influence the effectiveness of antitumor drugs in chemotherapy and immunotherapy, offering fresh perspectives and targets for cancer treatment. In addition, the DEGs associated with SHC2 were analyzed in this study via GO and KEGG analyses. The KEGG enrichment revealed that the DEGs were involved mainly in olfactory transduction, neuroactive ligand‒receptor interactions, gustatory transduction, protein digestion and absorption, salivary secretion, steroid hormone biosynthesis, mucin-type O-linked glycosylation biosynthesis, the renin‒angiotensin system, youth-onset diabetes and ascorbate and aldose metabolism. The GO results indicate that the DEGs are involved primarily in systemic processes, G protein-coupled receptor signaling pathways, G protein-coupled receptor activity, stimulus detection, chemosensory perception, chemical stimulus detection, stimulus detection involving sensory perception, chemical stimulus detection involving chemosensory perception, nucleosomes, and DNA packaging complexes. Earlier research has shown that these biological processes are intimately linked to the development of tumors. For example, earlier research revealed that the neuroactive ligand‒receptor interaction pathway is linked to the prognosis and response to immunotherapy in patients with COAD. Genes in this pathway, such as LTB 4 R2, could be used as novel targets for colon cancer therapeutic development ( 27 ). Disturbances in neuroactive ligand‒receptor interactions and the renin‒angiotensin system pathway have been associated with cancer-related cognitive impairment ( 28 ). In addition, numerous experimental studies have demonstrated that angiotensin receptor blockers (ARBs) can inhibit the proliferative capacity of breast cancer cells ( 29 ), inhibit the invasion ability of intrahepatic cholangiocarcinoma cells ( 30 ), inhibit liver metastasis of colorectal cancer by increasing the anti-angiogenic effect of bevacizumab ( 31 ), and improve the survival of patients with non-small cell lung cancer and pancreatic cancer ( 32 , 33 ). And it can promote apoptosis of nasopharyngeal cancer(NPC) cells through the PI3 K/AKT signaling pathway, thereby improving the survival rate of NPC patients and inhibiting tumor growth ( 34 ). Additionally, a significant amount of research has indicated that steroid hormones drive malignant changes in tumors such as breast cancer ( 35 ), prostate cancer ( 36 ), and bladder cancer ( 37 ). These studies suggest that genes differentially expressed in SHC2 may be involved in multiple biological processes related to tumor development. In addition, the results of the cell function experiment revealed that the knockdown of SHC2 expression significantly reduced the proliferation, migration and invasion ability of COAD cells. Evidence suggests that SHC2 acts as an oncogene in COAD. According to our study, targeting SHC2 may provide a novel pathway for the treatment of COAD, which is expected to improve patient prognosis by enhancing the immune system and preventing tumor growth. This research represents the first comprehensive analysis of SHC2 expression patterns, genomic mutations, methylation levels, the tumor microenvironment, immune infiltration, and drug sensitivity across various cancers and clarifies the biological role of SHC2 in COAD through cell function experiments. This study extends our understanding of SHC2 across cancers, including COAD, but has several limitations. Such as the lack of clinical and animal experimental data. Therefore, we will further investigate the predictive value and biological role of SHC2 in clinical pathological samples and in vivo COAD models in future research.Future studies should investigate how SHC2 affects immune infiltration and gene modification in COAD and the multiple mechanisms by which SHC2 regulates COAD development. 5 Conclusions In conclusion, our findings revealed that SHC2 expression is elevated in COAD cell lines. Furthermore, SHC2 knockdown inhibited the proliferation, migration, and invasion of COAD cells. These results indicate that SHC2 might act as a molecular marker for patients with tumors and could be targeted in the clinical diagnosis and treatment of COAD. Abbreviations COAD Colon adenocarcinoma SHC2 SHC-transforming protein 2 CNV copy number variation SNV single nucleotide site variation TCGA The Cancer Genome Atlas OS overall survival DSS disease-specific surviva PFI progression-free interval GDSC Genomics Database for Cancer Drug Sensitivity CTRP Cancer Treatment Response Portal PFS progression-free survival DFI disease-free interval DEGs differentially expressed genes ACC Adrenocortical Cancer BLCA Bladder Cancer BRCA Breast Cancer CESC Cervical Cancer CHOL Bile Duct Cancer DLBC Large B-cell lymphoma ESCA Esophageal Cancer GBM Glioblastoma HNSC Head and neck cancer KICH Kidney chromophobe KIRC Kidney clear cell carcinoma KIRP Kidney papillary cell carcinoma LAML Acute Myeloid Leukemia LGG Lower Grade Glioma LIHC Liver cancer LUAD Lung adenocarcinoma LUSC Lung squamous cell carcinoma MESO Mesothelioma OV Ovarian cancer PAAD Pancreatic Can PRAD Prostate Cancer PCPG Pheochromocytoma and paraganglioma READ Rectal cancer SARC Sarcoma SKCM Melanoma STADZ Stomach cancer TGCT Testicular Cancer NPC nasopharyngeal cancer. Declarations Acknowledgement We thank all the databases that participated in this study. Author contributions All authors participated in the present study. JP elaborated the initial experimental design; HZ and RT performed the experiments; HZ, RT, and MW conducted the data analysis; HZ wrote the manuscript; JP reviewed the final manuscript. All authors read and approved the final manuscript. Funding This research was supported by the Key Research and Development Projects in Lvliang City (NO.2023SHFZ47). Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Declarations Ethics approval，consent to participate and consent for publication Not applicable. Competing interests Authors declare no conflict of interest. References Jia SN, Han YB, Yang R, Yang ZC. Chemokines in colon cancer progression. Semin Cancer Biol. 2022;86(Pt 3):400–7. 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J Steroid Biochem Mol Biol. 2020;196:105499. Yang L, Huang W, Bai X, Wang H, Wang X, Xiao H et al. Androgen dihydrotestosterone promotes bladder cancer cell proliferation and invasion via EPPK1-mediated MAPK/JUP signalling. Cell Death Dis 2023 June 16;14(6):363. Additional Declarations No competing interests reported. Supplementary Files Westernblot.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 09 Jan, 2026 Reviewers invited by journal 07 Jan, 2026 Editor assigned by journal 05 Jan, 2026 Editor invited by journal 16 Dec, 2025 Submission checks completed at journal 29 Nov, 2025 First submitted to journal 29 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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05:56:43","extension":"html","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":128511,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8169608/v1/be0a602560d8dde57199fc74.html"},{"id":100008811,"identity":"0b37e721-9df7-4713-acd5-8b8ad0c523c5","added_by":"auto","created_at":"2026-01-12 05:56:50","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":503483,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSHC2 was differentially expressed between tumor tissues and NMTs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) SHC2 expression in human cancer specimens derived from the TCGA database. (B) An analysis of SHC2 expression in cancerous tissues and their corresponding adjacent NMTs was conducted via data from TCGA. ns: \u003cem\u003eP\u003c/em\u003e \u0026gt;0.05; *: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8169608/v1/a2646321ee205cd09b38c364.jpeg"},{"id":100008772,"identity":"b80a8261-b0f8-4070-97bf-8736159b83ac","added_by":"auto","created_at":"2026-01-12 05:56:42","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1011007,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of SHC2 with clinical features across various cancer types.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Relationshipsbetween the expression level of SHC2 and the T stage across cancers. (B) Relationshipsbetween the expression level of SHC2 and the N stage across cancers. (C) Relationship between the expression level of SHC2 and the M-stage of pancancer. *: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8169608/v1/596e79e34ce7e04d63c51e9d.jpeg"},{"id":100008776,"identity":"b7595648-931f-4c4b-8178-24a4305def0b","added_by":"auto","created_at":"2026-01-12 05:56:43","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1096028,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between SHC2 expression and the prognosis of cancer patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Correlation between SHC2 levels and OS in patients with various cancers. (B) Correlation between SHC2 levels and DSS in patients with various cancers. (C) Correlation between SHC2 levels and the PFI in patients with various cancers. *: P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8169608/v1/c96819bda0e2735f2fd39afd.jpeg"},{"id":100361015,"identity":"7912aef8-ba57-46e8-8170-e2fd28cd4d6f","added_by":"auto","created_at":"2026-01-16 07:44:18","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":665613,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacteristics of SHC2 genetic changes in different types of cancer.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The alteration frequencies and mutation sites of SHC2. (B) SNV frequency of SHC2 across cancers. (C) Variation in SHC2 copy number in different cancers. (D) Survival differencesbetween the gene set mutants and WT. (E) Survival differencesbetween different CNV groups.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8169608/v1/46c60d5037920137f4d6d988.jpeg"},{"id":100008791,"identity":"7cce2266-ea07-43a1-80fd-44b183287165","added_by":"auto","created_at":"2026-01-12 05:56:43","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1234170,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMethylation analysis of SHC2.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Correlation between the methylation of SHC2 and its expression. (B) Correlationsbetween SHC2 mRNA levels and methylation status. (C) Variations in survival rates between tumors with high and low methylation levels. ns: \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05; *: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; ****: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8169608/v1/dd8bbbe0cf2fd08722f219a9.jpeg"},{"id":100008809,"identity":"0e445e95-1fc7-42f9-bec9-43727b1e16a5","added_by":"auto","created_at":"2026-01-12 05:56:44","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":893343,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationshipbetween SHC2 expression and the infiltration of immune cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The xCell algorithm illustrates the link between SHC2 expression and immune cell infiltration in different types of cancer. (B) TIMER algorithm revealing the association between SHC2 expression and the infiltration of immune cells in different types of cancer. *: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8169608/v1/b4917f786f1b2d24f6a048c7.jpeg"},{"id":100361318,"identity":"7b273a31-5331-49f5-ad62-9b500fa70b42","added_by":"auto","created_at":"2026-01-16 07:44:54","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3250868,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between SHC2 expression and immune cell infiltration.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8169608/v1/44351f61eb0a8a44ae2f31a5.jpeg"},{"id":100008802,"identity":"2add7fba-41fd-4ea2-ba7e-2d214d283fe4","added_by":"auto","created_at":"2026-01-12 05:56:44","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":864366,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of the correlation between SHC2 expression and drug sensitivity across different types of cancer.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The CTRP database was used to analyze the correlation between SHC2 expression and drug sensitivity in multiple cancer types. (B) The GDSC database was used to analyze the correlation between SHC2 expression and drug sensitivity in multiple cancers.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8169608/v1/ac1663a3e58176c10c0bb7a1.jpeg"},{"id":100008782,"identity":"47765a85-395f-4bec-9f4f-81f779558933","added_by":"auto","created_at":"2026-01-12 05:56:43","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":759433,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial transcriptome analysis of SHC2 gene expression pattern in COAD.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Tissue hires image. (B) The cell type characteristic map shows the dominant cell characteristics of different microareas and depicts the composition of the microenvironment at idle resolution. (C) Expression pattern of the SHCE gene in spatial transcriptome analysis of colon cancer samples. (D) Location and distribution of malignant and non-malignant areas on slices. (E) Analysis of SHC2 gene expression in malignant and normal areas. (F) Correlation between cell content and cell content in spot, and correlation between cell content and SHC2 gene expression.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8169608/v1/9227960d3f502af87de7168d.jpeg"},{"id":100360961,"identity":"68298d9c-5e86-4928-8fcd-78803aa3e707","added_by":"auto","created_at":"2026-01-16 07:44:14","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":696951,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis of SHC2.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Volcanographic showing the differences in the mRNA profiles of SHC2 expression levels. (B) Volcano plot displaying the different lncRNA profiles associated with SHC2 expression levels. (C) SHC2 analysis ofKEGG terms utilizing DEGs. (D) SHC2 analysisof the GO terms enriched with DEGs. （E-H）GSEA enrichment analysis of SHC2 differential genes.\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8169608/v1/d5c5b96e2e66287ac90d0881.jpeg"},{"id":100008805,"identity":"d2df9d91-7814-4336-a46e-219fe0cacb57","added_by":"auto","created_at":"2026-01-12 05:56:44","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1822114,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSHC2 reduced the expansion, migration, and invasive capabilities of COAD cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) SHC2 expression in COAD cell lines was investigated viaWestern blotting. (B) CCK-8 assay results indicated that reducing SHC2 expression markedly suppressed the proliferation of COAD cells. (C) Downregulationof SHC2 significantly inhibited the colony formation ability of COAD cells.(D) (D) Transwell assays demonstrated that downregulation of SHC2 significantly reduced the migration and invasion capabilities of COAD cells. *: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8169608/v1/d19959496ffc1a585808b327.jpeg"},{"id":100421585,"identity":"72ade253-568c-44d1-bd77-2e02d5ca3236","added_by":"auto","created_at":"2026-01-16 13:35:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14165383,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8169608/v1/1a78c7ab-1196-41e7-8bd9-c0c17509b2b1.pdf"},{"id":100008771,"identity":"199e6640-46a4-44d2-a816-ec8b50e77692","added_by":"auto","created_at":"2026-01-12 05:56:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":130109,"visible":true,"origin":"","legend":"","description":"","filename":"Westernblot.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8169608/v1/388be6a1409214b5d857dd40.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of SHC2 as a prognostic biomarker: from pan-cancer analysis to colon adenocarcinoma validation","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eColon adenocarcinoma (COAD) is among the most prevalent malignant tumors of the digestive system worldwide and poses a significant threat to patient morbidity and mortality (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Globally, it is the third most prevalent cancer and the second leading cause of death (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), and the 5-year relative survival rate of patients with colon adenocarcinoma is only 65% (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The occurrence of colon adenocarcinoma is affected by multiple factors, such as genetics, the environment and lifestyle (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The tumor microenvironment, immune cell infiltration and epigenetic changes play key roles in tumor progression and treatment (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Consequently, it is crucial to discover new biomarkers to improve the prognosis of patients with colon adenocarcinoma and refine treatment choices.\u003c/p\u003e \u003cp\u003eSHC-transforming protein 2 (SHC2) is a gene product composed of 582 amino acids (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) and belongs to the SHC family. In addition to SHC2, the SHC family also includes SHC1, SHC3 and SHC4. Their main function is to serve as adaptor proteins to conduct upstream signals and further activate downstream pathways. They can participate in the pathways of growth factors, especially in the MAPK and PI3K/Akt pathways (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Among them, SHC1 and SHC3 have been found to have a significant impact on various cancers, such as hepatocellular carcinoma (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), breast cancer (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) and lung cancer (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Therefore, the SHC family can serve as an effective target for tumor prognosis and treatment. However, the function of SHC2 in various cancers, including colorectal adenocarcinoma, remains largely unexplored.\u003c/p\u003e \u003cp\u003eThis research involved extracting data from the TCGA databases to conduct a detailed analysis of tumor and normal tissues: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) A comparison of the expression of SHC2 in tumor tissue and normal tissues was performed; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) a thorough prognostic analysis was conducted using survival and expression data from each tumor patient in the TCGA database; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) a detailed analysis of the copy number variation (CNV), single nucleotide site variation (SNV), methylation levels, and prognostic levels of tumor patients under different expression patterns in the TCGA database was performed; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) an extensive study was performed to examine the relationship between SHC2 expression in entire cancer and the infiltration of immune cells in tumor tissues; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) the GDSC and CTRP drug sensitivity data were obtained from the GSCA, and the relationship between SHC2 expression and drug sensitivity across all cancer types was examined; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) Performing spatial transcriptomics analysis on COAD tissue sections using the Sparkle database to investigate the relationship between SHC2 expression and tumor cell distribution; (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) GO and KEGG enrichment analyses were used to identify the functional changes and pathway effects caused by differential genes related to SHC2; and (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) using These data provide a theoretical basis for exploring the nature of SHC2 in tumors and provide new ideas for further study of the mechanism of action of SHC2 in COAD.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 SHC2 expression in human cancer\u003c/h2\u003e \u003cp\u003eSHC2 mRNA expression data and all clinical data were downloaded from The Cancer Genome Atlas (TCGA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancer.gov/)database\u003c/span\u003e\u003cspan address=\"https://www.cancer.gov/)database\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003es. We analyzed SHC2 mRNA expression in human cancer tissues and their corresponding paraneoplastic healthy tissues in the TCGA database via the TIMER2.0 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.comp-genomics.org/\u003c/span\u003e\u003cspan address=\"http://timer.comp-genomics.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In addition, by integrating the TCGA datasets, we further explored the expression profiles of SHC2 across cancer types in depth. All tumor abbreviations are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAbbreviations of the tumors from TCGA database.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbbreviations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdrenocortical Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBladder Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBRCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervical Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCESC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBile Duct Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHOL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge B-cell lymphoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDLBC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEsophageal Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eESCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlioblastoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead and neck cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHNSC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney chromophobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKICH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney clear cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKIRC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney papillary cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKIRP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute Myeloid Leukemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLAML\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower Grade Glioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLIHC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLUAD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung squamous cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLUSC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMesothelioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMESO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvarian cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePancreatic Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePAAD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProstate Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRAD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePheochromocytoma and paraganglioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePCPG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRectal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREAD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSARC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMelanoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSKCM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStomach cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTAD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTesticular Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGCT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThymoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHYM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrioid cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUCEC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUterine Carcinosarcoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUCS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOcular melanomas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUVM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical data and prognostic significance of SHC2 in human cancer\u003c/h2\u003e \u003cp\u003eWe analyzed the TCGA database to explore how SHC2 expression levels are related to clinicopathological characteristics in pancancer patients and further investigated the relationships between SHC2 expression and overall survival (OS), disease-specific survival (DSS) and the progression-free interval (PFI) of pancancer patients (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Mutation and methylation analysis of SHC2\u003c/h2\u003e \u003cp\u003eSHC2 genetic variations across cancers were identified via cBioPortal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). We examined CNVs, SNVs and methylation of the SHC2 gene across cancers and their correlations with gene expression and survival via the GSCA database (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Drug sensitivity analysis of SHC2\u003c/h2\u003e \u003cp\u003eWe explored the associations between SHC2 gene expression levels and the IC50, an indicator of drug sensitivity, in the Genomics Database for Cancer Drug Sensitivity (GDSC) and Cancer Treatment Response Portal (CTRP) databases at the pancancer level via the GSCA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.life.hust.edu.cn/GSCA/#/drug\u003c/span\u003e\u003cspan address=\"http://bioinfo.life.hust.edu.cn/GSCA/#/drug\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) platform (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Differential expression analysis and gene set enrichment analysis\u003c/h2\u003e \u003cp\u003eWe analyzed the differential expression of SHC2 via DESeq2 and edgeR differential analysis tools in R software and screened the differentially expressed molecules on the basis of the significance thresholds and expression fold difference thresholds. Using the TCGA database, the relationships between SHC2 and other mRNAs and lncRNAs were examined. GO and KEGG pathway enrichment analyses were subsequently performed on the significantly coexpressed genes via the clusterProfiler functional annotation toolkit in R software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Transcriptional landscape of COAD\u003c/h2\u003e \u003cp\u003eThis study used the Sparkle database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.grswsci.top\u003c/span\u003e\u003cspan address=\"https://www.grswsci.top\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to conduct spatial transcriptome analysis of colon cancer tissue sections. The Sparkle database integrates 10xVisium sequencing data and builds a pan-cancer spatial transcriptome map based on previous research(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), providing comprehensive data support for the analysis of the colon cancer microenvironment. During the analysis, the spatial transcriptome data was first visualized through the SpatialPlot function in the Seurat package to display the largest cell types in each microspot and their spatial distribution. Subsequently, Spearman correlation analysis method was used to calculate the correlation between cell content and cell content in all spots, as well as the correlation between cell content and gene expression. In order to present the correlation results more intuitively, the study used the linkET package for visualization, and comprehensively displayed the intensity, direction and significance of the correlation through multi-dimensional information such as color coding and graphic size.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Cell lines and cell culture\u003c/h2\u003e \u003cp\u003eThe HCT116 and RKO cell lines were purchased from the cell bank of the Chinese Academy of Sciences and cultured in RPMI 1640 medium (Thermo Fisher, 12633020) supplemented with 10% fetal bovine serum (Gibco, 16010159) and 1% penicillin/streptomycin double antibody solution. The cells were grown in a thermostat incubator at 37\u0026deg;C with saturated humidity of 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Transfection\u003c/h2\u003e \u003cp\u003eThe cells were inoculated in six-well plates, the serum-free medium was replaced when the cell density was moderate, the lip3000 (Thermo Fisher, L3000015) and siRNA complexes were added dropwise to the six-well dishes, the dishes were gently shaken, and subsequent experiments were carried out after 24 h of incubation. si-SHC2 (si-SHC2#1:5- GCACAAUUACUACAACAGCAUCCCG-3; si-SHC2#2:5-CUCAAGACAGUGGAUAUCGAGGCA \u0026minus;\u0026thinsp;3) was purchased from GenePharma.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Western blot assays\u003c/h2\u003e \u003cp\u003eCollect cell samples and extract total protein. After determining the protein concentration, load the samples into the gel wells and run the gel under constant voltage or constant current conditions. Subsequently, transfer the electrophoretically separated proteins from the gel onto a PVDF membrane under an electric field. Then, block the protein bands at room temperature using 5% skim milk powder in blocking buffer. The PVDF membrane regions corresponding to the target protein and the reference protein were excised separately. The excised membrane strips were incubated overnight with primary antibodies specific to the target protein: SHC2 (Abcam, ab243385) and beta-actin (Abcam, ab8227). After washing, the membranes were incubated with a secondary antibody diluent (Abcam, ab205718). Following the removal of unbound secondary antibody, color development and imaging were performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 CCK-8 assays\u003c/h2\u003e \u003cp\u003eWe digested and counted the cells, inoculated them into 96-well plates at a density of approximately 1000 cells per well, and incubated them in an incubator for 4 h. After the cells had adhered to the wall, we introduced 10 \u0026micro;L of CCK-8 reagent (Yeasen, 40203ES76) into each well and allowed them to incubate for 1 h, protected them from light, and then measured the absorbance values of the wells at 450 nm using an enzyme marker.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Colony formation experiment\u003c/h2\u003e \u003cp\u003eThe cells were digested, counted, inoculated into 6-well plates at a density of approximately 500 cells per well, and then cultured in an incubator for 14 days. Once the incubation was complete, the medium was discarded, and the plates were washed with PBS. Then, the cells were fixed with 4% paraformaldehyde. Following fixation, the cells were treated with crystal violet solution and then rinsed to eliminate any surplus dye. Finally, the formed cell clones were photographed and counted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Transwell assays\u003c/h2\u003e \u003cp\u003eWe digested and counted the cells and inoculated them into Transwell chambers (Corning, 3460) filled with serum-free medium. The chambers were subsequently placed in 24-well plates, and 500 \u0026micro;L of complete medium was added to the lower chambers before they were incubated for 24 or 48 hours. At the end of the incubation, the chambers were removed, washed to remove nonmigrating cells, fixed with 4% formaldehyde, and stained with 0.1% crystal violet. Finally, the chambers were rinsed, and the cells that had migrated to the undersurface of the membrane were photographed and counted under a light microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe differences between groups were statistically analyzed via Student's t test, with a \u003cem\u003eP\u003c/em\u003e value of less than 0.05 indicating statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1 SHC2 expression in human cancer\u003c/h2\u003e \u003cp\u003eWe analyzed the TCGA database to investigate the expression levels of SHC2 in various human cancers. Our results revealed that SHC2 expression was upregulated in seven human cancers, namely, CHOL, COAD, KIRC, KIRP, LIHC, PCPG, and PRAD, and that SHC2 expression was downregulated in five human cancers, namely, BLCA, BRCA, CESC, KICH, and LUSC, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. In addition, compared with that in healthy tissues, the expression of SHC2 was markedly greater in paired samples from five cancers: CHOL, COAD, KIRC, KIRP, and PRAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). These findings suggest that SHC2 may play different roles in different tumor types.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Correlations between SHC2 expression and the clinical characteristics of patients with human cancer\u003c/h2\u003e \u003cp\u003eWe subsequently investigated how SHC2 is related to the clinical characteristics of pancancer, such as T stage, N stage and M stage. SHC2 expression was significantly correlated with the T stage of several cancers, and in COAD, the expression level of SHC2 increased with increasing T stage; however, in LUAD, BRCA, and PAAD, the expression level of SHC2 decreased with increasing T stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In addition, SHC2 expression was significantly correlated with N stage in COAD, BRCA, KIRP and READ, and the expression level of SHC2 increased with increasing N stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Similarly, there was a notable correlation between SHC2 expression and M stage across various cancers. In THCA, TGCA and BLCA, the expression level of SHC2 increased with increasing M stage; however, in KIRC, the expression level of SHC2 decreased with increasing M stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Our results suggest that the expression level of SHC2 is correlated differently with the clinical characteristics of different tumors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3 SHC2 expression correlates with prognosis in human cancer\u003c/h2\u003e \u003cp\u003eTo determine the role of SHC2 expression in cancer patient prognosis, we accessed TCGA clinical data to analyze its correlation with OS, DSS, and PFI in 33 types of cancer. Our research indicated that elevated SHC2 expression was associated with improved overall survival in patients with ACC, LGG, and PAAD, whereas it was linked to poorer overall survival in those with COAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo exclude bias caused by nontumor events, we further assessed the effects of SHC2 expression levels on DSS and PFI. Our findings indicated that elevated SHC2 expression was linked to poorer DSS in COAD, STAD, and UVM patients, whereas it was associated with better DSS in ACC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In addition, elevated SHC2 expression was negatively correlated with patient PFI in COAD, PRAD, and STAD. Conversely, in patients with LGG and PAAD, high SHC2 expression was positively correlated with the PFI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eThese findings indicate a strong correlation between SHC2 expression levels and patient prognosis across various tumor types, with high SHC2 expression generally linked to poor outcomes in COAD patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Genetic variation in SHC2 and differences in survival\u003c/h2\u003e \u003cp\u003eTo study the correlation between SHC2 genetic variants in various cancers, the cBioPortal tool was used to identify genetic alterations in the data extracted from the TCGA dataset. The types of SHC2 mutations, loci, and number of cases are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. The major type of genetic alteration in SHC2 is missense mutation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we analyzed the heatmap of the percentage of SNVs in SHC2 in different cancers, and the results revealed that SNVs in SHC2 occurred mainly in UCEC, SKCM, COAD, LUSC, and HNSC and were more prevalent in KIRP, CESC, BLCA, LUAD, STAD, READ, PAAD, BRCA, LIHC, KIRC, GBM, PRAD and LGG, with few SNVs, and no SNVs were found in ESCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThe CNVs of SHC2 across cancers were subsequently evaluated, and we detected differential deletion and amplification of SHC2 in various cancers, with a predominance of amplification mutations in ACC, GMB, KICH, LGG, PCPG, SARC, DLBC, KIRC, MESO, COAD, THYM, and THCA but with varying levels of deletion mutations. In LAML, UVM, READ, CHOL, KIRP, PRAD, TGCT, LIHC, PAAD, SKCM, BRCA, BLCA, UCEC, CESC, STAD, HNSC, LUSC, ESCA, LUAD, UCS, and OV, deletion mutations were predominant, but amplifying mutations were still present to varying degrees (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eIn addition, we examined the survival disparities between the SHC2 genomic mutation group and the WT group in these cancers via progression-free survival (PFS), disease-free interval (DFI), OS, and DSS analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Finally, the results of CNV and survival analyses of SHC2 showed significant differences in OS for KIRC, UCEC, CESC, KIRP, KICH, and HNSC; significant differences in PFS for PRAD, KIRC, UCEC, CESC, KIRP, KICH, COAD, LUSC, and SARC; and significant differences in DSS for KIRC, UCEC, CESC, KIRP, KICH, and THCA; and significant differences in DFI for KIRP, DLBC, UCEC, UCEC, and COAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). These findings emphasize that genetic alterations may influence the expression pattern of SHC2 in various tumor types as well as the prognosis of patients with tumors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.5 DNA methylation of SHC2 in human cancer\u003c/h2\u003e \u003cp\u003eDNA methylation, a vital epigenetic alteration (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) influences gene expression and is strongly correlated with tumor prognosis and the effectiveness of treatment (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Our findings indicated that the level of DNA methylation aggregation in SHC2 was notably lower in the majority of cancer tissues than in normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In KIRC and KIRP tumor tissues, the CpG aggregation methylation level of CASZ 1 was notably greater than that in normal tissues.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo explore further the connection between the SHC2 methylation level and SHC2 mRNA expression, our results from the GSCA revealed that the RNA expression of SHC2 was associated with methylation in most cancers except LUSC, GBM, ESCA, READ, UCS and OV, especially in SARC and KIRP, where the SHC2 DNA methylation level was strongly negatively correlated with SHC2 mRNA expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eAfter that, we categorized 33 tumor patients on the basis of the methylation status of SHC2 and analyzed the differences in DSS and OS among the tumor patients, which revealed that SHC2 hypermethylated patients were significantly different from hypomethylated patients in terms of DSS in SKCM, HNSC and KIRP, and SHC2 hypermethylated patients were significantly different from hypomethylated patients in terms of OS in SKCM (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), suggesting that SHC2 methylation is associated with a survival advantage in cancer patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Analysis of the immune correlation between SHC2 and human cancer\u003c/h2\u003e \u003cp\u003eWe utilized the xCell and TIMER algorithms to investigate the relationship between SHC2 expression and immune cell infiltration across various cancers, focusing on the correlation between SHC2 expression and immune cell presence in tumor tissues. The xCell algorithm was used to explore the relationships between SHC2 expression and different types of infiltrating immune cells. The figure indicates a significant correlation between SHC2 expression and the level of infiltrating immune cells across most cancer types, especially showing a strong positive correlation with most immune cells in CHOL, SARC, SKCM, TGCT, THCA and THYM. In most cancer cases, there was a positive correlation between SHC2 expression and both T-cell CD\u003csup\u003e4+\u003c/sup\u003e Th2 and common lymphoid progenitor cells, whereas T-cell CD\u003csup\u003e4+\u003c/sup\u003e central memory cells were negatively correlated (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe employed the TIMER algorithm to explore the relationships between SHC2 expression and different types of immune cells. The results revealed a strong link between SHC2 expression and the infiltration of immune cells such as B cells, macrophages, neutrophils, myeloid dendritic cells, CD\u003csup\u003e8+\u003c/sup\u003e T cells, and CD\u003csup\u003e4+\u003c/sup\u003e T cells. Among these genes, SHC2 was significantly and positively correlated with the infiltration of various immune cell types in LGG, PRAD, SARC, TGCT and THCA tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eTo investigate the correlation between the expression of specific gene SHC2 and immune-related characteristics, we used Spearman correlation analysis method. We found significant positive correlations between SHC2 gene expression and multiple immune-related characteristics. Heat map results showed that the expression of a large number of immune-related characteristics was highly positively correlated with the expression of SHC2 gene, indicating that SHC2 may play an important role in the process of immune regulation. These findings help further explore the potential of SHC2 as an immunotherapy target and provide new ideas for the treatment of related diseases (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Cancer drug sensitivity analysis of SHC2\u003c/h2\u003e \u003cp\u003eThe sensitivity of tumor cells to chemotherapeutic drugs may play a key role in drug resistance in tumors, so we analyzed the correlation between SHC2 expression and drug sensitivity across all cancers. For GSCA, we gathered GDSC and CTRP drug sensitivity data and evaluated their relationships with SHC2 expression. According to the CTRP database, SHC2 expression was positively correlated with the following drugs: clofarabine, dasatinib, BRD-K63431240, bosutinib, and zebularine but negatively correlated with SB-525334 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to the GDSC database, there was a positive correlation between SHC2 expression and the drugs z-LLNle-CHO, sunitinib, bosutinib, AICAR and AZD7762 but a negative correlation with SB52334 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). These findings indicate that SHC2 is strongly linked to drug sensitivity across various tumor cell lines and could be a promising target for cancer treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Spatial transcriptomic sections analysis of SHC2 in COAD\u003c/h2\u003e \u003cp\u003eTo further study the expression of the SHC2 gene in COAD, we used the Sparkle database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.grswsci.top\u003c/span\u003e\u003cspan address=\"https://www.grswsci.top\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to perform spatial transcriptome analysis on colon cancer tissue sections (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-C). Through the visualization results of the Spatial Feature Plot function, it was found that the expression of SHC2 gene was significantly similar to the distribution of tumor cells. Specifically, the high-expression region of the SHC2 gene highly overlaps with the microdomains where tumor cells are located, while the expression of SHC2 in normal tissue regions is relatively low (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD-E). In addition, the expression of SHC2 gene was significantly positively correlated with the content of tumor cells in the spot, which was highly consistent with previous cell localization analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eF). This result suggests that in COAD, the SHC2 gene may be mainly expressed by tumor cells, and its expression level may be closely related to the biological behavior of the tumor.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Biological functions of SHC2 in COAD\u003c/h2\u003e \u003cp\u003eTo explore potential therapeutic strategies for different SHC2 expression patterns in COAD, we functionally annotated SHC2-associated differentially expressed genes (DEGs) and identified a total of 3535 DEGs (mRNAs and lncRNAs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-B). KEGG analysis revealed that the DEGs were chiefly involved in olfactory transduction, neuroactive ligand‒receptor interaction, taste transduction, protein digestion and absorption, salivary secretion, steroid hormone biosynthesis, mucin type 0-glycan biosynthesis, the renin‒angiotensin system, the early-onset diabetes of the young, and ascorbate and aldarate metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC). According to the GO enrichment results, the DEGs were chiefly involved in the following processes: the system process, G protein-coupled receptor signaling pathway, detection of stimulus, sensory perception of chemical stimulus, detection of chemical stimulus, detection of stimulus involved in sensory perception, G protein-coupled receptor activity, detection of chemical stimulus involved in sensory perception, and the nucleosome and DNA packaging complex (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.10 Downregulation of SHC2 suppresses the malignant phenotype of COAD\u003c/h2\u003e \u003cp\u003eTo explore and validate the biological function of SHC2 in the development of COAD, we constructed an SHC2 knockdown cell model to downregulate the expression of SHC2 in HCT116 and RKO cells and verified the knockdown effect via western blotting (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA). We determined that the downregulation of SHC2 decreased the proliferative capacity of HCT116 and RKO cells, as determined by a CCK-8 assay (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB). We further found that a decrease in SHC2 inhibited the colony formation ability of HCT116 and RKO cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eC). Transwell assays revealed that decreased SHC2 expression inhibited the migration and invasion ability of HCT116 and RKO cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eD). These results imply that SHC2 functions as an oncogene in COAD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eWith progress in tumor molecular biology, molecular targeted therapy has become a new means and method of tumor treatment because of its advantages of strong specificity, obvious efficacy, and minimal damage to normal tissues. In the treatment of cancer patients, targeted therapy can improve their prognosis (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Currently, the morbidity and mortality of colon adenocarcinoma patients are still very high. Therefore, it is important to find novel target molecules for the treatment of patients with colon adenocarcinoma to improve their prognosis.\u003c/p\u003e \u003cp\u003eIn our study, SHC2 expression was found to be upregulated in seven human cancers, namely, CHOL, COAD, KIRC, KIRP, LIHC, PCPG and PRAD. The expression of SHC2 in paired samples was significantly greater in five cancers, CHOL, COAD, KIRC, KIRP and PRAD, than in healthy tissues.\u003c/p\u003e \u003cp\u003eIn addition, clinicopathologic data revealed that in COAD patients, the expression level of SHC2 increased with increasing T stage. The expression level of SHC2 increased with increasing N stage in COAD, BRCA, KIRP and READ patients. In THCA, TGCA and BLCA patients, the expression level of SHC2 increased with increasing M stage. Analysis of patient prognostic data revealed that in COAD, OS decreased in patients with high SHC2 expression. In COAD, STAD and UVM, DSS decreased in patients with high SHC2 expression. In addition, in COAD, PRAD and STAD, the PFI decreased in patients with high SHC2 expression. In summary, SHC2 is highly expressed specifically in COAD, and its expression increases with increasing severity of tumor invasion (T stage) and lymph node metastasis (N stage). SHC2 is an independent risk factor for shorter OS, DSS and PFI in patients. This evidence strongly suggests that SHC2 is a key promoter of the development of COAD, invasion and metastasis, is an important poor prognostic biomarker in tumors, and has the potential to be a therapeutic target.\u003c/p\u003e \u003cp\u003eGenomic variants are characteristic of cancer and accumulate with cancer progression, providing important clues to the cause and prognosis of cancer (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Our study revealed a correlation between SHC2 expression and genomic variants such as CNV, SNV and methylation. This research examined the survival differences between SHC2 mutants and wild-type SHC2, the CNV of SHC2 and its impact on survival, the relationship between SHC2 methylation and mRNA expression levels, and the survival differences between SHC2 hypomethylation and hypermethylation in human cancer. Our study confirmed that SHC2 is an important molecule with high-frequency genetic alterations in a wide range of cancers that are regulated by DNA methylation and that its molecular status (genetic variation, methylation level) can significantly affect the survival prognosis of cancer patients. It is both a potential oncogenic driver and a valuable biomarker for prognostic assessment.\u003c/p\u003e \u003cp\u003eThe tumor microenvironment is vital for tumor growth and spread, and its ability to suppress the immune system is a key factor in the ineffectiveness of antitumor treatments (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Immune cells infiltrating tumors have a major impact on the genesis and progression of cancer (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), and choosing suitable and individualized immunotherapy methods requires a thorough understanding of the immune infiltration status of patients with tumors. Currently, data concerning the role of SHC2 in the immune system of cancer patients are scarce, and more research into its function in tumor immunity is needed. In our study, in various tumor types, notable links were found between SHC2 expression and the levels of various infiltrating immune cells and their subtypes, such as B cells, macrophages, DCs, neutrophils, CD\u003csup\u003e8+\u003c/sup\u003e T cells, and CD\u003csup\u003e4+\u003c/sup\u003e T cells. These findings suggest that SHC2 may play an important role in immune regulation, suggesting that SHC2 may be an important node of the immune regulatory pathway and a potential immunotherapeutic target in cancer.\u003c/p\u003e \u003cp\u003eThe low 5-year survival rate of cancer patients is largely due to drug resistance (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), making the enhancement of drug sensitivity a crucial topic in oncology. Our research indicated that the expression of the SHC2 gene was linked to chemotherapy sensitivity, and in the CTRP database, SHC2 expression was positively correlated with several drugs, especially clofarabine, dasatinib, BRD-K63431240 and zebularine, but was negatively correlated with SB-525334. In the GDSC database, there was a positive correlation between SHC2 expression and several drugs, especially Z-LLNle-CHO, but a negative correlation with SB52334. Owing to the variability among tumor cells, the relationship between SHC2 and various drugs across different tumors differs slightly, potentially affecting the treatment of these tumors. These findings indicate that SHC2 is strongly correlated with drug sensitivity across various tumor cell lines and could be a potential target for cancer treatment. These findings indicate that SHC2 is crucial in tumor progression because it influences genetic changes, tumor immunity, and the tumor microenvironment. Simultaneously, SHC2 can influence the effectiveness of antitumor drugs in chemotherapy and immunotherapy, offering fresh perspectives and targets for cancer treatment.\u003c/p\u003e \u003cp\u003eIn addition, the DEGs associated with SHC2 were analyzed in this study via GO and KEGG analyses. The KEGG enrichment revealed that the DEGs were involved mainly in olfactory transduction, neuroactive ligand‒receptor interactions, gustatory transduction, protein digestion and absorption, salivary secretion, steroid hormone biosynthesis, mucin-type O-linked glycosylation biosynthesis, the renin‒angiotensin system, youth-onset diabetes and ascorbate and aldose metabolism. The GO results indicate that the DEGs are involved primarily in systemic processes, G protein-coupled receptor signaling pathways, G protein-coupled receptor activity, stimulus detection, chemosensory perception, chemical stimulus detection, stimulus detection involving sensory perception, chemical stimulus detection involving chemosensory perception, nucleosomes, and DNA packaging complexes.\u003c/p\u003e \u003cp\u003eEarlier research has shown that these biological processes are intimately linked to the development of tumors. For example, earlier research revealed that the neuroactive ligand‒receptor interaction pathway is linked to the prognosis and response to immunotherapy in patients with COAD. Genes in this pathway, such as LTB 4 R2, could be used as novel targets for colon cancer therapeutic development (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Disturbances in neuroactive ligand‒receptor interactions and the renin‒angiotensin system pathway have been associated with cancer-related cognitive impairment (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). In addition, numerous experimental studies have demonstrated that angiotensin receptor blockers (ARBs) can inhibit the proliferative capacity of breast cancer cells (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), inhibit the invasion ability of intrahepatic cholangiocarcinoma cells (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), inhibit liver metastasis of colorectal cancer by increasing the anti-angiogenic effect of bevacizumab (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), and improve the survival of patients with non-small cell lung cancer and pancreatic cancer (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). And it can promote apoptosis of nasopharyngeal cancer(NPC) cells through the PI3 K/AKT signaling pathway, thereby improving the survival rate of NPC patients and inhibiting tumor growth (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Additionally, a significant amount of research has indicated that steroid hormones drive malignant changes in tumors such as breast cancer (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), prostate cancer (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), and bladder cancer (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). These studies suggest that genes differentially expressed in SHC2 may be involved in multiple biological processes related to tumor development.\u003c/p\u003e \u003cp\u003eIn addition, the results of the cell function experiment revealed that the knockdown of SHC2 expression significantly reduced the proliferation, migration and invasion ability of COAD cells. Evidence suggests that SHC2 acts as an oncogene in COAD. According to our study, targeting SHC2 may provide a novel pathway for the treatment of COAD, which is expected to improve patient prognosis by enhancing the immune system and preventing tumor growth.\u003c/p\u003e \u003cp\u003eThis research represents the first comprehensive analysis of SHC2 expression patterns, genomic mutations, methylation levels, the tumor microenvironment, immune infiltration, and drug sensitivity across various cancers and clarifies the biological role of SHC2 in COAD through cell function experiments. This study extends our understanding of SHC2 across cancers, including COAD, but has several limitations. Such as the lack of clinical and animal experimental data. Therefore, we will further investigate the predictive value and biological role of SHC2 in clinical pathological samples and in vivo COAD models in future research.Future studies should investigate how SHC2 affects immune infiltration and gene modification in COAD and the multiple mechanisms by which SHC2 regulates COAD development.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eIn conclusion, our findings revealed that SHC2 expression is elevated in COAD cell lines. Furthermore, SHC2 knockdown inhibited the proliferation, migration, and invasion of COAD cells. These results indicate that SHC2 might act as a molecular marker for patients with tumors and could be targeted in the clinical diagnosis and treatment of COAD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eColon adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSHC2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSHC-transforming protein 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCNV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecopy number variation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esingle nucleotide site variation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoverall survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edisease-specific surviva\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprogression-free interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGDSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenomics Database for Cancer Drug Sensitivity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCancer Treatment Response Portal\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprogression-free survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edisease-free interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edifferentially expressed genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdrenocortical Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBLCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBladder Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBRCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBreast Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCESC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCervical Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHOL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBile Duct Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDLBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLarge B-cell lymphoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEsophageal Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGBM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlioblastoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHNSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHead and neck cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKICH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKidney chromophobe\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKIRC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKidney clear cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKIRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKidney papillary cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLAML\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute Myeloid Leukemia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLower Grade Glioma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLIHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLiver cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLUAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLung adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLUSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLung squamous cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMESO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMesothelioma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOvarian cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePAAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePancreatic Can\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProstate Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCPG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePheochromocytoma and paraganglioma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eREAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRectal cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSARC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSarcoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSKCM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMelanoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTADZ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStomach cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTGCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTesticular Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enasopharyngeal cancer.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the databases that participated in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors participated in the present study. JP elaborated the initial experimental design; HZ and RT performed the experiments; HZ, RT, and MW conducted the data analysis; HZ wrote the manuscript; JP reviewed the final manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Key Research and Development Projects in Lvliang City (NO.2023SHFZ47).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval，consent to participate and consent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJia SN, Han YB, Yang R, Yang ZC. 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Cell Death Dis 2023 June 16;14(6):363.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mgnm","sideBox":"Learn more about [BMC Medical Genomics](http://bmcmedgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mgnm/default.aspx","title":"BMC Medical Genomics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Colon adenocarcinoma, SHC2, prognosis and treatment, biomarker","lastPublishedDoi":"10.21203/rs.3.rs-8169608/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8169608/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eColon adenocarcinoma (COAD) is a malignant neoplasm of the digestive system characterized by a high incidence and significant mortality on a global scale. The majority of patients are diagnosed at intermediate to advanced stages of the disease. SHC-transforming protein 2 (SHC2), a member of the SHC family, has little known about its function in a variety of cancers, including COAD. This investigation thoroughly examined the expression characteristics, prognostic value, genomic variation and methylation patterns of SHC2 in pancarcinoma and discussed its correlation with tumor immune cell infiltration. Furthermore, we verified that SHC2 expression was elevated in COAD cell lines and that lowering SHC2 expression levels significantly reduced the invasion, migration and proliferation ability of COAD cell lines. Our results revealed that SHC2 is significantly dysregulated in COAD and that its expression level is closely related to patient prognosis, immune microenvironment remodeling, and drug sensitivity and affects tumor progression by regulating key signaling pathways. This study systematically reveals the value of SHC2 as a therapeutic target and prognostic indicator for COAD, offering a theoretical foundation for a thorough investigation of its molecular mechanism.\u003c/p\u003e","manuscriptTitle":"Identification of SHC2 as a prognostic biomarker: from pan-cancer analysis to colon adenocarcinoma validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 05:56:36","doi":"10.21203/rs.3.rs-8169608/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"219702137911243961192684052896184194862","date":"2026-01-09T08:34:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-07T05:54:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-05T09:35:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-16T05:14:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-29T18:41:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Genomics","date":"2025-11-29T13:29:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mgnm","sideBox":"Learn more about [BMC Medical Genomics](http://bmcmedgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mgnm/default.aspx","title":"BMC Medical Genomics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"51257f74-16a6-44b8-af5f-1939f02b9a5b","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T05:56:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 05:56:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8169608","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8169608","identity":"rs-8169608","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}