Comprehensive Pan-cancer Analysis Revealed CASP10 As a Promising Biomarker For Diverse Tumor Types

Abstract

We aimed to explore the comprehensive cancer landscape of Caspase-10 (CASP10). CASP10, a member of the caspase family, is located at the human chromosome locus 2q33-34. Studies have suggested its potential role in the development of certain cancers. To evaluate CASP10 expression in normal and pan-cancer tissues, we integrated data from The Cancer Genome Atlas (TCGA), GEO, Human Protein Atlas (HPA), and UALCAN databases. The diagnostic and prognostic significance of CASP10 was analyzed using Receiver Operating Characteristic (ROC), Cox regression, and Kaplan-Meier analysis. Correlations of CASP10 with clinical parameters were assessed via the Wilcoxon test, Kruskal-Wallis test, and logistic regression analysis. Genomic variations were explored with cBioPortal, GSCALite database, and UALCAN databases. LinkedOmics database was used to detect the function of CASP10 in pan-cancer. Interactions between CASP10 and the Tumor Immune Microenvironment (TIME) were investigated using TISIDB, TIMER2, and TISCH databases. The GSCALite database was utilized to assess the sensitivity of CASP10 to small-molecule drugs. In addition, Western Blotting (WB) was employed to detect the expression of the CASP10 in our clinical Liver Hepatocellular Carcinoma (LIHC) and Stomach Adenocarcinoma (STAD) cohorts. The transcription and protein expression of CASP10 significantly differ across cancer types, marking it as a biomarker for diagnosis and prognosis. Its expression correlated with certain clinical characteristics such as histological types and Alpha-Fetoprotein (AFP) levels. CASP10 gene exhibited a 2% alteration frequency across pan-cancer patients, with significant SNV and CNV profiles, and decreased methylation levels. CASP10 was closely related to the Nuclear Factor-κappa B (NF-κB), TNF, cell cycle, and JAK-STAT signal pathways. CASP10 showed correlation with immune components in the tumor microenvironment, including lymphocytes, immune stimulators, immune inhibitors, MHC molecules, chemokines, receptors, and Cancer-Associated Fibroblasts (CAFs). Importantly, CASP10 could predict the sensitivity of diverse anti-cancer drugs. Finally, WB analysis validated the overexpression of CASP10 in LIHC and STAD tissues. Our comprehensive bioinformatic analysis reveal the function of CASP10 on the diagnosis, prognosis, and progression of diverse cancer types.

Keywords

CASP10 Pan-cancer Diagnostic biomarker Prognostic biomarker Bioinformatic analysis

Introduction

Cancer remains a significant threat to global health, causing a profound health and economic burden on societies. The latest global cancer statistics report nearly 20 million new cancer cases in 2022, with lung, breast, colorectal, prostate, and stomach cancers being the most common.¹ Furthermore, cancer leads to 9.7 million deaths, with lung, colorectal, liver, breast, and stomach cancers being the primary types.¹ Experts predicted that by 2050, the number of new cancer cases would jump by 77% to 35 million, compared to 2022.¹ Beyond conventional treatments like surgery, radiotherapy, and chemotherapy, targeted therapy has emerged as a promising new approach in recent years.^2–4 We aim to use pan-cancer data to identify more effective treatment targets for various tumors.

Caspases (CASP), an evolutionarily conserved family of cysteine-aspartate specific proteases, play pivotal roles in apoptosis and the innate immune response.^5,6 They are categorized into initiator caspases, such as CASP2/8/9/10, which orchestrate the onset of apoptosis, and effector or executioner caspases, including CASP 3/6/7, which execute the apoptotic program, and, CASP1/4/5 participate in innate immunity.^7,8 Some CASPs are involved in non-apoptotic processes like cell proliferation, differentiation, and migration, contributing to metastasis and chemotherapy resistance in cancer, showcasing their diverse roles in cellular functions.⁹

CASP10, a member of the caspase family, is located on the human chromosome locus 2q33-34 and exhibits high homology with CASP8. It is found in all primates and select rodents like guinea pigs, but not in mice and rats.^5,10 Research suggests that CASP10 has been implicated in activating the Nuclear Factor-κappa B (NF-κB) pathway.¹¹ Additionally, CASP10 participates in a non-apoptotic or anti-apoptotic signaling pathway, facilitating NF-κB activation through interactions with RIP, NIK, and IKKalpha.¹² And, CASP10 is crucial for cell survival. For example, studies indicate that CASP10 is essential for the survival of all myeloma cell lines, as it prevents BCLAF1-induced autophagy.¹³ A study in HeLa cells reveale that CASP10 shifts the CD95L-mediated response from CASP8-induced cell death to NF-κB activation and cell survival.¹⁴ Furthermore, the CASP10-P13-tBID axis promotes terminal differentiation of human erythroid cells rather than apoptosis.¹⁵

Moreover, CASP10 is closely related to a variety of cancers. Such as, CASP10 is upregulated in clear cell renal cell carcinoma and positively correlates with tumor grade.¹⁶ Won et al. identified three somatic mutations in CASP10, including two missense and one nonsense mutation, which may induce apoptosis resistance and contribute to tumorigenesis in gastric cancer cells.¹⁷ Higher expression levels of CASP10 are also associated with unfavorable overall survival in gastric cancer patients.¹⁸ CASP10 mutations have been found in Non-Small Cell Lung Cancers (NSCLCs) and are associated with metastatic cases.¹⁹ Similarly, CASP10 mutations are found in colon cancers,²⁰ T-Acute Lymphoblastic Leukemia (T-ALL), and Multiple Myelomas (MMs).²¹ Loss of apoptotic function due to CASP10 gene mutations may contribute to the pathogenesis of human Non-Hodgkin Lymphoma (NHL).²² Polymorphisms in the CASP10 gene, such as rs13006529*T and rs13010627, have been identified as potential risk factors for breast cancer,^23,24 however, the CASP10 I410 allele may reduce breast cancer risk.²⁵ CASP10 expression is downregulated in rectal adenomas and cancers, with its mRNA levels correlating with tumor differentiation.²⁶

In addition, CASP10 is also associated with other diseases. For example, independent missense mutations in CASP10 underlie abnormal lymphocyte and dendritic cell homeostasis, and immune regulatory defects in patients with Autoimmune Lymphoproliferative Syndrome (ALPS) type II.²⁷ CASP10 variants may contribute to the pathogenesis of Primary Biliary Cholangitis (PBC) by dysregulating the inflammatory response.²⁸

Limited by its absence in rodent genomes, functional studies on CASP10 are incomplete, and its role in cancer progression remains unclear. Our comprehensive pan-cancer research aims to uncover CASP10’s non-apoptotic functions and assess its potential as a therapeutic target across diverse cancer types.

Materials and methods

Our study workflow is depicted in Figure 1.

Figure 1.

Systematic workflow of CASP10 in Pan-Cancer.

CASP10 expression profiles in human beings and Pan‑Cancer

We accessed the Human Protein Atlas (HPA) database (https://www.proteinatlas.org/) to obtain expression profiles of CASP10, including RNA and protein levels across human tissues, and RNA levels in single-cell tissues. Additionally, the HPA database provided immunohistochemical images showing CASP10 protein expression in normal and tumor tissues. Furthermore, the HPA database detailed the subcellular distribution of CASP10 within tumor cells.²⁹

We extracted gene expression profiles and clinical data for 33 tumor cases from The Cancer Genome Atlas (TCGA) database (https://www.cancer.gov/) to analyze CASP10 RNA expression in normal and tumor tissues. Furthermore, we examined CASP10 expression using the GSE76427 dataset from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). We employed the “ggplot2” package with R software to visualize the expression data. We further acquired the CASP10 expression profile from a variety of tumor cell lines through the Cancer Cell Line Encyclopedia (CCLE) dataset (https://portals.broadinstitute.org/ccle/about). Additionally, we utilized the CPTAC module of the UALCAN database (https://ualcan.path.uab.edu/index.html) to assess CASP10 protein expression across various cancer types.³⁰

The connection between CASP10 and clinical characteristics of cancer patients

We employed Receiver Operating Characteristic (ROC) curves to evaluate the diagnostic value of CASP10 in pan-cancer. Data analysis and visualization were conducted using the “pROC” and “ggplot2” packages with R software. Additionally, we used forest plots and Kaplan-Meier curves to examine the correlation between CASP10 expression and clinical outcomes, including Overall Survival (OS), Disease-Specific Survival (DSS), and Progression-Free Interval (PFI) in pan-cancer. We also applied the Wilcoxon or Kruskal-Wallis test to explore associations between CASP10 expression and clinicopathological characteristics such as histological type, age, Alpha-Fetoprotein (AFP) levels, Body Mass Index (BMI), pathologic N stage, gender, IDH status, and 1p/19q codeletion in pan-cancer. Lastly, logistic regression analysis was utilized to analyze the relationship between CASP10 expression and a range of clinical characteristics, including pathologic T stage, pathologic stage, tumor status, gender, age, BMI, residual tumor, histologic grade, AFP levels, Albumin (g/dl), prothrombin time, Child-Pugh grade, fibrosis ishak score, vascular invasion, and adjacent hepatic tissue inflammation in LIHC.

Genomic variations of CASP10 in Pan-Cancer

We performed analysis with the cBioPortal database (https://www.cbioportal.org) to identify CASP10 genetic mutations in pan-cancer.³¹ Tumor Mutational Burden (TMB) scores from the TCGA database were subjected to Spearman’s correlation analysis to assess their association with CASP10. We also used the GSCALite database to analyze the SNV and CNV profiles of CASP10 in pan-cancer.³² Additionally, the UALCAN database (http://ualcan.path.uab.edu/analysis.html) was employed to examine CASP10 methylation profiles across various cancers and their adjacent normal tissues.³⁰

Functional enrichment analysis of CASP10

Utilizing the LinkedOmics database (http://www.linkedomics.org/login.php),³³ we identified genes correlated with CASP10 through the Spearman’s correlation analysis and visualized the results with volcano plots. Subsequently, we conducted a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to explore the pathway associations of CASP10 and its related genes.

Related genes analysis of CASP10

We analyzed RNA-sequencing data from the TCGA database to identify genes related to CASP10. Additionally, we mapped the PPI network for CASP10 using the STRING database (https://cn.string-db.org/).³⁴ Lastly, we explored the correlation of CASP10 and its related genes in pan-cancer through Spearman’s correlation analysis in the Tumor Immune Estimation Resource 2.0 (TIMER2) database (http://timer.cistrome.org/).³⁵

Relationships between CASP10 and tumor immune microenvironment (TIME)

Using the TISIDB database (http://cis.hku.hk/TISIDB/index.php),³⁶ we investigated the correlations between CASP10 expression and various immune regulatory factors across different cancer types. These factors included Tumor-Infiltrating Lymphocytes (TILs), immune stimulators and inhibitors, Major Histocompatibility Complex (MHC) molecules, chemokines, and receptors.

Relationships between CASP10 and cancer-associated fibroblasts (CAFs)

Using the Extended Polydimensional Immunome Characterization (EPIC), Microenvironment Cell Populations-counter (MCPCOUNTER), XCELL, and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms from the TIMER2 database,³⁵ we evaluated the association between CASP10 expression and fibroblast infiltration in various cancers. Results were visualized using heatmaps and scatter plot.

Expression profiles of CASP10 in single cell

We analyzed CASP10 expression profiles in single-cell data obtained from the TISCH database. Using the R packages “MAESTRO” and “Seurat,” we performed thorough data processing and analysis, followed by re-clustering of cells into distinct groups using the t-SNE method.

The immunotherapies and drug therapies of CASP10

We assessed the TIDE score based on CASP10 expression levels in cancers to predict immunotherapy efficacy, using the R packages “ggplot2” and “ggpubr”.³⁷ Additionally, we explored the relationship between CASP10 expression and drug responsiveness across various cancers using the GSCALite database.³²

Tissue specimens

We collected clinical samples, including five pairs of Liver Hepatocellular Carcinoma (LIHC) and Stomach Adenocarcinoma (STAD) tissues along with their adjacent non-tumorous tissues, for Western Blotting (WB) analysis. For both LIHC and STAD, we included patients who underwent surgical treatment and were confirmed through pathological diagnosis. We excluded patients with previous antitumor therapy, concurrent malignancies, or other diseases. This study was approved by the Ethics Committees of the Affiliated Hospital of Guilin Medical University (Agreement Number: 2021WJWZC14) and followed the Declaration of Helsinki, with all participants signing informed consent.

Statistical analysis

We performed statistical analyses using R software (version 4.0.3), as detailed in the bioinformatics methods section. A p-value of less than 0.05 indicated statistical significance.

Results

Expression profile of CASP10 in human beings

Supplemental Figure S1(a) demonstrated the expression of CASP10 mRNA and protein across diverse human organs and tissues. Specifically, Supplemental Figure S1(b) showed that CASP10 mRNA was predominantly expressed in the duodenum, bone marrow, small intestine, spleen, rectum, colon, lung, tonsil, stomach, and placenta, as indicated by the consensus dataset. Moreover, HPA database data showed that CASP10 protein was mainly localized in the testis, parathyroid gland, adrenal gland, lung, stomach, duodenum, small intestine, colon, rectum, and urinary bladder (Supplemental Figure S1(c)). Supplemental Figure S1(d)–(k) elaborated on intricate CASP10 expression in different single-cell tissues, such as breast, colon, kidney, liver, lung, ovary, pancreas, and prostate.

Expression profile of CASP10 in Pan-Cancer

Initially, we used the TCGA database to identify the expression profile of CASP10 in various unpaired samples, revealing significant variations across 16 different cancer types, including Breast Invasive Carcinoma (BRCA), Cholangiocarcinoma (CHOL), Colon Adenocarcinoma (COAD), Glioblastoma Multiforme (GBM), Head and Neck Squamous Cell Carcinoma (HNSC), Kidney Chromophobe (KICH), Kidney Renal Clear Cell Carcinoma (KIRC), Kidney Rrenal Papillary Cell Carcinoma (KIRP), LIHC, Lung Adenocarcinoma (LUAD), Lung Squamous Cell Carcinoma (LUSC), Prostate Adenocarcinoma (PRAD), Rectum Adenocarcinoma (READ), STAD, Thyroid Carcinoma (THCA), and Uterine Corpus Endometrial Carcinoma (UCEC) (Figure 2(a)). Additionally, by analyzing paired samples from the TCGA database, we noted significant variations in CASP10 expression among 13 cancer types, including BRCA, CHOL, COAD, HNSC, KICH, KIRC, LIHC, LUAD, LUSC, PRAD, STAD, THCA, and UCEC (Figure 2(b)). And, upon analyzing the data from GSE76427 LIHC, we discovered that CASP10 is highly expressed in LIHC (Figure 2(c)).

Figure 2.

Expression profile of CASP10 in Pan‑Cancer: (a) and (b) Expression profile of CASP10 in 33 unpaired (a) and 23 paired (b) tumor samples within the TCGA database, (c) CASP10 mRNA expression level in GSE76427 LIHC within the GEO database, (d) CASP10 mRNA expression level in a range of cancer cell lines within the CCLE database, and (e) CASP10 protein expression level in normal and tumor tissues within the HPA database. (ns: no significance; * p < 0.05; ** p < 0.01; *** p < 0.001).

Furthermore, we obtained the CASP10 expression matrix of tumor cell lines from the CCLE dataset and found that CASP10 is mainly expressed in Multiple Myeloma (MM), Acute Lymphoblastic Leukemia (ALL), LCML, LAML, Chronic Lymphocytic Leukemia (CLL), Pancreatic Adenocarcinoma (PAAD), Cervical squamous cell carcinoma (CESC), KIRC, STAD, and LIHC cancer cell lines (Figure 2(d)). Immunohistochemical staining data from the HPA database showed reduced CASP10 expression in normal samples and increased levels in LIHC, Renal Cancer, and THCA (Figure 2(e)). We also employed the CPTAC module of the UALCAN database to compare CASP10 protein expression levels between normal and tumor tissues, revealing higher expression in clear cell RCC, UCEC, PAAD, head and neck, and glioblastoma, and lower expression in breast, colon, and liver cancers (Supplemental Figure S2(a)). The HPA database also revealed the subcellular distribution of CASP10, primarily localized in the Golgi apparatus and vesicles in A-431 and U-20S cells, and predominantly in the Golgi apparatus in Hep-G2 cells (Supplemental Figure S2(b)).

Diagnostic analysis of CASP10 in Pan-Cancer

Utilizing ROC analysis, we explored the diagnostic potential of CASP10 in pan-cancer, and found the promising role of CASP10 as a reliable biomarker in BRCA (AUC = 0.805, 95% CI: 0.768–0.841), CESC (AUC = 0.721, 95% CI: 0.554–0.888), CHOL (AUC = 0.981, 95% CI: 0.947–1.000), COAD (AUC = 0.776, 95% CI: 0.712–0.840), Esophageal Adenocarcinoma (ESAD) (AUC = 0.764, 95% CI: 0.552–0.976), GBM (AUC = 0.886, 95% CI: 0.833-0.940), KICH (AUC = 0.983, 95% CI: 0.964–1.000), LIHC (AUC = 0.728, 95% CI: 0.668–0.788), LUSC (AUC = 0.904, 95% CI: 0.876–0.932), Oral Squamous Cell Carcinoma (OSCC) (AUC = 0.733, 95% CI: 0.639–0.827), READ (AUC = 0.711, 95% CI: 0.587–0.834), STAD (AUC = 0.727, 95% CI: 0.634–0.820), THCA (AUC = 0.738, 95% CI: 0.692–0.784), Thymoma (THYM) (AUC = 0.712, 95% CI: 0.435–0.990), and UCEC (AUC = 0.761, 95% CI: 0.692–0.829) (Figure 3).

Figure 3.

ROC curves of CASP10 in Pan-Cancer. The diagnostic value of CASP10 in BRCA, CESC, CHOL, COAD, ESAD, GBM, KICH, LIHC, LUSC, OSCC, READ, STAD, THCA, THYM, and UCEC was exhibited by ROC analysis.

Survival analysis of CASP10 in Pan-Cancer

We employed Cox regression analysis and Kaplan-Meier analysis as prognostic indicators to assess the prognostic value of CASP10. Our Cox regression analysis indicated that CASP10 expression was significantly associated with Overall Survival (OS) in Lower-Grade Glioma (LGG) (HR = 2.096, 95% CI: 1.473–2.982, p < 0.001), Mesothelioma (MESO) (HR = 0.607, 95% CI: 0.380-0.971, p = 0.0372), Sarcoma (SARC) (HR = 0.665, 95% CI: 0.440–0.976, p = 0.0373), Skin Cutaneous Melanoma (SKCM ) (HR = 0.655, 95% CI: 0.500–0.859, p = 0.0022), THYM (HR = 0.106, 95% CI: 0.013–0.870, p = 0.0367), UCEC (HR = 1.564, 95% CI: 1.036–2.362, p = 0.0334), and UVM (HR = 4.728, 95% CI: 1.748–12.790, p = 0.0022) (Figure 4(a)). And, CASP10 expression was significantly associated with Disease-Specific Survival (DSS) in KIRP (HR = 2.284, 95% CI: 1.033–5.049, p = 0.0413), LGG (HR = 2.216, 95% CI: 1.522–3.226, p < 0.001), SARC (HR = 0.631, 95% CI: 0.407–0.977, p = 0.0391), and SKCM (HR = 0.621, 95% CI: 0.465–0.829, p = 0.0012) (Figure 4(b)). Moreover, CASP10 expression was significantly associated with Progression-Free Interval (PFI) in BRCA (HR = 0.635, 95% CI: 0.455–0.884, p = 0.0072), COAD (HR = 0.680, 95% CI: 0.477–0.970, p = 0.0332), KIRP (HR = 1.761, 95% CI: 1.040–2.981, p = 0.0351), LGG (HR = 1.644, 95% CI: 1.248–2.165, p = 0.0004), LIHC (HR = 1.511, 95% CI: 1.128–2.025, p = 0.0056), SKCM (HR = 0.740, 95% CI: 0.591–0.928, p = 0.0090), UCEC (HR = 1.641, 95% CI: 1.154–2.334, p = 0.0058), and Uveal Melanoma (UVM) (HR = 3.439, 95% CI: 1.510–7.830, p = 0.0033) (Figure 4(c)).

Figure 4.

The correlation between CASP10 expression and clinical outcomes in Pan-Cancer: (a–c) The influence of CASP10 expression on OS (a), DSS (b), and PFI (c) was exhibited by forest plots in pan-cancer. (d)–(f) The influence of CASP10 expression on OS (d), DSS (e), and PFI (f) was exhibited by Kaplan-Meier curves in ACC, LGG, LIHC, and UVM.

Our Kaplan-Meier analysis indicated that elevated CASP10 expression served as a risk factor in Adrenocortical Carcinoma (ACC) (HR = 2.40, 95% CI: 1.14–5.08, p = 0.022), LGG (HR = 2.63, 95% CI: 1.86–3.72, p < 0.001), LIHC (HR = 1.89, 95% CI: 1.33–2.71, p < 0.001), and UVM (HR = 5.00, 95% CI: 1.96–12.78, p < 0.001) for OS (Figure 4(d)). Similarly, for DSS, overexpression of CASP10 was a hazard factor in ACC (HR = 2.37, 95% CI: 1.09–5.15, p = 0.030), LGG (HR = 2.72, 95% CI: 1.89–3.92, p < 0 .001), LIHC (HR = 1.63, 95% CI: 1.02-2.58, p = 0.039), and UVM (HR = 5.73, 95% CI: 2.08–15.76, p < 0.001) (Figure 4(e)). And, CASP10 expression was significantly correlated with PFI in ACC (HR = 2.77, 95% CI: 1.48–5.19, p = 0.001), LGG (HR = 1.96, 95% CI: 1.46–2.63, p < 0.001), LIHC (HR = 1.51, 95% CI: 1.13–2.03, p = 0.005), and UVM (HR = 4.38, 95% CI: 1.90–10.06, p < 0.001) (Figure 4(f)).

Connection between CASP10 and clinical characteristics

We subsequently analyzed the relationship between CASP10 expression and clinical characteristics in different tumors. In UCEC, tumors with mixed or serous histological types demonstrated higher CASP10 expression than those with endometrioid histology (Figure 5(a)). Additionally, CASP10 expression was upregulated in older patients (>60 years) compared to the younger patients (⩽60 years) in UCEC (Figure 5(b)). In LIHC, individuals with AFP > 400 ng/ml had higher levels of CASP10 expression than those with AFP ⩽ 400 ng/ml (Figure 5(c)). And, in UVM, patients with a BMI >30 showed increased CASP10 expression compared to those with a BMI ⩽ 30 (Figure 5(d)). In KIRP, CASP10 expression was higher in N1 and N2 stages than in N0, and it was also upregulated in patients with a BMI >30 compared to those with a BMI ⩽30 (Figure 5(e) and (f)). Conversely, older patients (>60 years) and male patients in KIRP exhibited lower CASP10 expression levels (Figure 5(g) and (h)). In GBM, male patients demonstrated higher CASP10 expression than female patients (Figure 5(i)). In LGG, tumors harboring a mutated Isocitrate Dehydrogenase (IDH) status exhibited decreased CASP10 expression in comparison to those with the wild-type lDH status (Figure 5(j)). Furthermore, LGG patients with 1p/19q codeletion had reduced CASP10 expression compared to those without codeletion (Figure 5(k)). Notably, LGG tumors classified as oligoastrocytoma or oligodendroglioma histologies expressed lower CASP10 than those of astrocytoma histology (Figure 5(l)).

Figure 5.

The connection between CASP10 expression and clinical characteristics: (a) and (b) CASP10 expression was related to histological type and age in UCEC, (c) CASP10 expression was correlated with AFP levels in LIHC, (d) CASP10 expression was associated with BMI in UVM, (e–h) CASP10 expression was correlated with pathologic N stage, BMI, age, and gender in KIRP, (i) CASP10 expression was related to gender in GBM, and (j–l) CASP10 expression was associated with IDH status, 1p/19q codeletion, and histological type in LGG. (*p < 0 .05; **p < 0 .01; *** p < 0 .001).

Based on TCGA data, we compiled clinical records of 374 LIHC cases and utilized logistic regression to assess the relationship between CASP10 expression and LIHC clinicopathological traits. Our analysis showed a strong correlation between CASP10 expression and both histologic grade (p = 0 .007) and AFP levels (p = 0 .004), as presented in Table 1.

Table 1.

CASP10 expression correlated with clinical characteristics by logistic regression.

Characteristics	Total (N)	OR (95% CI)	P value
Pathologic T stage (T2&T3&T4 vs T1)	371	1.202 (0.799–1.806)	0.377
Pathologic stage (Stage III&Stage IV vs Stage I&Stage II)	350	1.095 (0.678–1.769)	0.711
Tumor status (With tumor vs Tumor free)	355	1.525 (0.999–2.326)	0.050
Gender (Male vs Female)	374	0.692 (0.448–1.070)	0.098
Age (>60 vs ⩽60)	373	0.686 (0.456–1.032)	0.070
BMI (>25 vs ⩽25)	337	1.037 (0.676–1.590)	0.868
Residual tumor (R1&R2 vs R0)	345	0.845 (0.325–2.196)	0.730
Histologic grade (G3&G4 vs G1&G2)	369	1.803 (1.175–2.767)	0.007
AFP(ng/ml) (>400 vs ⩽400)	280	2.351 (1.322–4.179)	0.004
Albumin(g/dl) (⩾3.5 vs <3.5)	300	1.477 (0.856–2.548)	0.161
Prothrombin time (>4 vs ⩽4)	297	1.155 (0.703–1.899)	0.569
Child-Pugh grade (B&C vs A)	241	1.327 (0.550–3.199)	0.529
Fibrosis ishak score (1/2&3/4&5&6 vs 0)	215	1.310 (0.745–2.301)	0.348
Vascular invasion (Yes vs No)	318	1.139 (0.718–1.809)	0.580
Adjacent hepatic tissue inflammation (Mild&Severe vs None)	237	0.919 (0.552–1.529)	0.745

Boldface indicates statistical significance (p < 0.05).

Genomic variations of CASP10 in Pan-Cancer

The cBioportal database indicated that CASP10 gene alterations were present in 2% (56/2583) of pan-cancer patients (Figure 6(a)). The predominant alteration patterns included mutation, amplification, deep deletion, and multiple alterations. Bone cancer exhibited the highest alteration frequency, followed by bladder and ovarian cancers (Figure 6(b)). The CASP10 gene featured 12 mutation sites within the 0–521 amino acid region (Figure 6(c)). Additionally, a positive correlation between CASP10 expression and TMB was observed in 6 cancer types (Figure 6(d)).

Figure 6.

CASP10 gene mutation features in Pan-Cancer: (a) CASP10 gene mutation pattern in pan-cancer, (b) CASP10 gene mutation frequencies and types in pan-cancer, (c) CASP10 gene mutation sites in pan-cancer, and (d) CASP10 expression was correlated with TMB in cancers.

We next analyzed the SNV and CNV profiles of CASP10 using the GSCALite database. The heatmap of SNV percentages revealed that UCEC, SKCM, and STAD had the top three SNV deleterious mutations for CASP10, at 20, 17, and 10 respectively (Supplemental Figure S3(a)). Missense mutations were the primary variant classification, Single Nucleotide Polymorphisms (SNPs) were the main variant type, and C>T was the predominant SNV class (Supplemental Figure S3(b)). In Supplemental Figure S4(a), Heterozygous amplifications and deletions were common CNV types for CASP10 in pan-cancer. Supplemental Figure S4(b) demonstrated a positive correlation between CASP10 CNV and mRNA levels in 16 of the 33 cancer types. In our analysis of CASP10 CNV, we observed that heterozygous amplifications predominantly occurred in Uterine Carcinosarcoma (UCS) (41.1%), Ovarian Serous Cystadenocarcinoma (OV) (33.3%), and Testicular Germ Cell Tumors (TGCT) (30.0%), while heterozygous deletions were most frequent in KICH (71.2%), Bladder Urothelial Carcinoma (BLCA) (33.6%), and SARC (29.6%) (Supplemental Figure S4(c)). Homozygous amplifications were most common in LUSC (3.0%), OV (2.9%), and SARC (1.9%), and homozygous deletions were most prevalent in BLCA (2.5%), UVM (1.3%), and CESC (1.0%) (Supplemental Figure S4(d)). Additionally, high CASP10 expression was associated with a higher TP53 mutation rate in UCEC and LUAD, based on the mutation profiles (Supplemental Figures S5 and S6). Furthermore, the UALCAN database highlighted a downregulation of CASP10 methylation in BLCA, CESC, HNSC, KIRC, KIRP, LIHC, LUAD, Pheochromocytoma and Paraganglioma (PCPG), UCEC, READ, and THCA tissues relative to normal tissues. In contrast, PRAD tissues exhibited an upregulation of CASP10 methylation levels compared to the normal tissues (Figure 7).

Figure 7.

DNA methylation levels of CASP10 in pan-cancer. The promoter methylation level of CASP10 in various cancers compared to the normal tissues.

Functional enrichment analysis of CASP10 in Pan-Cancer

Subsequently, we used the LinkedOmics database to explore the role of CASP10 in tumor tissue. In LAML, 1,347 genes had a significant positive relationship with CASP10, and 1,514 genes had a negative one (Figure 8(a)). KEGG pathway analysis revealed close associations between CASP10 and both the NF-κB and TNF signaling pathways (Figure 8(b)). In GBM, 2,825 genes had a significant positive relationship with CASP10, and 1,571 genes had a negative one (Figure 8(c)). KEGG pathway analysis indicated a close relationship between CASP10 and NF-κB signaling pathway (Figure 8(d)). In LGG, 6,018 genes had a significant positive relationship with CASP10, and 4,879 genes had a negative one (Figure 8(e)). KEGG pathway analysis indicated a close relationship between CASP10 and NF-κB signaling pathway (Figure 8(f)). In KIPAN, 9,514 genes had a significant positive relationship with CASP10, and 5,816 genes had a negative one (Figure 8(g)). KEGG pathway analysis indicated a close relationship between CASP10 and the TNF signaling pathway (Figure 8(h)). In THYM, 2,424 genes had a significant positive relationship with CASP10, and 1,312 genes had a negative one (Figure 8(i)). KEGG pathway analysis indicated a close relationship between CASP10 and cell cycle (Figure 8(j)). In BLCA, 2,257 genes had a significant positive relationship with CASP10, and 1,815 genes had a negative one (Supplemental Figure S7(a)). KEGG pathway analysis indicated a close relationship between CASP10 and the NF-κB signaling pathway (Supplemental Figure S7(b)). In DLBC, 2,241 genes had a significant positive relationship with CASP10, and 1,217 genes had a negative one (Supplemental Figure S7(c)). KEGG pathway analysis revealed close associations between CASP10 and both the TNF signaling pathways and pathways in cancer (Supplemental Figure S7(d)). In UCEC, 1,917 genes had a significant positive relationship with CASP10, and 507 genes had a negative one (Supplemental Figure S7(e)). KEGG pathway analysis indicated a close relationship between CASP10 and the JAK-STAT signaling pathway (Supplemental Figure S7(f)). In LIHC, 5,310 genes had a significant positive relationship with CASP10, and 1,639 genes had a negative one (Supplemental Figure S8(a)). KEGG pathway analysis indicated a close relationship between the CASP10 pathway in cancer (Supplemental Figure S8(b)). In PRAD, 7,174 genes had a significant positive relationship with CASP10, and 3,530 genes had a negative one (Supplemental Figure S8(c)). KEGG pathway analysis indicated a close relationship between the CASP10 pathway in cancer (Supplemental Figure S8(d)). In KIRC, 6,699 genes had a significant positive relationship with CASP10, and 2,944 genes had a negative one (Supplemental Figure S8(e)). KEGG pathway analysis indicated a close relationship between the CASP10 pathway in cancer (Supplemental Figure S8(f)).

Figure 8.

KEGG analysis of CASP10: (a) Spearman’s test identified correlated genes of CASP10 in LAML, (b) KEGG analysis revealed CASP10-associated pathways in LAML, (c) Spearman’s test identified correlated genes of CASP10 in GBM, (d) KEGG analysis revealed CASP10-associated pathways in GBM, (e) Spearman’s test identified correlated genes of CASP10 in LGG, (f) KEGG analysis revealed CASP10-associated pathways in LGG, (g) Spearman’s test identified correlated genes of CASP10 in KIPAN, (h) KEGG analysis revealed CASP10-associated pathways in KIPAN, (i) Spearman’s test identified correlated genes of CASP10 in THYM, and (j) KEGG analysis revealed CASP10-associated pathways in THYM.

Analysis of co-expression genes of CASP10

We analyzed RNA-seq data from the TCGA database to identify the CASP10 co-expression genes in LIHC. A heat map highlights the top 30 genes strongly correlated with CASP10 (Figure 9(a)). We selected the top 10 genes with the highest interaction scores and analyzed their relationship with CASP10. The chord diagram showed positive associations between CASP10 and its co-expression genes, including PRKCI, WDFY1, TMEM131, DENND5A, CASP8, CREB1, TRAFD1, STK4, DCP1A, and MOB1A (Figure 9(b)). Notably, the TIMER2 database revealed a positive correlation between CASP10 and its co-expressed genes in pan-cancer (Figure 9(c)).

Figure 9.

Analysis of related genes of CASP10: (a) A heat map illustrated the top 30 genes correlated with CASP10 in LIHC, (b) A chord diagram represented the interconnections between CASP10 and its top 10 associated genes, (c) The correlation between CASP10 and its top 10 related genes in pan-cancer, (d) PPI analysis illustrated the top 30 genes correlated with CASP10, (e) A chord diagram represented the interconnections between CASP10 and its top 10 associated genes, and (f) The correlation between CASP10 and its top 10 related genes in pan-cancer.

At the protein level, we used the STRING tool to identify the top 30 proteins interacting with CASP10 (Figure 9(d)). We then selected the top 10 proteins with the highest interaction scores and found positive relationships between CASP10 and its co-expressed partners, including CASP3, CASP7, CASP8, FADD, BID, FASLG, FAS, TRADD, RIPK1, and CFLAR (Figure 9(e)). The TIMER2 database also showed positive relationships between CASP10 and these 10 co-expressed partners in pan-cancer (Figure 9(f)).

Among the identified co-expressed genes, we discovered that elevated expression of CASP3, CASP7, DCP1A, STK4, MOB1A, CREB1, DENND5A, TRAFD1, WDFY1, and PRKCI were significantly associated with adverse survival outcomes in LIHC patients, as evidenced by HR ranging from 1.49 to 1.88, with statistical significance (p < 0 .05) (Figure 10).

Figure 10.

Survival analysis of related genes of CASP10 in LIHC. The influence of CASP3, CASP7, DCP1A, STK4, MOB1A, CREB1, DENND5A, TRAFD1, WDFY1, and PRKCI expression on OS were exhibited by Kaplan-Meier curves in LIHC.

Analysis of relationships between CASP10 and TIME

We utilized the TISIDB database to analyze the relationships between CASP10 and key components of the TIME, such as lymphocytes, immune stimulators, immune inhibitors, MHC molecules, chemokines, and receptors. Our analysis revealed strong associations between CASP10 and some lymphocyte subsets, notably NK in LGG and Th1 in UVM (Figure 11(a)). For immune stimulators, CASP10 positively correlated with C10orf54 in SARC and negatively with PVR in KICH (Figure 11(b)). Regarding immune inhibitors, CASP10 showed a positive relationship with CSF1R in GBM and a negative relationship with PVRL2 in TGCT (Figure 11(c)). Significant correlations were also observed between CASP10 and MHC molecules, such as HLA-DRB1 in GBM and HLA-DOA in LGG (Figure 11(d)). In the analysis of chemokines, CASP10 expression positively correlated with CCL28 in ESCA and negatively correlated with CXCL17 in TGCT (Figure 11(e)). Additionally, CASP10 expression positively correlated with CXCR3 in TGCT and CCR5 in BRCA (Figure 11(f)).

Figure 11.

Relationships between CASP10 and TIME: (a)–(f) Relationships between CASP10 expression and lymphocyte (a), immune stimulator (b), immune inhibitor (c), MHC molecules (d), chemokine (e), and receptor (f). Red indicated positive correlations, while blue signified negative correlations.

Analysis of relationships between CASP10 and CAFs

CAFs in the tumor stroma regulate various immune cells and promote tumor progression. Utilizing the EPIC, MCPCOUNTER, XCELL, and TIDE algorithms in the TIMER2 database, we examined the correlation between CASP10 expression and fibroblast infiltration across diverse malignancies. Our analysis, based on EPIC, MCPCOUNTER, and TIDE algorithms, revealed a positive correlation between CASP10 levels and CAF infiltration in multiple cancer types, including BRCA, BRCA-LumA, KIRC, KIRP, LGG, LIHC, PCPG, PRAD, SKCM-Primary, THCA, and UVM (Figure 12(a)). Representative scatter plots were shown in Figure 12(b).

Figure 12.

Relationships between CASP10 and CAFs: (a) The heatmap displayed the relationships between CASP10 expression and fibroblast infiltration in pan-cancer based on EPIC, MCPCOUNTER, XCELL, and TIDE algorithms and (b) The scatter plot displayed the correlation between CASP10 levels and CAFs infiltration in BRCA, BRCA-LumA, KIRC, KIRP, LGG, LIHC, PCPG, PRAD, SKCM-Primary, THCA, and UVM.

Analysis of the expression pattern of CASP10 in single-cell

We analyzed single-cell data from the TISCH database to detect the expression pattern of CASP10. Figure 13(a) showed that the LIHC_GSE166635 dataset contains various cell types, including B cells, CD8T cells, DC cells, and other cell types. Notably, CASP10 expression levels vary widely among these cells (Figure 13(b)). Endothelial cells exhibited the highest CASP10 expression, followed by Tprolif cells and B cells (Figure 13(c)). Similarly, the THCA_GSE148673 dataset revealed a range of cell types, including B cells, CD4Tconv, CD8T cells, and other cell types, with varying CASP10 expression (Figure 13(d) and (e)). Endothelial cells had the highest expression, followed by DCs and B cells (Figure 13(f)). The STAD_GSE167297 dataset, which included B cells, CD8 T cells, DC cells, and other cell types, and showed varied CASP10 expression (Figure 13G, H). Plasma cells exhibited the highest CASP10 expression, followed by endothelial and epithelial cells (Figure 13(i)). Lastly, the HNSC_GSE103322 dataset, comprising CD4Tconv cells, CD8T cells, CD8Tex cells, and other cell types, displaied distinct CASP10 expression profiles (Figure 13(j) and (k)). Plasma cells demonstrated the highest expression, followed by endothelial cells and momo macro cells (Figure 13(l)).

Figure 13.

Expression pattern of CASP10 in single cell: (a) The single-cell types in the LIHC_GSE166635 dataset. (b, c) The expression levels of CASP10 in the single cells of LIHC_GSE166635 dataset. (d) The single-cell types in THCA_GSE148673 dataset. (e, f) The expression levels of CASP10 in the single cells of the THCA_GSE148673 dataset, (g) The single-cell types in STAD_GSE167297 dataset, (h, i) The expression levels of CASP10 in the single celles of STAD_GSE167297 dataset, (j) The single-cell types in HNSC_GSE103322 dataset, and (k, l) The expression levels of CASP10 in the single cells of the HNSC_GSE103322 dataset.

Exploring the potential of CASP10 in cancer immunotherapies and drug therapies

Using the TIDE algorithm, we evaluated the potential immunotherapy efficacy in cancer patients classified by CASP10 expression levels. In LIHC and THCA, the low CASP10 expression group showed lower TIDE scores compared to the high CASP10 group, which had higher TIDE scores (Figure 14(a) and (b)). Furthermore, we observed a negative relationship between CASP10 and drug sensitivities in the GSCALite database. The GDSC database analysis identified TAK-715, Z-LLNle-CHO, and AICAR as drugs with the most significant inverse correlation, as shown in Figure 14(c). Similarly, the CTRP database identified bosutinib, neratinib, and lapatinib as drugs with the highest negative association, as shown in Figure 14(d).

Figure 14.

The immunotherapies and drug therapies of CASP10: (a, b) Comparative TIDE scores for groups stratified by CASP10 expression levels in LIHC (a) and THCA (b), (c, d). The role of CASP10 in predicting drug therapy outcomes in pan-cancer, as reflected in the GDSC (c) and CTRP (d) databases. (***p < 0 .001).

WB validation of CASP10

Our WB analysis of CASP10 expression in LIHC and STAD cancer tissues and their corresponding non-neoplastic tissues showed overexpression in malignant tissues compared to adjacent non-malignant tissues (Figure 15(a)–(d)). These findings confirm elevated CASP10 levels in human malignancies.

Figure 15.

WB analysis of CASP10 expression in tumor tissues: CASP10 expression in LIHC (a) and (b) and STAD (c) and (d) by WB analysis. (*p < 0.05).

Discussion

Genome-wide pan-cancer analysis enables simultaneous examination of multiple cancer types, offering a comprehensive view of gene mutations and expression profile, and facilitating the identification of sensitive biomarkers for cancer diagnosis and targeted therapy.^38–41

Beyond their well-known role in programmed cell death, CASPs are increasingly recognized for their non-apoptotic functions. For instance, elevated CASP1 levels correlate with poorer survival in pancreatic cancer patients, and its inhibition significantly reduces cell viability and migration in pancreatic cancer cells.⁴² CASP2 inhibits the chaperone-facilitated autophagic degradation of GPX4, thereby enhancing the survival of cancer cells with mutant p53.⁴³ CASP3 increases colon cancer resistance to radiotherapy and chemotherapy, and also promotes the invasion and metastasis of colon cancer cells.⁴⁴ Phosphorylated CASP8 at Y380 triggers NFκB activation in glioblastoma, enhancing inflammation and angiogenesis.⁴⁵ CASP9 can regulate the survival of A549 and HCT116 tumor cells.⁴⁶ Based on these findings, we propose that CASP10 also possesses non-apoptotic functions, and our analyses provide a foundation for exploring these functions.

The results of immunofluorescence staining revealed that CASP10 is primarily localized within the Golgi apparatus and vesicles of tumor cells. Given that the Golgi apparatus plays a pivotal role in protein trafficking and modification, and vesicular transport is essential for cellular signaling, the subcellular localization of CASP10 may be functionally relevant. Previous reports have found the non-apoptotic activity of CASP9 in A549 and HCT116 tumor cells, which regulates cell survival by modulating lysosomal biogenesis and function.⁴⁶ Based on these findings, We hypothesize that CASP10 may play a role in regulating cell proliferation and survival.

Our analysis of 33 TCGA cancer datasets showed upregulated CASP10 mRNA in CHOL, GBM, HNSC, KIRC, LIHC, STAD, and THCA tissues compared to normal tissues. Immunohistochemical data from the HPA database further confirmed elevated CASP10 protein levels in LIHC, kidney cancer, and THCA tissues relative to their normal counterparts. Notably, CASP10 had potential as a diagnostic biomarker for BRCA, CESC, CHOL, COAD, ESAD, GBM, KICH, LIHC, LUSC, OSCC, READ, STAD, THCA, THYM, and UCEC. Additionally, high CASP10 expression was associated with reduced OS, DSS, and PFI in ACC, LGG, LIHC, and UVM. Our WB experiments demonstrated increased CASP10 protein levels in LIHC and STAD tumor tissues compared to normal tissues. These findings indicate a non-apoptotic role for CASP10 as an oncogene in cancer development and progression.

LIHC patients with AFP > 400 ng/ml showed higher CASP10 expression than those with AFP ⩽ 400 ng/ml. Logistic regression analysis revealed a strong correlation between CASP10 expression and AFP levels in LIHC. As a key serological biomarker, AFP is crucial for LIHC screening, surveillance, diagnosis, and prognosis.^47,48 The correlation between CASP10 expression and AFP levels suggest a significant role for CASP10 in cancer progression. Therefore, combining CASP10 and AFP assessment may improve the accuracy of LIHC screening, monitoring, diagnosis, and prognostic evaluation compared to AFP testing alone.

Recent research highlights the importance of TMB as a predictor of immune checkpoint inhibitor (ICI) efficacy, with TMB-High (TMB-H) tumors demonstrating greater responsiveness to ICI therapy.⁴⁹ These tumors typically present multiple neoantigens that, upon immune system recognition, can trigger a robust anti-tumor immune response.^50,51 Our study revealed a positive correlation between CASP10 expression and TMB in specific cancer types, leading us to hypothesize that patients with elevated CASP10 levels may experience improved survival outcomes following immunotherapy.

Elevated CASP10 expression correlates with a higher incidence of TP53 mutations in UCEC and LUAD. TP53, a crucial tumor suppressor gene, encodes the p53 protein, which is essential for regulating cell cycle progression, DNA repair, and apoptosis.^52,53 Mutations in TP53 disrupt these processes, resulting in genomic instability, metabolic reprogramming, and a supportive tumor microenvironment, thereby promoting cancer cell proliferation, invasion, metastasis, and chemotherapy resistance.⁵⁴ The association between increased CASP10 expression and TP53 mutations suggest that they may interact through common pathways, such as cell cycle regulation, to drive oncogenesis.

DNA methylation, a key epigenetic mechanism, regulates gene expression without altering the genomic sequence.^55,56 Recent research has highlighted the prevalence of aberrant DNA methylation patterns across various cancers, establishing it as a critical biomarker for malignancy progression.^57–59 Our study revealed reduced CASP10 promoter methylation levels in multiple cancer types, including BLCA, CESC, HNSC, KIRC, KIRP, LIHC, LUAD, PCPG, READ, THCA, and UCEC, compared to normal tissues, offering insights into CASP10’s role as an epigenetic modulator in cancer development.

Functional enrichment analysis of CASP10 across pan-cancer datasets revealed its association with several key signaling pathways, including NF-κB, TNF, cell cycle, and JAK-STAT. These findings are consistent with its known role in facilitating NF-κB pathway activation.^11,12 For the TNF pathway, studies show that TNF-α interaction with TNFR1 inhibits antigen-presenting dendritic cells infiltration and activation, thereby promoting tumor progression.⁶⁰ Additionally, TNF-α-driven lung inflammation can enhance adenocarcinoma progression via MIF-CD74 upregulation.⁶¹ Our results suggest that CASP10 may contribute to tumor progression through the TNF pathway. Dysregulation of the cell cycle, a characteristic of tumor cells, results in uncontrolled proliferation.^62,63 Our results indicate the potential role of CASP10 in tumor progression via cell cycle dysregulation. The JAK-STAT pathway, aberrantly activated in various cancers,⁶⁴ can promote tumorigenesis, metastasis, cancer stem cell transition, and chemoresistance by enhancing epithelial-mesenchymal transition (EMT).⁶⁵ Thus, CASP10’s association with the JAK-STAT pathway may represent another mechanism for tumor progression.

CAFs, characterized by their versatility, plasticity, and resilience, play a pivotal role in tumor progression through intricate interactions with tumor-infiltrating immune cells and other immune components within the tumor microenvironment.^66,67 Recent research has underscored their capacity to foster tumorigenesis by promoting angiogenesis, metastasis, drug resistance, extracellular matrix remodeling, and the establishment of an immunosuppressive microenvironment.^68,69 Our study revealed a positive correlation between CASP10 expression and CAFs infiltration across diverse tumor types, highlighting the significant role of CASP10 in tumor immunity.

The TIDE algorithm uses a unique gene expression signature to predict the effectiveness of immune checkpoint blockade (ICB) in cancer treatment. It evaluates two primary pathways of tumor immune evasion: the dysfunction of tumor-infiltrating Cytotoxic T Lymphocytes (CTL) and the exclusion of T cells by immunosuppressive factors.³⁷ A higher TIDE score correlates with a greater likelihood of immune resistance in tumors, resulting in diminished responsiveness to ICB therapies.^70,71 Notably, in LIHC and THCA, the CASP10 low-expression group exhibited lower TIDE scores, whereas the high-expression group had higher scores, suggesting that CASP10 may serve as a biomarker for ICB treatment outcomes.

Our analysis revealed an inverse correlation between CASP10 expression and the responsiveness to an array of anti-cancer therapeutics, including TAK-715, Z-LLNle-CHO, AICAR, bosutinib, neratinib, lapatinib, among others. CASP10 may be identified as a potential biomarker for therapeutic decision-making in cancer treatment. In LIHC and STAD, individuals with elevated CASP10 expression may exhibit greater sensitivity to the aforementioned anti-cancer agents, while those with low expression may require alternative treatments. Our findings provide valuable guidance, and future research will explore the role of CASP10 in refining patient stratification and treatment decisions for personalized medicine.

It is important to acknowledge the limitations of our study. Firstly, our research on CASP10 is limited to bioinformatics analysis and lacks in vivo and in vitro experiments to elucidate its role in cancer initiation and progression. Secondly, there is a deficiency in detailed mechanistic studies on CASP10 in cancer development. Therefore, future research should focus on addressing these two issues.

Conclusion

The advantages of this study are as follows: This is the first comprehensive pan-cancer analysis of CASP10, revealing significant expression differences across various cancer types, and establishing it as a potential biomarker for both diagnosis and prognosis. Secondly, the genomic and epigenetic insights deepen our understanding of CASP10’s role in cancer progression. Thirdly, CASP10 is closely related to critical signaling pathways such as NF-κB, TNF, cell cycle, and JAK-STAT, which play essential roles in cancer development and progression. Fourth, CASP10 interacts with various immune components within the TIME, suggesting its role in modulating the tumor immune landscape. Fifth, CASP10 can predict sensitivity to multiple anti-cancer drugs, indicating its potential clinical applications. Lastly, WB confirmed CASP10 overexpression in clinical cohorts of LIHC and STAD, validating our bioinformatics findings. In conclusion, our findings provide a foundation for exploring the non-apoptotic role of CASP10 in pan-cancer, offering a potential new perspective for cancer treatment.

Supplemental Material

sj-docx-2-iji-10.1177_03946320251327620 – Supplemental material for Comprehensive Pan-cancer Analysis Revealed CASP10 As a Promising Biomarker For Diverse Tumor Types

Supplemental material, sj-docx-2-iji-10.1177_03946320251327620 for Comprehensive Pan-cancer Analysis Revealed CASP10 As a Promising Biomarker For Diverse Tumor Types by Qian Wang, Yaping Jiang, Weijia Liao and Pengpeng Zhu in International Journal of Immunopathology and Pharmacology

Supplemental Material

sj-pptx-1-iji-10.1177_03946320251327620 – Supplemental material for Comprehensive Pan-cancer Analysis Revealed CASP10 As a Promising Biomarker For Diverse Tumor Types

Supplemental material, sj-pptx-1-iji-10.1177_03946320251327620 for Comprehensive Pan-cancer Analysis Revealed CASP10 As a Promising Biomarker For Diverse Tumor Types by Qian Wang, Yaping Jiang, Weijia Liao and Pengpeng Zhu in International Journal of Immunopathology and Pharmacology

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Pengpeng Zhu

Ethics approval

This study was approved by the Ethics Committees of the Affiliated Hospital of Guilin Medical University (Agreement Number: 2021WJWZC14).

Informed consent

Written informed consent was obtained from all subjects before the study.

Trial registration

Not applicable.

Supplemental Material

Supplemental material for this article is available online.

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