Research ArticleExperimental Studies

Comprehensive Analysis of KREMEN2 as an Immunotherapeutic and Prognostic Biomarker in Pan-Cancer

JIN LIU, BIN ZHU, JIADI CHEN and YINGPING CAO
Anticancer Research October 2023, 43 (10) 4491-4509; DOI: https://doi.org/10.21873/anticanres.16643

Abstract

Background/Aim: Kremen2 has been shown to play an important role in multiple cancers formation as a negative regulatory factor in the Wnt signaling pathway. Our study aimed to explore the potential value of KREMEN2 in pan-cancer and investigate the molecular mechanisms associated with tumor development, providing a basis for prognostic factors and new therapeutic targets for cancer. Materials and Methods: Raw RNA-seq data for 32 types of cancers were obtained from The Cancer Genome Atlas (TCGA), while Xena database provided overall survival (OS) and progression-free survival (PFI) data for TCGA patients. R language was used to identify the association between KREMEN2 and immune response, tumor mutational burden (TMB), and microsatellite instability (MSI). Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA) were conducted in pan-cancer. A Nomogram prediction model and weighted gene co-expression network analysis (WGCNA) were constructed in colorectal cancer (CRC). Results: KREMEN2 was found highly expressed in 17 types of tumor tissues compared to normal tissues. KREMEN2 was only correlated with some tumor pathological stages. KREMEN2 with high expression had poor prognosis in pan-cancer. KREMEN2 expression was significantly associated with immune infiltration, immune checkpoints, immune-related genes, commonly regulated tumor-related genes, TMB, and MSI. Moreover, GSVA and GSEA analyses suggested that KREMEN2 played a role in cell cycle in pan-cancer. KREMEN2 expression had a significant impact on the performance of Nomogram prediction model in CRC, and WGCNA analysis indicated that KREMEN2 performed special functions in CRC. Conclusion: The comprehensive pan-cancer analysis revealed that KREMEN2 is a promising tumor prognostic biomarker and a potential anti-tumor immunotherapeutic target in human tumors.

Key Words:

In most countries, cancer was the first or second leading cause of death before the age of 70 and there were approximately 19.3 million new cases and 10 million cancer-related deaths worldwide in 2020 (1). To alleviate the heavy social and family burden, early diagnosis and effective treatment of cancer are crucial. Therefore, finding specific biomarkers for tumor diagnosis and treatment is of great significance. As high-throughput screening technologies have exploded in recent years, screening of tumor biomarkers has been able to expand from genomics, transcriptomics, proteomics, and metabolomics standpoint. Using bioinformatics methods to re-explore the tumor transcriptome of large databases, further analyzing gene expression differences, and screening biomarkers, provides more powerful tools for assisting in diagnosis, predicting prognosis, and improving treatment of tumors (2). Kremen was first reported as a member of kringle-containing proteins, which was a transmembrane protein containing a single kringle in its extracellular region (3). Kremen2, one of the high-affinity Dkk1 receptors, is a component of a membrane complex with Dkk1 and LRP6 modulating canonical Wnt signalling in vertebrates (4). In addition, Kremen2 was found to modulate Dkk2 activity during Wnt/LRP6 signaling, functioning as a switch that turned Dkk2 from an activator into an inhibitor of Wnt/lRP6 signaling (5). As a negative regulatory factor in the Wnt signaling pathway, Kremen2 has been shown to play an important role in multiple biological processes such as embryonic development, tissue regeneration, and cancer formation (6-10).

Multiple studies have shown that knocking-down KREMEN2 had an inhibitory effect on tumor growth and migration in tumors such as gastric cancer and colon cancer (9, 10). However, there is currently a lack of pan-cancer research on KREMEN2, and little is known on its impact on the tumor immune microenvironment and potential mechanisms. The prognosis of solid tumors and the infiltration of immune cells in the tumor microenvironment (TME) are closely related. Exploring the relationship between KREMEN2 expression and immune infiltration is of great value for elucidating its role in tumor occurrence and development. Therefore, in order to more systematically and comprehensively understand KREMEN2, this study explored the expression and biological function of KREMEN2 from a pan-cancer perspective using public databases. Our study aimed to explore the potential value of KREMEN2 in pan-cancer and investigate the molecular mechanisms associated with tumor development, providing a basis for prognostic factors and new immunotherapeutic targets for cancer.

Materials and Methods

Data acquisition and differential expression analysis of KREMEN2. The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/), as the largest cancer gene information database, contains data including gene expression, copy number variations (CNVs), Single nucleotide polymorphisms (SNPs) (11). The raw RNA-seq data, namely mRNA expression data, for 32 types of pan-cancer were downloaded for subsequent analysis. Data of each tumor cell line obtained from Cancer Cell Line Encyclopedia (CCLE) database (https://portals.broadinstitute.org/ccle/) was also downloaded and analyzed for gene expression levels in these tumor tissues according to their tissue origins. In addition, the correlation between KREMEN2 expression and tumor stage was investigated. The 32 TCGA cancer types included adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma (CESC), cholangiocarcinoma (CHOL), Colon adenocarcinoma/Rectum adenocarcinoma Esophageal carcinoma (COADREAD), lymphoid neoplasm diffuse large B cell lymphoma (DLBC), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), brain lower grade glioma (LGG), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), acute myeloid leukemia (LAML), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), mesothelioma (MESO), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), sarcoma (SARC), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), testicular germ cell tumors (TGCT), thyroid carcinoma (THCA), thymoma (THYM), uterine corpus endometrial carcinoma (UCEC), uterine carcinosarcoma (UCS), and uveal melanoma (UVM).

Prognostic analysis of KREMEN2. The UCSC Xena Database (https://xena.ucsc.edu/) is an online platform designed to store, share, and analyze public cancer genomics data, which contains large-scale gene expression profiles, mutations, CNVs, and proteomic datasets sourced from various public data resources such as TCGA and CCLE (12). The overall survival (OS) and progression-free survival (PFI) of TCGA patients’ data were downloaded from Xena database to further study the relationship between KREMEN2 expression and patient prognosis. Kaplan-Meier (KM) analysis and univariate cox regression analysis were used for survival analysis for each cancer type. KM survival analysis was evaluated using “survival” and “survminer” packages and cox analysis was conducted using “survival” and “forestplot” packages.

Relationship between KREMEN2 expression and immunity infiltration. The CIBERSORT algorithm was used to analyze the RNA-seq data of 32 types of cancers in different subgroups, in order to infer the relative proportions of immune-infiltrating cells and perform correlation analysis of KREMEN2 expression and immune cell content. ESTIMATE is an algorithm that uses expression data to evaluate stromal and immune cells in pan-cancer, predicting the TME based on immune and stromal scores (13). The “ESTIMATE” package in R was used to investigate the relationship between KREMEN2 expression and stromal and immune cells in pan-cancer. TISIDB (http://cis.hku.hk/TISIDB) is a web portal for tumor and immune system interaction, which integrates multiple heterogeneous data types (14). The potential relationship between KREMEN2 expression and immune regulatory factors such as chemokines, immunosuppressants, immune stimulants, MHC molecules, and chemokine receptor were explored through the TISIDB website.

Relationship between KREMEN2 expression and TMB or MSI. Tumor mutational burden (TMB) is defined as the total amount of somatic gene coding errors, base substitutions, insertions or deletions detected per million bases (15). In this study, TMB was defined by calculating the mutation frequency and the number of variants/exon length for each tumor sample, dividing non-synonymous mutation sites by the total length of the protein-coding region. Microsatellite instability (MSI) means the occurrence of new microsatellite alleles at a microsatellite site in a tumor, compared with normal tissue, due to the insertion or deletion of a duplicate unit (16). The MSI value for each TCGA patient in this article was obtained from a previously published study (17). Correlation between KREMEN2 and TMB and MSI were conducted using Spearman correlation analysis.

Gene Set Variation Analysis. Gene Set Variation Analysis (GSVA) is a non-parametric and unsupervised method for evaluating the enrichment of gene sets in transcriptome data. GSVA transforms gene-level changes into pathway-level changes by scoring interested gene sets, thus determining the biological functions of samples. In this study, gene sets were downloaded from the Molecular Signatures Database (version 7.0), and the GSVA algorithm was used to score each gene set, assessing potential biological function changes among different samples.

Gene Set Enrichment Analysis. Gene Set Enrichment Analysis (GSEA) uses pre-defined gene sets to rank genes based on their differential expression levels in two classes of samples, and then test whether the pre-defined gene sets are enriched at the top or bottom of this ranking table. In this study, GSEA was performed using the “clusterprofiler” and “enrichplot” packages to explore the possible molecular mechanisms of prognostic differences among patients with different types of tumors by comparing differences in signaling pathways between high- and low-expression groups of genes.

Nomogram prediction model construction. Nomogram prediction model is a novel clinical evaluation tool widely used in disease diagnosis, disease risk prediction, and disease prognosis assessment. Based on regression analysis, the Nomogram prediction model integrates multiple independent risk factors, and displays the scores of each factor on a same plane with labeled line segments in certain proportions, which is intuitive and clear (18). It also facilitates clinical application due to the display of the score allocation for each predictor and has high practical value (18). In this study, a Nomogram model was constructed to better evaluate the prognosis of patients in CRC.

WGCNA analysis. By constructing a weighted gene co-expression network analysis (WGCNA), synergistically expressed gene modules were identified, and the association between gene networks and KREMEN2 was explored, as well as the core genes in the network. The WGCNA-R package was used to construct a co-expression network of all genes in the CRC dataset, and the top 5,000 genes with the highest variance were selected using this algorithm for further analysis.

Statistical analysis. All statistical analysis were conducted using R language (version 4.2.2). Single variable survival analysis was used to calculate hazard ratios (HRs) and 95% confidence intervals. KM analysis was used to study the survival rate of patients based on their high or low levels of gene expression. All statistical tests were two-sided, with a p-value less than 0.05 considered statistically significant.

Results

Differential expression of KREMEN2 in pan-cancer. The differential expression analysis of KREMEN2 was performed using TCGA database in 32 different types of cancers in human. Results showed that KREMEN2 was highly expressed in 17 types of tumors, including BLCA, BRCA, CESC, CHOL, CRC, ESCA, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, SARC, STAD, THCA, UCEC (Figure 1A). In most normal tissues, the expression level of KREMEN2 was relatively low compared to that of cancer tissues. The expression of KREMEN2 in different tumor cell lines in the CCLE expression spectrum was shown in the figure (Figure 1B). In addition, KREMEN2 was associated with the stage of multiple tumors, including BLCA, KIRC, KIRP, LUSC, TGCT, and THCA (Figure 1C).

Figure 1.
Figure 1.
Figure 1.

Differential expression of KREMEN2 in pan-cancer. (A) KREMEN2 expression in pan-cancer in TCGA. (B) KREMEN2 expression in tumor cell lines in CCLE. (C) Relationship between KREMEN2 expression and tumor stage for BLCA, KIRC, KIRP, LUSC, TGCT, THCA. ACC: Adrenocortical carcinoma; BLCA: bladder urothelial carcinoma; BRCA: breast invasive carcinoma; CESC: cervical squamous cell carcinoma; CHOL: cholangiocarcinoma; COADREAD: colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma; DLBC: lymphoid neoplasm diffuse large B cell lymphoma; ESCA: esophageal carcinoma; GBM: glioblastoma multiforme; LGG: brain lower grade glioma; HNSC: head and neck squamous cell carcinoma; KICH: kidney chromophobe; KIRC: kidney renal clear cell carcinoma; KIRP: kidney renal papillary cell carcinoma; LAML: acute myeloid leukemia; LIHC: liver hepatocellular carcinoma; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma; MESO: mesothelioma; OV: ovarian serous cystadenocarcinoma; PAAD: pancreatic adenocarcinoma; PCPG: pheochromocytoma and paraganglioma; PRAD: prostate adenocarcinoma; SARC: sarcoma; SKCM: skin cutaneous melanoma; STAD: stomach adenocarcinoma; TGCT: testicular germ cell tumors; THCA: thyroid carcinoma; THYM: thymoma; UCEC: uterine corpus endometrial carcinoma; UCS: uterine carcinosarcoma; UVM: uveal melanoma.

Prognostic value of KREMEN2 in pan-cancer. The relationship between KREMEN2 expression and the prognosis of cancer patients was evaluated. Survival indicators included OS and PFI. The results showed that KREMEN2 expression was closely related to the OS of seven types of cancers, including CRC, KICH, KIRC, KIRP, LAML, LUAD, and UVM (Figure 2A). KM-plot survival analysis results suggested that KREMEN2 was associated with poor OS in four types of cancers, including CRC, KIRC, KIRP, and LUAD (Figure 2B). KREMEN2 expression was closely related to the PFI of six types of cancers, including CRC, KIRC, KIRP, OV, PCPG, and UVM (Figure 2C). KM-plot survival analysis results suggested that KREMEN2 was associated with poor PFI in eight types of cancers, including BLCA, CRC, KIRC, KIRP, LUAD, PCPG, SARC, and SKCM (Figure 2D).

Figure 2.
Figure 2.
Figure 2.
Figure 2.

Survival analysis of KREMEN2 in pan-cancer. (A) Univariate cox regression analysis of KREMEN2 associated with OS in pan-cancer. (B) KM-plot survival analysis of KREMEN2 associated with OS for CRC, KIRC, KIRP, and LUAD. (C) Univariate cox regression analysis of KREMEN2 associated with PFI in pan-cancer. (D) KM-plot survival analysis of KREMEN2 associated with PFI for BLCA, CRC, KIRC, KIRP, LUAD, PCPG, SARC, and SKCM. ACC: Adrenocortical carcinoma; BLCA: bladder urothelial carcinoma; BRCA: breast invasive carcinoma; CESC: cervical squamous cell carcinoma; CHOL: cholangiocarcinoma; COADREAD/CRC: colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma; DLBC: lymphoid neoplasm diffuse large B cell lymphoma; ESCA: esophageal carcinoma; GBM: glioblastoma multiforme; LGG: brain lower grade glioma; HNSC: head and neck squamous cell carcinoma; KICH: kidney chromophobe; KIRC: kidney renal clear cell carcinoma; KIRP: kidney renal papillary cell carcinoma; LAML: acute myeloid leukemia; LIHC: liver hepatocellular carcinoma; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma; MESO: mesothelioma; OV: ovarian serous cystadenocarcinoma; PAAD: pancreatic adenocarcinoma; PCPG: pheochromocytoma and paraganglioma; PRAD: prostate adenocarcinoma; SARC: sarcoma; SKCM: skin cutaneous melanoma; STAD: stomach adenocarcinoma; TGCT: testicular germ cell tumors; THCA: thyroid carcinoma; THYM: thymoma; UCEC: uterine corpus endometrial carcinoma; UCS: uterine carcinosarcoma; UVM: uveal melanoma.

Relationship between KREMEN2 expression and immune infiltration in pan-cancer. TME is mainly composed of tumor-associated fibroblasts, immune cells, extracellular matrix, various growth factors, inflammatory factors, special physicochemical characteristics, and cancer cells themselves, which significantly affect the diagnosis, survival outcomes, and clinical treatment sensitivity of tumors. The results of our study showed that KREMEN2 expression was closely related to immune infiltration. It was significantly correlated with Follicular helper T cell in 10 types of cancers, CD8 T cells in 11 types of cancers, and M0 macrophages in 10 types of cancers (Figure 3A). The TME of pan-cancer was further analyzed, and the results showed that the scores of TMEscoreB, TMEscoreA, TMEscore, Pan_F_TBRs, Nucleotide_excision_repair, Mismatch_Repair, Immune_Checkpoint, EMT3, EMT2, EMT1, DNA_replication, DNA_damage_response, CD_8_T_effector, Base_excision_repair, Antigen_processing_machinery were correlated with different types of tumors (Figure 3B). In addition, ESTIMATE algorithm was used to analyze the correlation of KREMEN2 with stromal score and immune score in pan-cancer. The results showed that KREMEN2 had negative correlation with stromal score and immune score in LAML, LGG, LUAD, OV, PAAD, SARC, and UVM, but had positive correlation with stromal score and immune score in KICH and LUSC. There was a positive or negative correlation between KREMEN2 and some cancers with stromal score or immune score. KREMEN2 had no correlation with stromal score and immune score in ACC, CHOL, ESCA, GBM, LIHC, MESO, PCPG, PRAD, STAD, UCEC, and UCS (Figure 3C).

Figure 3.
Figure 3.
Figure 3.

The relationship between KREMEN2 expression and immune infiltration. (A) Correlation of KREMEN2 with immune cells in pan-cancer based on CIBERSORT. (B) Correlation of KREMEN2 with TME analysis in pan-cancer. (C) Correlation of KREMEN2 with the stromal score, immune score in pan-cancer based on ESTIMATE. ACC: Adrenocortical carcinoma; BLCA: bladder urothelial carcinoma; BRCA: breast invasive carcinoma; CESC: cervical squamous cell carcinoma; CHOL: cholangiocarcinoma; COADREAD: colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma; DLBC: lymphoid neoplasm diffuse large B cell lymphoma; ESCA: esophageal carcinoma; GBM: glioblastoma multiforme; LGG: brain lower grade glioma; HNSC: head and neck squamous cell carcinoma; KICH: kidney chromophobe; KIRC: kidney renal clear cell carcinoma; KIRP: kidney renal papillary cell carcinoma; LAML: acute myeloid leukemia; LIHC: liver hepatocellular carcinoma; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma; MESO: mesothelioma; OV: ovarian serous cystadenocarcinoma; PAAD: pancreatic adenocarcinoma; PCPG: pheochromocytoma and paraganglioma; PRAD: prostate adenocarcinoma; SARC: sarcoma; SKCM: skin cutaneous melanoma; STAD: stomach adenocarcinoma; TGCT: testicular germ cell tumors; THCA: thyroid carcinoma; THYM: thymoma; UCEC: uterine corpus endometrial carcinoma; UCS: uterine carcinosarcoma; UVM: uveal melanoma.

Relationship between KREMEN2 expression and key regulatory genes. This study further conducted a gene co-expression analysis to explore the relationship between KREMEN2 expression and immune-related genes in pan-cancer. The analyzed genes included MHC, immunostimulator, immunoinhibitor, chemokines, and chemokine receptors. The results showed that most of the immune-related genes were significantly correlated with KREMEN2 (Figure 4A). In addition, KREMEN2 was significantly associated with commonly regulated tumor-related genes such as TGF BETA SIGNALING, TNFA SIGNALING, hypoxia, pyroptosis, DNA repair, autophagy genes, iron death-related genes, and immune checkpoint (Figure 4B).

Figure 4.
Figure 4.
Figure 4.

Relationship between KREMEN2 expression and key regulatory genes. (A) Correlation between KREMEN2 expression and immune-related genes including MHC, immunostimulator, immunoinhibitor, chemokines, and chemokine receptors. (B) Correlation between KREMEN2 expression and other tumor-related genes including TGF BETA SIGNALING, TNFA SIGNALING, hypoxia, pyroptosis, DNA repair, autophagy genes, iron death-related genes, and immune checkpoint. ACC: Adrenocortical carcinoma; BLCA: bladder urothelial carcinoma; BRCA: breast invasive carcinoma; CESC: cervical squamous cell carcinoma; CHOL: cholangiocarcinoma; COADREAD: colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma; DLBC: lymphoid neoplasm diffuse large B cell lymphoma; ESCA: esophageal carcinoma; GBM: glioblastoma multiforme; LGG: brain lower grade glioma; HNSC: head and neck squamous cell carcinoma; KICH: kidney chromophobe; KIRC: kidney renal clear cell carcinoma; KIRP: kidney renal papillary cell carcinoma; LAML: acute myeloid leukemia; LIHC: liver hepatocellular carcinoma; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma; MESO: mesothelioma; OV: ovarian serous cystadenocarcinoma; PAAD: pancreatic adenocarcinoma; PCPG: pheochromocytoma and paraganglioma; PRAD: prostate adenocarcinoma; SARC: sarcoma; SKCM: skin cutaneous melanoma; STAD: stomach adenocarcinoma; TGCT: testicular germ cell tumors; THCA: thyroid carcinoma; THYM: thymoma; UCEC: uterine corpus endometrial carcinoma; UCS: uterine carcinosarcoma; UVM: uveal melanoma.

Relationship between KREMEN2 expression and TMB and MSI. TMB and MSI are emerging biomarkers associated with immune therapy response. This study investigated the relationship between KREMEN2 expression and TMB and MSI. The results showed that KREMEN2 expression was significantly correlated with TMB in tumors, including KICH, THYM, PCPG, LUAD, PRAD, THCA, CRC (Figure 5A). In MSI, KREMEN2 expression was significantly related to KICH, TGCT, LUSC, STAD, GBM, THCA, BLCA, KIRP, OV, SKCM, HNSC, LGG (Figure 5B).

Figure 5.
Figure 5.

Relationship between KREMEN2 expression and TMB and MSI. (A) Correlation between KREMEN2 expression and TMB in pan-cancer. (B) Correlation between KREMEN2 expression and MSI in pan-cancer. TMB: Tumor mutational burden; MSI: microsatellite instability; ACC: adrenocortical carcinoma; BLCA: bladder urothelial carcinoma; BRCA: breast invasive carcinoma; CESC: cervical squamous cell carcinoma; CHOL: cholangiocarcinoma; COADREAD: colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma; DLBC: lymphoid neoplasm diffuse large B cell lymphoma; ESCA: esophageal carcinoma; GBM: glioblastoma multiforme; LGG: brain lower grade glioma; HNSC: head and neck squamous cell carcinoma; KICH: kidney chromophobe; KIRC: kidney renal clear cell carcinoma; KIRP: kidney renal papillary cell carcinoma; LAML: acute myeloid leukemia; LIHC: liver hepatocellular carcinoma; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma; MESO: mesothelioma; OV: ovarian serous cystadenocarcinoma; PAAD: pancreatic adenocarcinoma; PCPG: pheochromocytoma and paraganglioma; PRAD: prostate adenocarcinoma; SARC: sarcoma; SKCM: skin cutaneous melanoma; STAD: stomach adenocarcinoma; TGCT: testicular germ cell tumors; THCA: thyroid carcinoma; THYM: thymoma; UCEC: uterine corpus endometrial carcinoma; UCS: uterine carcinosarcoma; UVM: uveal melanoma.

Association of KREMEN2 expression with GSVA and GSEA in pan-cancer. To investigate the molecular mechanisms of KREMEN2 in pan-cancer, our study first scored all tumors using GSVA, and then divided the samples into high and low expression groups based on the median KREMEN2 expression in each tumor for comparison. The results showed that in different tumors, high or low expression of KREMEN2 were enriched in different signal pathways. Here the GSVA analysis results of KREMEN2 were presented in BLCA, BRCA, CESC, CRC, KICH, LUAD, LUSC, PAAD, STAD (Figure 6A). The results showed that KREMEN2 expression was mainly positively correlated with E2F_TARGETS, P53_PATHWAY, DNA_REPAIR, REACTIVE_OXYGEN_SPECIES_PATHWAY, G2M_CHECKPOINT, MYC_TARGETS, and APOPTOSIS in different types of tumors. GSEA analysis was also performed on the aforementioned 9 types of tumors (Figure 6B). In various tumors, KREMEN2 expression was mainly positively associated with CELL_CYCLE, MAPK_SIGNALING_PATHWAY, FOCAL_ADHESION, PATHWAYS_IN_CANCER, CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION, SPLICEOSOME in GSEA.

Figure 6.
Figure 6.
Figure 6.

Association of KREMEN2 expression with GSVA and GSEA in BLCA, BRCA, CESC, CRC, KICH, LUAD, LUSC, PAAD, STAD. (A) GSVA analysis in BLCA, BRCA, CESC, CRC, KICH, LUAD, LUSC, PAAD, STAD. (B) GSEA analysis in BLCA, BRCA, CESC, CRC, KICH, LUAD, LUSC, PAAD, STAD. GSVA: Gene Set Variation Analysis; GSEA: Gene Set Enrichment Analysis; BLCA: bladder urothelial carcinoma; BRCA: breast invasive carcinoma; CESC: cervical squamous cell carcinoma; COADREAD/CRC: colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma; KICH: kidney chromophobe; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma; PAAD: pancreatic adenocarcinoma; STAD: stomach adenocarcinoma.

Risk and independent prognostic analysis of KREMEN2. A Nomogram predictive model was constructed based on the expression level of KREMEN2 and clinical symptoms. The results of the regression analysis were displayed in the form of a column chart, indicating that the expression level of KREMEN2 had a significant contribution to the predictive efficacy of the model in CRC samples (Figure 7A). The results of the correction curve showed that the Nomogram model had good predictive accuracy, as the predicted OS was consistent with the actual observed OS (Figure 7B).

Figure 7.
Figure 7.

A Nomogram prediction model of KREMEN2 in CRC. (A) Nomogram predictive model based on the expression level of KREMEN2 and clinical symptoms. (B) Correction curve of Nomogram predictive model. OS: Overall survival.

WGCNA analysis. A WGCNA network was constructed based on CRC data to explore the KREMEN2-related regulatory network in CRC. The soft-thresholding power β was determined by the function “sft$powerEstimate”, and gene modules were detected based on the tom matrix. In this analysis, a total of 12 gene modules were detected, including black (334), blue (615), brown (538), green (471), greenyellow (240), grey (87), magenta (325), pink (326), purple (272), red (359), turquoise (943), and yellow (490). Further analysis between the modules and KREMEN2 revealed that the turquoise module had the most significant correlation with KREMEN2 (cor=0.29, p=5×10−15) (Figure 8A). Pathway analysis was conducted using genes from the turquoise module, and GO analysis results showed that the genes were mainly enriched in pathways such as generation of precursor metabolites and energy, cell-substrate junction, structural constituent of ribosome, etc. (Figure 8B). KEGG results showed that the genes were mainly enriched in pathways such as Carbon metabolism, Cell cycle, HIF-1 signaling pathway, etc. (Figure 8C).

Figure 8.
Figure 8.

KREMEN2-related regulatory network in CRC. (A) The moduleen2 and clinical symptoms. (B) Correction curve of Nomogram prysis using genes from the turquoise module. (C) KEGG analysis using genes from the turquoise module.

Discussion

Tumor biomarkers can serve as tools for early detection, diagnosis, prognosis, targeted therapy, treatment monitoring, and response prediction in certain cases. The ability of biomarkers to assist in making different clinical decisions makes them an indispensable tool in current cancer treatment. The results of tumor markers are currently used in the clinic to predict the prognosis of each patient and assist in achieving personalized combination therapy (19). Analyzing KREMEN2 in this study was based on the research objectives mentioned above. KREMEN2 has been reported to be associated with the occurrence and development of tumors in previous studies, which mainly focused on a small number of tumors and small sample experiments (9, 10). The impact of KREMEN2 in tumors and whether it can serve as a diagnostic or prognostic biomarker remains to be determined.

In this study, it was found that in 17 types of tumors, the expression of KREMEN2 in tumor tissue samples was significantly higher than that in normal tissue samples by analyzing the TCGA database. This result was consistent with previous small sample experiments that detected increased KREMEN2 expression in gastric cancer and colon cancer (9, 10). High expression of KREMEN2 was also associated with a poor prognosis. Previous small sample study has shown that when KREMEN2 was highly expressed, the prognosis of colon cancer patients was poor, which was consistent with the results obtained from our study analysis (10). In addition, our study results also indicated that KREMEN2 was related to the stage of multiple tumors. Therefore, KREMEN2 may be one of the biomarkers for the diagnosis and prognosis of tumors.

As research on tumors deepens, more and more researchers are focusing on the relationship and role between cancer cells and immune cells in tumors. Tumor cells can use immune evasion mechanisms to evade the killing of immune cells, thus the importance of tumor-infiltrating immune cells (TIICs) is increasingly valued (20). Currently, TIICs have been proven to be closely related to prognosis of various tumors such as breast cancer, lung cancer, and CRC (21-23). Recent studies have found that the quantity and distribution of TIICS can affect the treatment response of cancer patients (24). Therefore, a comprehensive analysis of immune cells in TME can analyze the relationship between KREMEN2 and TME at the cellular level, and also evaluate its potential as a target. The results of this study found that the expression of KREMEN2 was closely related to immune infiltration. In general, the immune process is a process caused by the interaction and connection of various immune cells. The expression of KREMEN2 was positively correlated with the degree of infiltration of most immune cells, indicating that KREMEN2 may regulate the degree of immune cell infiltration through certain mechanisms and thus alter the TME.

In addition to evaluating the association between KREMEN2 and TME at the level of immune cells, it can also be evaluated through an immune score, which is a new method of tumor classification based on immune structure. The immune score is a systematic and quantifiable indicator for evaluating the immune situation in TME. In TME, in addition to immune cells, which are non-tumor cells, stromal cells also constitute a significant proportion of cells. The role of stromal cells in tumors has gradually been discovered, and they can promote tumor formation and progression. Therefore, stromal scoring methods have also been developed to predict tumor prognosis and efficacy (25). Through tumor immune scoring and stromal scoring, it is more comprehensive to evaluate the relationship between KREMEN2 and the tumor immune microenvironment from another perspective in different types of tumors. In this study, it was discovered that KREMEN2 had a negative correlation with stromal score and immune score in LAML, LGG, LUAD, OV, PAAD, SARC, and UVM and there was a positive correlation between KREMEN2 and stromal score as well as immune score in KICH and LUSC. These results all indicated that the regulatory role of KREMEN2 varied between different tumors.

There was compatibility between KREMEN2 expression and TMB and MSI. Previous studies have shown that TMB and MSI were biomarkers for prognosis of various cancers and predictive indicators for the efficacy of many tumor immunotherapies (26, 27). This study showed that KREMEN2 expression was related to TMB in seven types of cancers, with most showing a positive correlation. This suggested that as KREMEN2 expression increased, the degree of tumor mutations also increased. KREMEN2 expression was associated with MSI in 12 types of tumors. These results suggested that KREMEN2 may affect tumor occurrence by participating in the process of gene mutation. KREMEN2 expression was positively related to TMB and MSI in KICH and THCA. Therefore, the potential survival advantages following immunotherapy may be greater in cases of KICH and THCA with high expression of KREMEN2. Consequently, KREMEN2 presents itself as a promising target for novel anti-cancer immunotherapeutic drugs. All of the above results indicated that KREMEN2 may be an important biomarker for the treatment and prognosis of various types of tumors. Dysregulation of KREMEN2 may affect the immunotherapy efficacy of patients, which requires further research to confirm.

The results of GSEA and GSVA further revealed that high expression of KREMEN2 was related to the occurrence and development of tumors. The GSVA results showed that in various tumors, KREMEN2 was mainly associated with REACTIVE_OXYGEN_SPECIES_PATHWAY, E2F_TARGETS, P53_PATHWAY, DNA_REPAIR, and G2M_CHECKPOINT. GSEA analysis results showed that in multiple cancers, high expression of KREMEN2 was related to cell adhesion, oxidative phosphorylation, and immune response processes. Reactive oxygen species (ROS) are products of oxygen consumption or cellular metabolism that can regulate the development and survival of cancer by affecting the behavior of cancer cells and tumor stromal components (28). It is believed that when lipid peroxidation occurs within cells, high concentrations of ROS can damage cell membranes or organelle membranes and may induce forms of cell death such as apoptosis, autophagy, and ferroptosis (29). Studies have found that ROS levels increased significantly in various cancer detections, and the difference can distinguish between normal and cancerous tissues, suggesting that ROS detection could become a diagnostic tool for early-stage tumors (30). Additionally, it is believed that tumor cells usually clear excessive ROS to avoid high concentrations of ROS inhibiting the growth and proliferation of cancer cells (31). Therefore, regulating the concentration of ROS inside cells could become a potential and effective method for cancer treatment. As one of 65 transcription factor families in the human genome, E2F family transcription factors (E2Fs) have 11 members, which play a key role in cell proliferation and regulation of cell cycle process (32-34). Different E2Fs play different roles in transcriptional regulation. E2F1 and E2F2 often act as transcription activators, while E2F4 and E2F5 act as transcription repressors, and E2F6-E2F8 are used as transcriptional inhibitors (35). E2F1 played an important role in the occurrence and development of lung cancer and other malignant tumors (36). Both mechanisms and pathways analyzed by GSVA and GSEA indicated that KREMEN2 dominated several important cancer-related pathways.

In conclusion, our study successfully demonstrated the high expression of KREMEN2 in 17 types of cancers and its association with survival, immune infiltration, as well as recent research hotspots such as pyroptosis, DNA repair, autophagy, ferroptosis, and drug sensitivity. Additionally, it was found that KREMEN2 had more functions in CRC, and its role in CRC needs further validation. The results of comprehensive pan-cancer analysis revealed that KREMEN2 is a promising tumor prognostic biomarker and a potential anti-tumor immunotherapeutic target in human tumors. However, our study had significant limitations as the impact of KREMEN2 has been proven in various tumors based on public databases, lacking evidence from laboratory data. Therefore, our results require further validation with more clinical and experimental data, as well as more relevant genes and pathways.

Acknowledgements

This work was supported by Fujian Province Young and Middle-aged Teachers’ Education Research Project (JAT220089).

Footnotes

  • Authors’ Contributions

    JL: Contributed to the experimental design, collected, and analyzed the data, and wrote the initial draft of the manuscript. BZ: performed statistical analysis and revised the manuscript critically for intellectual content. YPC and JDC: provided substantial input in conceptualizing the study, supervised the research process, and provided critical feedback on the manuscript. All Authors actively participated in discussions, reviewed, and approved the final version of the paper.

  • Conflicts of Interest

    The Authors declare no conflicts of interest in relation to this study.

  • Received July 5, 2023.
  • Revision received August 21, 2023.
  • Accepted August 29, 2023.

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Comprehensive Analysis of KREMEN2 as an Immunotherapeutic and Prognostic Biomarker in Pan-Cancer
JIN LIU, BIN ZHU, JIADI CHEN, YINGPING CAO
Anticancer Research Oct 2023, 43 (10) 4491-4509; DOI: 10.21873/anticanres.16643
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