Abstract Background It has been shown that the protein known as Small G Protein Signaling Modulator 1 (SGSM1), which has both a TBC domain and RUN domain and was linked to RAB- and RAP-mediated cellular signaling, is involved in the advancement of tumors. It is unclear, however, whether SGSM1 is pivotal in the development of pan-cancer. Methods The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases were used to examine the expression patterns of SGSM1 in a variety of malignancies. The SGSM1 protein expression levels were validated in the HPA and UALCAN databases. Protein-gene interaction analysis was carried out using data from the STRING and GeneMANIA databases. The predictive significance of SGSM1 expression was examined via the K-M plotter and survival data analyses and included OS, PFI, and DSS. For the purpose of conducting SGSM1-related GO and KEGG enrichment analyses, the R program “clusterProfiler” was utilized. We analyzed the TISIDB database in search of links between SGSM1 expression and immune or molecular subtypes. In addition, we examined how the expression of SGSM1 is linked to that of immune-related genes. Results In TCGA, SGSM1 expression was shown to be downregulated in the vast majority of malignancies, including HNSC, COAD, BRCA, and KIRP, and to indicate an unfavorable prognosis in cancers such as KIRC, LGG, MESO, PAAD, and UCEC. The expression of SGSM1 is highly variable across molecular and immunological cancer subtypes. In COAD, KIRC, PAAD, and PRAD tissues, the SGSM1 protein was downregulated in comparison with normal tissue. The AUC for ten different cancers predicted by SGSM1 was more than 0.7, indicating a considerable degree of accuracy. The Ras signaling pathway dominated the enriched KEGG pathways. SGSM1 in KIRC was discovered to have a negative link to immune-related genes, including CTLA4, PDCD1, TIGIT, LAG-3, and CD48. Conclusions The findings demonstrated that SGSM1 could be used as a novel biological marker for a wide variety of tumor types. SGSM1 may be implicated in the tumor immune microenvironment, which holds promise as a putative novel treatment target for cancer. Supplementary Information The online version contains supplementary material available at 10.1007/s12672-025-02940-2. Keywords: SGSM1, Pan-cancer analysis, Prognosis, Omics integrative analysis, Biomarker Introduction The Small G Protein Signaling Modulator 1 (SGSM1) protein, which was found on chromosome 22q11.2, was discovered to be primarily expressed in brain tissues [[40]1]. Moreover, SGSM1 was shown to be residing within the trans-Golgi network. In addition, the SGSM1 comprised TBC and RUN domains, both of which were related to RAB- and RAP-mediated cellular signaling. Several research reports have shown that RAP proteins, particularly RAP1, which regulates migration and adhesion in numerous malignancies via shuttling between its inactive GDP-bound and active GTP-bound form, serve a critical function in the development of cell-cell junctions based on cadherin [[41]2–[42]4]. SGSM1 served as a mediator for the interaction between the intracellular signaling pathways and the vesicle transport system. Past studies demonstrated a correlation between a low level of SGSM1 expression and a dismal prognosis in LGG and its respective subtypes [[43]5]. As per the findings of a recent study, the invasion, as well as metastasis of nasopharyngeal cancer, were caused by the degradation of SGSM1 [[44]6]. Bioinformatics analysis has emerged as a key tool for scanning and identifying putative biological markers for a wide variety of diseases, including cancer, due to the advent of analytical technologies for gene expressions and the accrual of sizable gene expression repositories on clinical samples [[45]7–[46]9]. Gene expression patterns may be analyzed using bioinformatics to determine the percentage of tumor-infiltrating immune cells (TIICs) and to discover functionally differentially expressed genes (DEGs) for diagnosing and predicting the prognosis of cancer patients [[47]10]– [[48]11]. Despite these findings, several critical questions remain unresolved. First, the expression patterns and prognostic significance of SGSM1 across diverse cancer types have not been comprehensively explored, limiting its potential as a pan-cancer biomarker. Second, the molecular mechanisms underlying SGSM1-mediated tumor suppression or promotion, particularly its interaction with immune microenvironments, are poorly understood. Third, whether SGSM1 could serve as a universal therapeutic target beyond specific malignancies (e.g., LGG and nasopharyngeal cancer) requires further validation. To address these gaps, we conducted a pan-cancer analysis integrating multi-omics data from 33 cancer types, aiming to: (1) systematically characterize the oncogenic role of SGSM1 across cancers; (2) evaluate its diagnostic and prognostic utility in clinical cohorts; and (3) identify potential mechanisms linking SGSM1 dysregulation to tumor progression and immune evasion. Our findings provide a foundation for developing SGSM1-targeted strategies in precision oncology. This research highlighted the differential expression of SGSM1 in various cancers and studied the crucial function of SGSM1 in the incidence and progression of cancer by evaluating the multilevel data involving 33 cancers from TCGA, TISIDB, GTEx, HPA, STRING, and other databases. SGSM1 was significantly correlated with the progression-free interval (PFI), disease-specific survival (DSS), and overall survival (OS) of stomach adenocarcinoma (STAD), bladder urothelial carcinoma (BLCA), adrenocortical carcinoma (ACC), uterine corpus endometrial carcinoma (UCEC). In addition, it also has an excellent prediction performance in acute myeloid leukemia (LAML), and testicular germ cell tumors (TGCT). We next focused on KIRC and discovered that SGSM1 independently functioned as a risk indicator for PFI, DSS, and OS. We also examined the DEGs across the high- and low-SGSM1 expression groups (categories), as well as the genes that are co-expressed with SGSM1. Together, We showed that SGSM1 was a valuable diagnostic and prognostic marker for pan-cancer, as well as a potential molecular target for KIRC. Materials and methods Data collection RNA-seq data (STAR-counts) and clinical information for 33 cancer types were obtained from the TCGA GDC Data Portal ([49]https://portal.gdc.cancer.gov/) via the TCGAbiolinks R package (v2.24.1). Normal tissue RNA-seq data (TPM) were sourced from the GTEx database (v8) through the UCSC Xena browser. All datasets are publicly accessible without restrictions. SGSM1 expression analysis The expression levels of SGSM1 in normal and tumor tissues were compared using the Wilcoxon rank sum test function in the “ggplot2” R package(v3.3.3).The TISIDB ([50]http://cis.hku.hk/TISIDB/) database [[51]12] was used to detect difference of SGSM1 mRNA expression level in various immune subgroups and molecular subgroups in pan-cancer. The HPA ([52]https://www.proteinatlas.org) database was used to explore the protein expression levels of SGSM1 in normal human tissues and tumor tissues [[53]13]. The antibodies of SGSM1 protein were HPA031621 respectively in diverse tumors. Finally, we explored the protein expression level of SGSM1 between primary tumors and normal tissues through the UALCAN portal [[54]14] ([55]http://ualcan.path.uab.edu/analysis-prot.html). Copy number variation (CNV) and methylation analysis The Gene Set Cancer Analysis (GSCA; [56]http://bioinfo.life.hust.edu.cn/GSCA/#/) database is a powerful bioinformatics analysis tool which mainly integrates the mRNA expression, mutation, immune infiltrates, methylation data from the TCGA database. The “mutation” module in the GSCA database was used to analyze CNVs and methylation of SGSM1 as well as their correlation with mRNA expression levels. Correlation between SGSM1 expression and pathological stages of cancers The Gene Expression Profiling Interactive Analysis (GEPIA) is an online platform that has a large collection of tumors and normal samples from GTEx and TCGA [[57]15]. GEPIA2’s “Pathological Stage Plot” module was used to investigate the relationship between SGSM1 expression and pathological stages. Development of the Protein-Protein interaction (PPI) network Twenty SGSM1-binding proteins were obtained from the STRING database [[58]7] using the primary criteria adjusted as below: active interaction sources (“Experiments, Text mining, Databases”) and minimum required interaction score (“medium confidence (0.400)”). GeneMANIA, accessible at [59]http://genemania.org/, is a fast method of constructing gene networks and predicting their functions in Cytoscape. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses The GO and KEGG enrichment analyses were carried out on a collection of 37 SGSM1-binding proteins with the use of the“ggplot2” package (v3.3.3) for visualization and the “clusterProfiler” package (v3.14.3) for statistical analysis [[60]16]. Network analysis was performed using Cytoscape (v3.9.1). SGSM1’s capacity to distinguish tumor from non-tumor tissues An evaluation of SGSM1’s diagnostic significance in pan-cancer was performed utilizing the receiver operating characteristic (ROC) curve. We discovered that the area under the ROC curve (AUC) was in the range of 0.5 to 1. Diagnostic performance is optimal when AUC is very near 1. Lower AUC values (0.5–0.7) denoted less accuracy, intermediate values (0.7–0.9) denote moderate accuracy, and higher values (0.9 and more) denote a high degree of accuracy. ROC analysis of the data was performed using the “pROC” package(v1.18.0), and the results were visualized using “ggplot2 ”package(v3.3.3). Survival analysis The link between SGSM1 expression and survival (DSS, OS, and PFI) was assessed utilizing Kaplan-Meier plots. Additionally, we evaluated the correlations between SGSM1 expression and PFI, DSS, and OS in distinct KIRC clinical groupings. We utilized the Xiantao platform ([61]https://www.xiantao.love/; accessed on 1 January 2023) for visual representation. Hypothesis testing was performed via Cox regression and P < 0.05 is significant. We used the “rms” package (v6.2-0) and the “survival” package (v3.3.1) for these analyses. Association of SGSM1 Expression with Clinical Features in KIRC To evaluate the relationship between SGSM1 expression and clinical characteristics in kidney renal clear cell carcinoma (KIRC), we performed the following steps: Data Preprocessing: RNA-seq data (level 3 HTSeq-FPKM) and clinical annotations from TCGA-KIRC were converted to transcripts per million (TPM) format to normalize sequencing depth differences. The TPM values were log2-transformed to approximate a normal distribution. Statistical Analysis: Differential SGSM1 expression across clinical subgroups (e.g., tumor stage, gender) was assessed using the Wilcoxon rank-sum test (for two groups) or Kruskal-Wallis test (for multiple groups). A significance threshold of *p* < 0.05 was applied without multiplicity correction due to the exploratory nature of the analysis. Software: Analyses were conducted with the “stats” (v4.2.1) and “car” (v3.1-0) packages. Univariate and multivariate Cox regression analyses in KIRC To determine the predictive significance of SGSM1 and other clinical variables in terms of PFI, OS, and DSS in KIRC, we conducted univariate and multivariate Cox regression analyses. Statistical testing was conducted utilizing the survival program. We used the “survival” package (v3.3.1) and the “rms” package(v6.2-0) for these analyses. Establishment and evaluation of the nomogram SGSM1 expression and the pathologic T, N, M stage were used to build a nomogram, which is an effective and convenient approach for estimating the survival in individual patients. The calibration curve was performed to verify the prediction accuracy of the nomogram. Correlation of SGSM1 with immune checkpoint genes and immune cell infiltration The association between SGSM1 expression and immune checkpoint genes (PDCD1, CD274, CD48, TIGIT, LAG3, and CTLA4) was analyzed using the Xiantao. platform ([62]https://www.xiantao.love/; accessed on 1 January 2023), a web tool for TCGA data mining and visualization. Spearman’s rank correlation coefficients (ρ) were calculated to quantify monotonic relationships, with *p* < 0.05 considered statistically significant. Immune checkpoint genes were selected based on their established roles in tumor immune evasion. Results were visualized using the “ggplot2”package (v3.3.3) in R. We used the TIDE (Tumor Immune Dysfunction and Exclusion) algorithm to predict potential immunotherapy responses. Statistical analyses were performed with the use of R software (v4.1.3). The CIBERSORT algorithm was applied to assess the levels of 22 infltrating immune cell subtypes. CIBERSORT can compute the abundance of specifc cell types in a mixed sample based on the bulk expression. The R packages “gsva”(v1.46.0) and “ggalluvial”(v0.12.3) were used. Statistical analysis R version 4.1.3 was used for all statistical studies. The survival curve was plotted by KM plotter. Wilcoxon test was used to compare the diferences between two groups, and Spearman analysis was used to calculate the correlation coefcients. Double tailed p < 0.05 was considered statistically signifcant. Results SGSM1 expression in pan-Cancer Figure S1 illustrates the workflow of our study.Considerable downregulation of SGSM1 expression was observed in 11 types of cancer in the TCGA tumor and adjoining normal samples, which included head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), lung adenocarcinoma (LUAD), kidney renal papillary cell carcinoma (KIRP), colon adenocarcinoma (COAD), breast invasive carcinoma (BRCA), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), and UCEC. In contrast, SGSM1 was upregulated in cholangiocarcinoma (CHOL), and liver hepatocellular carcinoma (LIHC) (Fig. [63]1A). Additionally, when comparing TCGA tumors to normal samples from the GTEx database, we discovered that SGSM1 expression was substantially downregulated in 14 diverse types of cancers, namely, ESCA, COAD, glioblastoma multiforme (GBM), HNSC, KIRC, BLCA, KIRP, LUSC, PRAD, BRCA, rectum adenocarcinoma (READ), and UCEC but was upregulated in paraganglioma (PCPG), CHOL, KICH, and LIHC (Fig. [64]1B). Venn diagram showed the cancer categories with downregulated SGSM1 expression (Fig. [65]1C); Venn diagram showed the cancer categories with upregulated SGSM1 expression (Fig. [66]1D). Fig. 1. [67]Fig. 1 [68]Open in a new tab Levels of SGSM1 gene expression in tumors and normal tissues. (A) Expression of SGSM1 in TCGA cancers as well as the adjoining normal tissues; (B) SGSM1 expression in TCGA cancers and normal samples compared to the data of the GTEx database, which served as controls ; (C) Venn diagram showed the cancer categories with downregulated SGSM1 expression; (D) Venn diagram showed the cancer categories with upregulated SGSM1 expression. (*p < 0.05, **p < 0.01, ***p < 0.001) To further explore the mechanisms underlying the abnormal expression of SGSM1 mRNA, we analyzed the relationship between gene copy number variation (CNV) and mRNA expression. The results from the GSCA database showed that there was a significant positive correlation between the expression of SGSM1 and CNV in patients with COAD, BRCA, HNSC, and LUAD(Figure S2A). DNA methylation is an epigenetic process that can significantly modulate gene transcription; therefore, we found that DNA methylation levels were significantly correlated with mRNA expression in most tumor types, especially in BLCA, CESC, ESCA, LUSC, and SKCM (Figure S2B). Association of SGSM1 with immune or molecular subtypes of cancers With the aid of the TISIDB database, we investigated whether or not SGSM1 differential expression was linked to certain molecular subtypes of pan-cancer. The findings illustrated differential expression of SGSM1 in the molecular subtypes of 15 kinds of tumors, namely, ACC, BRCA, COAD, HNSC, ESCA, UCEC, STAD, SKCM, PRAD, PCPG, OV, LUSC, LIHC, LGG, and KIRP. The expression level of SGSM1 was the least among the basal molecular subtypes for BRCA (Fig. [69]2A). Within the HM-SNV molecular subtype, SGSM1 had the lowest level of expression in COAD (Fig. [70]2B). In the basal molecular subtype of HNSC, SGSM1 expression was the lowest (Fig. [71]2C). The molecular subtype of C1 displayed the lowest expression of SGSM1 for KIRP (Fig. [72]2D). Additionally, the lowest SGSM1 expression was found in the LIHC molecular subtype iCluster:3 (Fig. [73]2E). The expression of SGSM1 was lowest in the classical Molecular Subtype of LUSC (Fig. [74]2F).The expression level of SGSM1 was the lowest in the 7-IDH1 molecular subtype of PRAD (Fig. [75]2G). The CN_LOW molecular subtype of UCEC showed the lowest expression level of SGSM1 (Fig. [76]2H). Fig. 2. [77]Fig. 2 [78]Open in a new tab Relationships between the expression of SGSM1 and the molecular subtypes of TCGA cancers. (A)BRCA; (B) COAD; (C) HNSC; (D) KIRP; (E)LIHC; (F)LUSC; (G)PRAD; (H)UCEC Fig. 4. [79]Fig. 4 [80]Open in a new tab Validation of SGSM1 expression in different tumor tissues. The expression difference of SGSM1 protein between tumor and normal tissues was compared with the data of HPA (200×). (B) Box plot displaying the expression level of SGSM1 in different tumor tissues compared to that in normal tissues from UALCAN (p < 0.05.) Furthermore, we identified a substantial link between SGSM1 expression and a diverse of immune subtypes (C1:wound healing, C2: IFN-gamma dominant, C3: inflammatory, C4: lymphocyte depleted, C5: immunologically quiet, C6: TGF-b dominant) of 8 kinds of tumors, which comprise BLCA (Fig. [81]3A), BRCA (Fig. [82]3B), COAD (Fig. [83]3C), HNSC (Fig. [84]3D), KIRC (Fig. [85]3E), LUAD (Fig. [86]3F), LUSC(Fig. [87]3G), PRAD(Fig. [88]3H) . Fig. 3. [89]Fig. 3 [90]Open in a new tab Associations between the expression of SGSM1 and immune subtypes in TCGA cancers. (A)BLCA; (B) BRCA; (C) COAD; (D) HNSC; (E) KIRC; (F)LUAD; (G)LUSC; (H)PRAD Validation of SGSM1 expression in different tumor tissues In PRAD, COAD, KIRC, and PAAD tissues, SGSM1 protein levels were downregulated opposite to those found in normal tissue. LIHC tissue showed an upregulation of SGSM1 protein contrary to normal tissue accordingly. For example, In KIRC cells, SGSM1 protein cannot be detected, but it is weakly stained in normal glomeruli and moderately stained in normal tubules (Antibody: HPA003986) (Fig. [91]4A). Based on UALCAN Database, We also found low expression of SGSM1 in COAD、KIRC、PAAD and PRAD tumor tissues, but high expression in LIHC tissues (Fig. [92]4B). SGSM1 expression was correlated with advanced stages of cancers The"Pathological Stage Plot” module of GEPIA2 was used to detect correlations between SGSM1 expression and cancerous stages. The result showed that SGSM1 expression were all significantly associated with tumor pathological stages in CESC, KICH (Kidney chromophobe), Lung adenocarcinoma (LUAD), LIHC (Liver Hepatocellular carcinoma), Pancreatic Aadenocarcinoma (PAAD), Ovarian serous cystadenocarcinoma (OV) (Fig. [93]5, all P < 0.05). Fig. 5. [94]Fig. 5 [95]Open in a new tab The correlation between SGSM1 expression and the pathological stages of cancers, including CESC (A), KICH (B), LUAD (C), LIHC (D), PAAD (E), OV (F), using GEPIA2’s“Pathological Stage Plot” module PPI /GeneMANIA network and GO / KEGG enrichment aAnalyses We used the STRING database to search for and identify 20 proteins that SGSM1 specifically targets for binding (Fig. [96]6A). Additionally, utilizing the GeneMANIA database, we searched for 20 targeted binding genes of SGSM1 (Fig. [97]6B). We have merged the two gene sets and removed duplicate entries, yielding a final list of 37 unique genes. Then, we analyzed the 37 targeted binding genes utilizing the GO enrichment tool (Fig. [98]6C), and the findings that the key biological processes (BP) comprised Rab-, Rap- and Ras- protein signal transduction, vesicle-mediated transport in synapse and modulation of exocytosis. In addition, the enriched cellular components (CCs) comprised the phagocytic vesicle, phagocytic vesicle membrane, endocytic vesicle, recycling endosome membrane, and postsynaptic endosome. Further, the molecular function (MF) was predominantly enriched in GTPase activity, ribonucleoside binding, purine ribonucleoside, and nucleoside binding, as well as GTP binding (Table [99]1). The enriched KEGG pathways were predominantly related to Pancreatic secretion, Endocytosis, Vasopressin-regulated water reabsorption, Ras signaling pathway, and Long-term potentiation (Fig. [100]6D).We also identied hub nodes within the Maximal Clique Centrality (MCC) algorithm using the CytoHubba plugin in Cytoscape(v3.9.1)(Figure S4),which provided meaningful biological insights into key regulatory elements. The results of cytohubba showed that SGSM1 and SGSM3 genes were located in the core of the PPI network and may play an important regulatory role, which is worthy of further exploration in the future. Fig. 6. [101]Fig. 6 [102]Open in a new tab Protein-protein interaction (PPI) network, gene-gene interaction network, GO analysis, and KEGG analysis of 20 targeted binding proteins of SGSM1. A network diagram of interactions between proteins encoded by the SGSM1 gene, drawn by using STRING; (B) GeneMANIA’s representation of the gene network including SGSM1; (C) GO and KEGG Enrichment Analysis of SGSM1-Associated Genes. The circular layout maps relationships between genes (left) and their significantly enriched terms (right) from functional analysis. It highlights which genes contribute to specific biological processes, molecular functions, or cellular components. Point size indicates the number of genes in each term; Red dots represent individual genes;.Blue dots represent pathways or GO terms.(D) GO and KEGG Enrichment Analysis of SGSM1-Associated Genes. Bubble plot showing significantly enriched biological processes. Point size indicates the number of genes in each term; color scale represents the adjusted p-value (− log10-transformed). The abscissa is GeneRatio, which represents the proportion of genes under the pathway to the total number of genes Table 1. Results of GO and KEGG enrichment analyses ONTOLOGY ID Description GeneRatio BgRatio pvalue p.adjust qvalue BP GO:0032482 Rab protein signal transduction 10/19 75/18,670 5.26e-20 2.28e-17 1.15e-17 BP GO:0007265 Ras protein signal transduction 14/19 448/18,670 1.79e-19 3.88e-17 1.95e-17 BP GO:0032486 Rap protein signal transduction 5/19 15/18,670 1.84e-12 2.66e-10 1.33e-10 BP GO:0099003 Vesicle-mediated transport in synapse 6/19 207/18,670 4.16e-08 4.51e-06 2.26e-06 BP GO:0017157 Regulation of exocytosis 6/19 217/18,670 5.50e-08 4.78e-06 2.40e-06 CC GO:0045335 Phagocytic vesicle 7/20 132/19,717 3.70e-11 4.07e-09 1.48e-09 CC GO:0030670 Phagocytic vesicle membrane 5/20 76/19,717 1.10e-08 4.19e-07 1.53e-07 CC GO:0030139 Eendocytic vesicle 7/20 303/19,717 1.23e-08 4.19e-07 1.53e-07 CC GO:0055038 Recycling endosome membrane 5/20 81/19,717 1.53e-08 4.19e-07 1.53e-07 CC GO:0098845 Postsynaptic endosome 3/20 13/19,717 2.54e-07 4.65e-06 1.69e-06 MF GO:0003924 GTPase activity 13/19 324/17,697 5.02e-19 1.86e-17 4.76e-18 MF GO:0005525 GTP binding 13/19 374/17,697 3.30e-18 3.02e-17 7.73e-18 MF GO:0032550 Purine ribonucleoside binding 13/19 378/17,697 3.79e-18 3.02e-17 7.73e-18 MF GO:0001883 Purine nucleoside binding 13/19 381/17,697 4.21e-18 3.02e-17 7.73e-18 MF GO:0032549 Ribonucleoside binding 13/19 382/17,697 4.35e-18 3.02e-17 7.73e-18 KEGG hsa04972 Pancreatic secretion 4/10 102/8076 4.75e-06 1.43e-04 6.00e-05 KEGG hsa04144 Endocytosis 4/10 252/8076 1.68e-04 0.003 0.001 KEGG hsa04962 Vasopressin-regulated water reabsorption 2/10 44/8076 0.001 0.013 0.005 KEGG hsa04014 Ras signaling pathway 3/10 232/8076 0.002 0.013 0.006 KEGG hsa04720 Long-term potentiation 2/10 67/8076 0.003 0.013 0.006 [103]Open in a new tab Diagnostic significance of SGSM1 in pan-cancer To evaluate SGSM1’s utility as a diagnostic tool for pan-cancer, a ROC curve analysis was carried out. From this data, we may conclude that SGSM1 is quite accurate (AUC > 0.7) in predicting 10 distinct forms of cancer, specifically, BLCA (AUC = 0.764, CI: 0.678–0.850) (Fig. [104]7A), PRAD (AUC = 0.719, CI: 0.648–0.790) (Fig. [105]7B), KIRP (AUC = 0.920, CI: 0.889–0.950) (Fig. [106]7C), BRCA (AUC = 0.789, CI: 0.747–0.831) (Fig. [107]7D), CHOL(AUC = 0.975, CI: 0.938–1.000) (Fig. [108]7E), COAD (AUC = 0.836, CI: 0.785–0.887) (Fig. [109]7F), HNSC (AUC = 0.778, CI: 0.711–0.845) (Fig. [110]7G), LUSC (AUC = 0.909, CI: 0.884–0.935)(Fig. [111]7H), READ (AUC = 0.941, CI: 0.899–0.984) (Fig. [112]7I), UCEC(AUC = 0.855, CI: 0.810–0.901) (Fig. [113]7J). Of these SGSM1 showed excellent predictive ability (AUC > 0.9) for LUSC, READ, KIRP, and CHOL. Fig. 7. [114]Fig. 7 [115]Open in a new tab The Receiver operating characteristic (ROC) curve for SGSM1 expression in pan-cancer. (A) BLCA; (B) PRAD; (C) KIRP; (D) BRCA; (E) CHOL; (F) COAD; (G) HNSC; (H) LUSC; (I) READ; (J) UCEC Fig. 10. [116]Fig. 10 [117]Open in a new tab Relationship of SGSM1 expression with the DSS in various clinical subgroups of KIRC. (A) T stage: T3&T4; (B) N stage: N0; (C) M stage: M0; (D) Pathologic stage: Stage III&Stage IV; (E) Gender: female ; (F) Age ≤ 60; (G) Histologic grade: G3&G4; (H)Race: white Significance of SGSM1 in predicting cancer patients’ prognosis For KIRC, LGG, MESO, PAAD, and UCEC, there was a substantial positive link between SGSM1 expression and PFI, OS, and DSS. The Cox regression findings illustrated that a lowered expression level of SGSM1 was linked to a dismal prognosis for KIRC patients, in terms of OS [hazard ratio (HR) = 0.55, 95% confidence interval (CI): 0.41–0.75, p <0.001], DSS (HR = 0.34, 95% CI: 0.22–0.52,p <0.001), and PFI (HR = 0.48, 95% CI: 0.35–0.66, p < 0.001) (Fig. [118]8A). In addition, the findings of the Cox regression on LGG illustrated that a reduced expression of SGSM1 was linked to an unfavorable prognosis, with OS (HR = 0.31, 95% CI: 0.21–0.46, p < 0.001), DSS (HR = 0.28, 95% CI: 0.19–0.43, p < 0.001), and PFI (HR = 0.45, 95% CI:0.34–0.60, p p < 0.001) (Fig. [119]8B). As per the findings of the Cox regression analysis, a lowered SGSM1 expression level was linked to a dismal prognosis for MESO: OS (HR = 0.44, 95% CI: 0.27–0.72, p = 0.001), DSS (HR = 0.43, 95% CI: 0.23–0.81, p = 0.008), and PFI (HR = 0.54, 95% CI:0.32–0.93, p = 0.027) (Fig. [120]8C). The findings of the Cox regression on PAAD indicated that a lowered expression level of SGSM1 was linked to a poorer prognosis: OS (HR = 0.65, 95% CI: 0.43–0.98, p = 0.039), DSS (HR = 0.69, 95% CI: 0.43–1.09, p = 0.112), and PFI (HR = 0.60, 95% CI: 0.40–0.88, p = 0.009) (Fig. [121]8D). Also, UCEC with elevated expression levels of SGSM1 exhibited a dismal prognosis: OS (HR = 1.73, 95% CI: 1.14–2.63, p = 0.009), DSS (HR = 2.16, 95% CI: 1.28–3.65,p = 0.004), and PFI (HR = 1.31, 95% CI: 0.93–1.86,p = 0.123) (Fig. [122]8E). Fig. 8. [123]Fig. 8 [124]Open in a new tab The Relationship of SGSM1 expression with the prognosis (OS, DSS, and PFI) of cancers. (A)KIRC; (B) LGG; (C) MESO; (D) PAAD; (E)UCEC Also, we assessed the links between SGSM1 and the prognosis (PFI, DSS, and OS) in diverse clinical subgroups associated with KIRC. The findings demonstrated that a lowered expression level of SGSM1 was linked to a dismal OS across a majority of clinical subgroups, including subgroup of T stage: T3&T4 (HR = 0.57, 95% CI: 0.39–0.85, p = 0.006, Fig. [125]9A), subgroup of N stage: N0 (HR = 0.59, 95% CI: 0.38–0.91, p = 0.018, Fig. [126]9B), subgroup of M stage: M0 (HR = 0.60, 95% CI: 0.41–0.89, p = 0.011, Fig. [127]9C), subgroup of Pathological stage: Stage III&Stage IV (HR = 0.62, 95% CI: 0.43–0.90, p = 0.012, Fig. [128]9D), subgroup of Gender: female (HR = 0.46, 95% CI: 0.27–0.76, p = 0.003, Fig. [129]9E), subgroup of Age ≤ 60 (HR = 0.49, 95% CI: 0.30–0.82, p = 0.006, Fig. [130]9F), subgroup of Histologic grade : G3&G4 (HR = 0.54, 95% CI: 0.38–0.78, p = 0.001, Fig. [131]9G), and subgroup of Race: white (HR = 0.55, 95% CI: 0.40–0.75, p < 0.001, Fig. [132]9H). Fig. 9. [133]Fig. 9 [134]Open in a new tab Relationship of SGSM1 expression with the OS in distinct clinical subgroups of KIRC. (A) T stage: T3&T4; (B) N stage: N0; (C) M stage: M0; (D) Pathologic stage: Stage III & Stage IV; (E) Gender: female; (F) Age ≤ 60; (G) Histologic grade: G3&G4; (H)Race: white Moreover, a lowered SGSM1 expression level was linked to an dismal DSS in the subgroup of T stage: T3&T4 (HR = 0.40, 95% CI: 0.24–0.64, p, Fig. [135]10A), subgroup of N stage: N0 (HR = 0.39, 95% CI: 0.22–0.68, p = 0.001, Fig. [136]10B), subgroup of M stage: M0 (HR = 0.26, 95% CI: 0.13–0.51, p < 0.001, Fig. [137]10C), subgroup of Pathologic stage: Stages III and IV (HR = 0.46, 95% CI: 0.30–0.72, p = 0.001, Fig. [138]10D), subgroup of Gender: female (HR = 0.21, 95% CI: 0.09–0.49, p < 0.001, Fig. [139]10E), subgroup of Age ≤ 60 (HR = 0.33, 95% CI: 0.18–0.63, p = 0.001, Fig. [140]10F), subgroup of Histologic grade: G3&G4 (HR = 0.36, 95% CI: 0.23–0.58, p < 0.001, Fig. [141]10G), and Race: white (HR = 0.33, 95% CI: 0.21–0.52, p < 0.001, Fig. [142]10H). Similarly, a lowered SGSM1 expression level predicted an unfavorable PFI in subgroup of T stage: T3&T4 (HR = 0.55, 95% CI: 0.37–0.81, p = 0.003, Fig. [143]11A), subgroup of N stage: N0 (HR = 0.50, 95% CI: 0.31–0.79, p = 0.003, Fig. [144]11B), subgroup of M stage: M0 (HR = 0.48, 95% CI: 0.31–0.74, p = 0.001, Fig. [145]11C), subgroup of Pathologic stage: Stages III and IV (HR = 0.60, 95% CI: 0.41–0.86, p = 0.006, Fig. [146]11D), subgroup of Gender: female (HR = 0.37, 95% CI: 0.20–0.70, p = 0.002, Fig. [147]11E), subgroup of Age ≤ 60 (HR = 0.44, 95% CI: 0.27–0.72, p = 0.001, Fig. [148]11F), subgroup of Histologic grade: G3&G4 (HR = 0.58, 95% CI: 0.41–0.848, p = 0.003, Fig. [149]11G), and subgroup of Race: white (HR = 0.45, 95% CI: 0.32–0.63, p < 0.001, Fig. [150]11H). Fig. 11. [151]Fig. 11 [152]Open in a new tab Relationship of SGSM1 expression with the PFI in distinct clinical subgroups of KIRC. (A)T stage: T3&T4; (B) N stage: N0; (C) M stage: M0; (D) Pathologic stage: Stage III&Stage IV; (E) Gender: female; (F) Age ≤ 60; (G) Histologic grade : G3&G4; (H)Race: white SGSM1 is associated with diverse clinical features in KIRC We additionally assessed the links between SGSM1 expression and other clinical parameters of KIRC, such as T stage, M stage, histological grade, and serum calcium levels(Table [153]2). The findings illustrated a lowered SGSM1 expression level in patients with T3/T4 (Fig. [154]12A), N1 (Fig. [155]12B), M1 (Fig. [156]12C), G3/G4 (Fig. [157]12D), stage III/IV (Fig. [158]12E), and Gender: male (Fig. [159]12F), respectively. Table 2. Association of SGSM1 expression with clinical-pathological parameters in patients with CcRCC Characteristic Low expression of SGSM1 High expression of SGSM1 p n 269 270 T stage, n (%) < 0.001 T1 115 (21.3%) 163 (30.2%) T2 35 (6.5%) 36 (6.7%) T3 111 (20.6%) 68 (12.6%) T4 8 (1.5%) 3 (0.6%) N stage, n (%) 0.009 N0 122 (47.5%) 119 (46.3%) N1 14 (5.4%) 2 (0.8%) M stage, n (%) 0.001 M0 203 (40.1%) 225 (44.5%) M1 53 (10.5%) 25 (4.9%) Pathologic stage, n (%) < 0.001 Stage I 111 (20.7%) 161 (30%) Stage II 28 (5.2%) 31 (5.8%) Stage III 72 (13.4%) 51 (9.5%) Stage IV 55 (10.3%) 27 (5%) Primary therapy outcome, n (%) 0.540 PD 6 (4.1%) 5 (3.4%) SD 2 (1.4%) 4 (2.7%) PR 0 (0%) 2 (1.4%) CR 48 (32.7%) 80 (54.4%) Gender, n (%) 0.091 Female 83 (15.4%) 103 (19.1%) Male 186 (34.5%) 167 (31%) Race, n (%) 0.609 Asian 4 (0.8%) 4 (0.8%) Black or African American 25 (4.7%) 32 (6%) White 237 (44.5%) 230 (43.2%) Age, n (%) 0.830 <=60 136 (25.2%) 133 (24.7%) > 60 133 (24.7%) 137 (25.4%) Histologic grade, n (%) < 0.001 G1 2 (0.4%) 12 (2.3%) G2 91 (17.1%) 144 (27.1%) G3 112 (21.1%) 95 (17.9%) G4 60 (11.3%) 15 (2.8%) Serum calcium, n (%) 0.014 Elevated 7 (1.9%) 3 (0.8%) Low 90 (24.6%) 113 (30.9%) Normal 89 (24.3%) 64 (17.5%) Hemoglobin, n (%) 1.000 Elevated 3 (0.7%) 2 (0.4%) Low 139 (30.3%) 124 (27%) Normal 101 (22%) 90 (19.6%) Laterality, n (%) 0.493 Left 130 (24.2%) 122 (22.7%) Right 138 (25.7%) 148 (27.5%) Age, median (IQR) 60 (52, 69) 61 (52, 72) 0.311 [160]Open in a new tab Fig. 12. [161]Fig. 12 [162]Open in a new tab Relationship of SGSM1 expression with distinct clinical features in KIRC. (A)T stage; (B) N stage; (C) M stage; (D) Histologic grade; (E) Pathologic stage; (F) Gender. ns, p ≥ 0.05; *p < 0.05; **p < 0.01; ***p < 0.001 Univariate and multivariate cox regression analyses in KIRC We included SGSM1 expression and clinical features in KIRC in the univariate and multivariate Cox regression analyses. Specifically, TNM stages, Histologic grade, age, and SGSM1 were all substantially related to OS as per the univariate analysis(Table [163]3). Additionally, M stage, age, and SGSM1 were all shown to be significant factors of OS in the multivariate Cox regression analysis (Table [164]3). Table 3. Univariate and multivariate regression analysis of SGSM1 expression Characteristics Total(N) Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value T stage(T3&T4 vs. T1&T2) 539 0.310 (0.229–0.420) < 0.001 0.674 (0.297–1.531) 0.346 M stage(M1 vs. M0) 506 0.228 (0.167–0.311) < 0.001 0.354 (0.209–0.598) < 0.001 Pathologic stage(III&IV vs. I& II) 536 0.253 (0.184–0.348) < 0.001 0.845 (0.334–2.140) 0.723 Histologic grade(G3&G4 vs. G1&G2) 531 0.370 (0.263–0.521) < 0.001 0.659 (0.395–1.101) 0.112 N stage(N1 vs. N0) 257 0.290 (0.154–0.546) < 0.001 0.680 (0.335–1.377) 0.283 Age( < = 60 vs. >60) 539 1.765 (1.298–2.398) < 0.001 1.703 (1.110–2.612) 0.015 SGSM1(low vs.high) 539 0.612 (0.514–0.728) < 0.001 0.753 (0.590–0.960) 0.022 [165]Open in a new tab Prognostic relevance of SGSM1 expression in KIRC Findings indicated that individuals with ccRCC showed high risk scores and reduced SGSM1 expression, whereas those with lower risk scores had increased SGSM1 expression.Then we analyzed the correlation between risk score, survival time, and SGSM1 expression profles (Fig. [166]13A). The risk factor plot showed that the group with high riskscore had a larger proportion of deaths. A further investigation of SGSM1 expression and subgroups was conducted in this study. Lower expression of SGSM1 was detected in the T3-T4 stage[HR = 0.57 (0.39–0.85), P = 0.006], pathologic stage III-IV[HR = 0.62 (0.43–0.90), P = 0.012], Histologic grade G3-G4 [HR = 0.54 (0.38–0.78), P = 0.001](Fig. [167]13B). A clinical prognostic risk score for ccRCC was developed using T stage, M stage, pathological grade, N stage, histological grade, gender, and SGSM1 expression(Fig. [168]13C). Furthermore, a calibration chart was employed to evaluate the model’s predictive accuracy(Fig. [169]13D). The SGSM1 expression might provide a more accurate prediction of patients’ survival chances over 3 and 5 years. Fig. 13. [170]Fig. 13 [171]Open in a new tab SGSM1 expression prognostic analysis (A)Risk scores and survival status of SGSM1 gene in KIRC patients. (B) Evaluation of the prognosis associated with SGSM1 expression in clinical subgroups. (C)A nomogram was developed utilizing the clinical characteristics of SGSM1 expression. (D) Multivariate Cox regression calibration chart displays the model’s predictive ability SGSM1 expression in relation to immune checkpoints and immune cell infiltration Moreover, the link between the expression of SGSM1 and several different immune checkpoints such as PDCD1, CD274, TIGIT, LAG-3, CD48, and CTLA4 was investigated (Fig. [172]14A). A negative relationship was observed between SGSM1 expression and that of PDCD1, TIGIT, LAG-3, CD48, and CTLA4 (P < 0.05 for all). Conversely, there was a positive link between CD274 and SGSM1 expression (P < 0.01, Fig. [173]14B-C). We additionally analyzed whether SGSM1 expression assessed by TIDE score correlates with immunotherapy sensitivity in a variety of tumors (e.g.,KIRC, BLCA, BRCA…) which showed as Figure S3A-F. Moreover, CIBERSORT was used to analyze the relationship between SGSM1 expression and immune cell infiltration which showed as Figure S3G-H.The expression of SGSM1 demonstrated significant correlations with Th2 cells (r = 0.397), and Tcm (r = 0.182), while exhibiting adverse associations with Treg (r=-0.417), and Th2 cells (r=-0.392)in KIRC, with all p-values < 0.001. Fig. 14. [174]Fig. 14 [175]Open in a new tab Relationship of SGSM1 expression and immune checkpoints in KIRC. (A) The link between SGSM1 expression and 6 immune checkpoints. (B) Single gene co-expression heat map of the immune checkpoints. (C) Correlation heat map of the immune checkpoints Discussion In the year 2007, the new SGSM1/2/3 protein family members were discovered to regulate the signaling pathways based on the small G proteins across a variety of tissues [[176]1]. In this study, we conducted a comprehensive bioinformatics analysis of SGSM1 using multiple public databases. Our pan-cancer analysis reveals that SGSM1 functions as a tumor suppressor in multiple malignancies, with significant downregulation in several cancer types (e.g., KIRC, BRCA) and robust associations with poor prognosis (PFI, DSS, OS). Moreover, the expression level of SGSM1 mRNA correlated with the clinicopathological stages of CESC, KICH, LUAD, and LIHC. Notably, SGSM1 expression correlates with immune subtypes and molecular pathways linked to Rab/Rap signaling, positioning it as a potential biomarker for precision oncology. The SGSM family, characterized by RUN and TBC domains, regulates small GTPases (RAB/RAP) to modulate vesicular transport and cell adhesion [[177]17]– [[178]18]. SGSM2, a homolog of SGSM1, suppresses metastasis in breast cancer by stabilizing E-cadherin [[179]19], while SGSM1 degradation promotes nasopharyngeal cancer progression [[180]6]. Snail expression is induced when SGSM2 is knocked down, which may facilitate the metastasis of non-small-cell lung cancer [[181]20]. Our findings extend these observations by demonstrating SGSM1’s pan-cancer tumor-suppressive role, potentially mediated through RAP1-mediated signaling (Fig. [182]6). To thoroughly examine the biological function of SGSM1, we undertook the GO and KEGG pathway enrichment analysis of its 37 targeted binding proteins. The findings illustrated that the BP was predominantly enriched in Rab/ Ras/ Rap protein signal transduction, MF was predominantly enriched in GTPase activity, GTP binding, and ribonucleoside binding, and the primary enriched pathways were Ras signaling pathway, Vasopressin-regulated water reabsorption, and Endocytosis. Our findings established the links between SGSM1 expression and various prognostic variables (PFI, DSS, or OS) in diverse clinical subgroups of KIRC. Furthermore, our results offer a more complete and further supplement and deepen the understanding of the function performed by SGSM1 in KIRC. The ROC curve as well as the KM survival curve in pan-cancer were subsequently used to evaluate SGSM1’s diagnostic and prognostic significance. We discovered that it was accurate in predicting 10 different types of cancer (AUC > 0.7), with a particularly high precision (AUC > 0.9) in the prediction of KIRP, CHOL, LUSC, and READ. SGSM1’s diagnostic accuracy (AUC > 0.9 in KIRP, CHOL) and prognostic independence in KIRC (Cox regression, p < 0.01) highlight its clinical utility. Presently, histological grade, disease pathology, and lymph node status are the most important clinical and histopathologic markers employed to evaluate the prognosis of patients with ccRCC. Numerous studies have outlined a variety of DSS and OS prognostic indicators, prediction algorithms, and gene signatures [[183]21–[184]24]. Integrating SGSM1 expression with existing biomarkers (e.g., GSDMB [[185]25], HADH [[186]26]) could refine risk stratification for ccRCC patients. Furthermore, its link to molecular subtypes (e.g., CIMP_low in ACC) provides a rationale for subtype-specific therapies. To date, however, no research has shown a link between SGSM1 genes and immunocytes in ccRCC. SGSM1’s inverse correlation with T-cell exhaustion markers (LAG3, CTLA4, PD-1) suggests its involvement in immune evasion. This aligns with recent advances in KIRC immunotherapy, where PD-1/CTLA4 inhibitors outperform traditional cytokine therapies [[187]27]. PD-1 increments are linked to improved PFS in advanced-stage melanoma survivors receiving nivolumab plus ipilimumab, according to previous studies [[188]28]. Targeting SGSM1 may synergize with immune checkpoint blockade by reversing T-cell dysfunction, a hypothesis supported by its association with T-cell infiltration (Figure S3). SGSM1 (Small G Protein Signaling Modulator 1) is known to interact with Rap and Ras GTPases, which are critical regulators of T-cell activation, differentiation, and exhaustion [[189]5]. Since Ras/Rap activation is essential for T-cell receptor (TCR) signaling and subsequent immune checkpoint expression, SGSM1 may indirectly dampen checkpoint gene expression by limiting Ras/Rap-dependent TCR and co-stimulatory signals. Ras/Rap hyperactivity is linked to increased PD-1 and CTLA-4 in exhausted T cells; thus, SGSM1-mediated suppression of these pathways could explain the inverse correlation. While correlative, the link between SGSM1 and immune checkpoints may stem from its role as a modulator of Ras/Rap signaling and downstream immune activation pathways [[190]1]. Mechanistic studies are needed to clarify whether SGSM1 directly suppresses checkpoint genes or does so indirectly by tempering T-cell stimulation. These insights could position SGSM1 as a novel target for immunotherapy resistance, where checkpoint inhibition is suboptimal. Despite these insights, our study has limitations. First, reliance on retrospective TCGA/GTEx data may introduce selection bias; prospective cohorts are needed to validate SGSM1’s prognostic utility. Second, the lack of experimental validation (e.g., CRISPR screens) limits mechanistic claims. Future work should: (1) dissect SGSM1-RAP1 interactions in vitro; (2) assess SGSM1’s impact on immunotherapy response in PDX models; (3) explore epigenetic regulation (e.g., SHISA3 methylation [[191]6]) in SGSM1-deficient tumors. Conclusions By systematically characterizing SGSM1’s pan-cancer roles, we bridge the gap between its molecular functions (RAP signaling, immune modulation) and clinical relevance (diagnosis, prognosis). This work not only establishes SGSM1 as a multi-faceted potential biomarker but also provides a framework for targeting GTPase-mediated pathways in precision oncology. Supplementary Information Below is the link to the electronic supplementary material. [192]Supplementary file1^ (5.5MB, docx) Acknowledgements