High expression of cuproptosis-related gene DLD in relation to good prognosis and immune cells infiltration in colon cancer

doi:10.21203/rs.3.rs-3133880/v1

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High expression of cuproptosis-related gene DLD in relation to good prognosis and immune cells infiltration in colon cancer

https://doi.org/10.21203/rs.3.rs-3133880/v1

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Background

Colon cancer (CC) is one of the diseases that pose a significant risk to human health. Cuproptosis is a newly discovered mechanism that regulates cell death, and DLD is an associated positive regulator of copper death. However, the role of DLD in the pathogenesis of colon cancer remains to be investigated.

Materials and Methods We analyzed the expression of DLD in colon cancer from the University of Alabama at Birmingham Cancer data analysis Portal (UALCAN) database and the Human Protein Atlas (HPA) database. Correlations between DLD expression and clinicopathological parameters were analyzed in UALCAN database. Kaplan-Meier (K-M) survival curves were used to elucidate the relationship between DLD expression and survival time of colon cancer patients, which was validated in the GSE:39582 dataset. Then, gene set enrichment analysis (GSEA) was performed to explore the biological function of DLD. The relationship between DLD expression and cancer immune infiltration was analyzed using the Tumor Immunization Estimation Resource database (TIMER), single sample gene set enrichment analysis (ssGSEA), and other immune infiltration methods.

Results We found that DLD was downregulated in colon cancer samples and that increased DLD expression was associated with better overall survival. Functional annotation of 302 DLD co-expressed genes and 4731 DLD differentially expressed genes and including GO, KEGG and GSEA enrichment analysis.DLD is essentially involved in Metabolic pathways, Oxidative phosphorylation, cancer pathway and WNT signaling pathway. The most common mutated genes in colon cancer were different in high and low DLD groups. Tumor immune infiltration analysis revealed that DLD may recruit infiltrating immune cells and modulate immunity in colon cancer, thus affecting prognosis.

Conclusion DLD plays a role in inducing cuproptosis and regulating immunity in colon cancer, which can be considered as a new biomarker for colon cancer.

DLD

Cuproptosis

Prognosis

Immune infiltrate

Colon cancer (CC) is one of the most common human malignancies worldwide and the fifth leading cause of cancer-related deaths¹. Many factors are involved in the development and progression of colon cancer, including the activation of oncogenes and inactivation of tumor suppressor genes. Despite advances in treatment in the last decades, tumor recurrence is common ². And most patients with colon cancer have a limited response to ICI therapy³. Emerging biomarkers such as tumor mutational load (TMB), inflammatory tumor microenvironment (TME) and microsatellite instability (MSI) have been identified to predict the therapeutic benefit of colon cancer⁴. Therefore, the discovery of additional diagnostic and prognostic biomarkers and therapeutic targets may lead to better treatment strategies for colon cancer patients and improve long-term outcomes.

Cuproptosis is a newly discovered mechanism that differs from all known regulatory mechanisms of cell death, including apoptosis, ferroptosis, pyroptosis, and necrotizing apoptosis. The main mechanism of cuproptosis is the targeting of copper ions for lipid acylation and aggregation of tricarboxylic acid cycle proteins, followed by the loss of iron-sulfur cluster proteins, leading to proteotoxic stress and ultimately cell death ⁵.Dihydrolipoamide dehydrogenase (DLD) gene encodes a member of the class I pyridine nucleotide-disulfide oxidoreductase family and is a key regulator of copper ionophore-induced cell death, as well as an upstream regulator of protein lipid acylation⁶; DLD has been studied in pan-cancer, showing that high expression of DLD is associated with a better clinical prognosis in colon adenocarcinoma(COAD) ^7,8. However, the impact of the copper death-associated gene DLD on clinicopathological factors and immune cell infiltration in CC remains largely unknown.

This study highlights the expression, prognostic and immunological significance of DLD in CC. Data from The Cancer Genome Atlas (TCGA) and Gene Expression Database (GEO) were used to assess the role of DLD in CC. Bioinformatics analysis confirmed the expression profile and prognostic value of DLD in CC. The association of DLD with somatic mutations and immune cell infiltration was assessed. GO/KEGG enrichment analysis was used to analyze the potential mechanisms between the high and low DLD groups. Finally, we determined that high expression of DLD improved the survival of CC patients, which may be related to the regulation of DLD in different functional states of tumor cells and immune microenvironment.

2.1 Acquisition of data

In this study, we downloaded mRNA sequencing data (FPKM format) from the TCGA database (https://portal.gdc.cancer.gov/, as of December 28, 2022) for normal and primary tumor samples, including 446 COAD samples and 93 normal colon samples. We also downloaded the GSE39582 dataset from Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/) for validation⁹. Clinical data of COAD patients from TCGA database and GEO database were also obtained. Patients were classified into high and low groups according to the cut-off point of DLD mRNA expression. A total of 1011 COAD samples (TCGA: 446; GSE39582: 562) and 112 normal tissues (TCGA: 93; GSE39582: 19) were included in this study.

2.2 DLD expression profiles in CC

DLD mRNA expression in CC was analyzed using The University of Alabama at Birmingham Cancer data analysis Portal (UALCAN, http://ualcan.path.uab.edu), a comprehensive, user-friendly, interactive web-based resource for analyzing cancer OMICS data ^10,11. Immunohistochemistry of DLD proteins in human normal colon tissue and colon cancer tissue was obtained using the Human Protein Atlas (HPA, https://www.proteinatlas.org/) ^12,13. In addition, the diagnostic power of DLD for CC was calculated using ROC curves.

2.3 Relationship between DLD expression and CC survival prognosis

Overall survival (OS) data were downloaded from the TCGA database COAD project and the GEO database: GSE39582. Kaplan-Meier (KM) survival curve analysis was implemented by the R packages "Survival" (version 3.2–10) and "Survminer" (visualization, version 0.4.9). Cox regression or Logrank test was used to analyze the relationship between DLD expression and survival of CC patients and the effect of DLD expression on the prognosis of different clinical variables.

2.4 Enrichment analysis of DLD gene co-expression network in CC

First, we identified co-expressed genes associated with DLD expression in the TCGA-COAD dataset and GSE:39586 in R software. We used Pearson's correlation coefficient to test the statistical correlation and the ggplot2 package of R software to draw heat maps for presentation. We performed Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the DAVID website (https://david.ncifcrf.gov/), and enriched gene words with FDR (false positive rate) q-values < 0.05 were considered statistically significant, and the results were displayed as bar graphs in the R software ¹⁴.

2.5 Gene set enrichment analysis

To explore the potential regulatory mechanisms of DLD, we divided the samples in the TCGA COAD dataset into two groups based on the cut-off points of DLD expression levels. The R package "DESeq2" was run to filter differentially expressed genes (DEGs) between CC samples with high and low DLD expression¹⁵. FC > 1.2 or < 0.83 and p < 0.05 were defined as filtering criteria. the results of DEGs were displayed as volcano plots using the R package "ggplot2". the gene set enrichment analysis (GSEA) analysis was performed using the OmicShare tools, a free online platform for data analysis (https://www.omicshare.com/tools). FDR (q-value) < 0.05 was considered statistically significant.

2.6 Tumor Mutation Load Analysis

Download somatic mutation and somatic copy number variation (CNA) data for COAD from the TCGA database. Mutation details of genes with top 30 high mutation frequencies between high and low DLD groups are displayed in waterfall plots using the "maftools" R package.

2.7 Analysis of tumor immune infiltration

The Tumor Immunity Evaluation Resource (TIMER, http://timer.cistrome.org/) is a comprehensive resource for the systematic analysis of immune infiltration in different cancer types ¹⁶. The correlation between DLD expression in CC and six types of immune cells (CD8 T cells, B cells, NK cells, CD4 T cells, macrophages, and neutrophils) was assessed using TIMER. A single sample gene set enrichment analysis (ssGSEA) was applied to compare 28 tumor-infiltrating immune cells in CC between high and low DLD groups.

2.8 Single-cell sequencing data analysis

CancerSEA (http://biocc.hrbmu.edu.cn/CancerSEA/home.jsp) was used to analyze the effect of DLD expression levels on the different functional states of cancer cells at the single-cell level ¹⁷. We downloaded the colorectal cancer expression and functional status files from the Cancer SEA database (database ID: EXP0051) and the corresponding colon cancer single-cell sequencing files from the GEO database (GSE81861) and visualized the correlation between DLD and functional status of colon cancer using the R package "ggplot2".

2.9 Statistical analysis

Statistical analyses for this study were performed using R language (version 4.3.0), which is the language and environment used for statistical calculations (R Foundation, Vienna, Austria, https://www.R -project.org/) and GraphPad Prism software (version 8.0.2). The ROC curve of the pROC package was applied to assess the potential diagnostic value of DLD expression and the area under the curve (AUC) was obtained ¹⁸. AUC > 0.7 showed good diagnostic accuracy. The chi-square (x^2) test was used to assess the correlation between DLD expression and the clinicopathological characteristics of the patients. Significance of differences between groups was determined by Student's t-test (unpaired, two-tailed) and one-way analysis of variance (ANOVA). The "Survminer" and "survivor" R packages were used in R language by Kaplan- Meier analysis and the log-rank test were used to determine the cut-off point for the difference in overall survival between the high and low DLD groups. p < 0.05 was considered statistically significant.

3.1 Reduced expression of DLD in CC.

We analyzed the expression level of DLD in CC from TCGA database, UALCAN database, and GSE39582. The results showed that FDX1 expression was low in CC (Fig. 1A-C). immunohistochemical results from the HPA database showed that DLD was expressed in the mucosal epithelium, lamina propria and submucosa of normal colon tissues (Fig. 1D). However, compared to normal colon tissue, there is no obvious expression in colon cancer tissue (Fig. 1E). More importantly, the ROC curves showed that DLD expression possessed the value of identifying CC and normal colon tissues in both cohorts (Fig. 1F-G).

3.2 Relationship between DLD expression and clinical characteristics of CC patients

Patients with different levels of DLD expression showed different patterns of clinical and pathological features. In the TCGA dataset and the GSE39582 dataset, DLD expression levels, different clinical stages (T), lymph node invasion (N), and different pathological stages (Stage) showed an asymmetric distribution (Fig. 2A, B). A comparative analysis was performed for different groups of these samples. In the TCGA database, the expression of CD161 decreased with increasing lymph node invasion (N) and pathological stage (Stage) level in the samples (P < 0.05, Fig. 2D, E), with no significant difference in the subgroup of patients with different clinical stages (T) (Fig. 2C). The same trend was observed for DLD expression in the GSE39582 dataset, although this difference was not statistically significant (Fig. 2G, H); however, in the subgroup of patients with different clinical stages (T), DLD expression decreased with higher clinical stages (T) (P < 0.05, Fig. 2F). These results suggest that DLD is lacking in CC with higher malignancy.

3.3 High DLD expression implies a better prognosis for CC patients.

To investigate the prognostic value of DLD in both cohorts, patients were divided into high and low DLD expression groups according to the optimal cut-off points calculated by the "survival" and "survminer" software packages. We then used Kaplan-Meier analysis to assess the prognostic significance of DLD expression in the TCGA-COAD database and the GSE39582 dataset. Kaplan-Meier survival curves were generated based on the cut-off points for DLD expression and showed that CC patients expressing high levels of DLD showed better OS rates (P < 0.05, Fig. 3A,3B).

3.4 Enrichment analysis of DLD co-expressed genes and DEGs in CC

To explore the biological functions associated with DLD, the 302 genes most associated with DLD were screened by Pearson correlation analysis (R > 0.5, P < 0.05) in the TCGA database. GO and KEGG analyses were performed on DLD co-expressed genes via the R package and the DAVID website. Fifty genes were screened in the GSE:39582 dataset using the same screening method. The biological processes most associated with DLD in the TCGA database include protein transport, mitochondrial ATP synthesis coupled proton transport and mitochondrial transport (Figure A). In addition, the most relevant cellular component for DLD is mitochondrion (Fig. 3C). Molecular functions include GTP binding, GTPase activity and protein serine/threonine phosphatase activity (Fig. 4E). the most relevant signaling pathways for DLD are Metabolic pathways and Oxidative phosphorylation (Fig. 4G). the biological processes, cellular components, molecular functions and signaling pathways associated with DLD in the GSE:39582 dataset were similar to those in the TCGA database (Fig. 4B, 4D,4F,4H). These findings suggest that DLD on colon cancer cells may play an important role in the energy metabolism and regulation of the disease.The genes with the strongest associations were (cor = 0.737, P = 8.93E-89), NDUFA5 (cor = 0.691, P = 5.78E-74), SDHD (cor = 0.654, P = 6.94E-64) and, SNX4 (cor = 0.653, P = 1.37E-63), when cor > 0.5 and P < 0.05 were set as the cutoff values (Fig. 4I).

We divided the samples in the TCGA COAD dataset into two groups according to the cut-off points of DLD expression levels, with 713 up-regulated genes and 4018 down-regulated genes (Fig. 4J). To further investigate the potential functions of DLD in CC, we performed GSEA analysis based on DLD levels in the TCGA database. a large set of genes were negatively enriched in the DLD high expression group, including pathways of tumors (NES=-2.38, FDR < 0.0001, Fig. 4L), Hh signaling (NES=-2.33, FDR = 0.001, Fig. 4L), WNT signaling pathway (NES=-1.96, FDR = 0.008, Fig. 4L), ECM (NES=-2.03, FDR = 0.007, Fig. 4L), angiogenesis (NES= -2.75, FDR = < 0.0001, Fig. 4K),blood vessel morphogenesis (NES= -2.8, FDR = < 0.0001, Fig. 4K),cell development (NES= -3.06, FDR = < 0.0001, Fig. 4K), cell proliferation(NES= -2.89, FDR = < 0.0001, Fig. 4K), response to oxygen-containing compound (NES= -2.7, FDR = < 0.0001, Fig. 4K).

3.5 DLD expression levels are associated with tumor mutation burden.

To determine whether DLD expression levels correlate with specific genomic features in CC, we performed somatic mutation analysis based on DLD expression levels using the "maftools" package in the TCGA-COAD database, which shows the top 30 mutated genes. In the high DLD group, 66.3% (173/261) of the samples were mutated, with a high frequency of mutations in APC (38%), TP53 (32%), TNN (32%) and KRAS (30%) (Fig. 5A). In contrast, in the low DLD group, up to 99.5% (201/202) of samples were mutated, with TTN (55%), APC (53%), TP53 (49%), KRAS (39%) and MUC16 (30%) mutated more frequently (Fig. 5B).

3.6 The relationship between DLD expression levels in single cells and tumor functional status

We analyzed the expression of DLD in COAD single cells and its relationship with tumor functional status by CancerSEA database. We found that DLD expression in COLD was positively correlated with "invasion" (r = 0.39, P < 0.05, Fig. 6A) and " DNA repair" (r = 0.29, P < 0.05, Fig. 6B). In contrast, DLD expression was negatively correlated with "angiogenesis" (r = − 0.28, P < 0.05, Fig. 6C).

3.7 Relationship between DLD and immune cell infiltration

The relationship between DLD expression and immune infiltration of CC was analyzed by the "Gene" module of the TIMER website. The results showed that DLD expression was associated with CD8 T cells (r = 0.225, P < 0.05), B cells (r = 0.214, P < 0.05), NK cells (r = 0.15, P < 0.05), CD4 T cells (r = 0.101, P < 0.05), macrophages (r = 0.193, P < 0.05), neutrophils (r = 0.124, P < 0.05), P < 0.05), and dendritic cell infiltration levels were positively correlated (Fig. 7A).

To confirm in depth the role of DLD in the tumor immune microenvironment, we assessed the levels of 28 immune cells using the ssGSEA algorithm (Fig. 7B-D). TCGA database COAD samples were assigned to either the high DLD-expressing or low DLD-expressing group based on the optimal cut point. In the high DLD-expressing samples, CD4 T cells, type 1 T helper cells, type 2 T helper cells, regulatory T cells, memory B cells, and NK cells were significantly increased in the high DLD-expressing samples. However, in samples with low DLD expression, CD8 T cells, Tfh cells, gamma delta T cells, type 17 T helper cells, activated B cells, immature B cells, CD56dim natural killer cells, myeloid derived suppressor cell, activated dendritic cell, plasmacytoid dendritic cell,iImmature dendritic cell, macrophage, eosinophil, mast cell, monocyte, neutrophil decreased (P < 0.05, Fig. 7D). DLD may regulate the immune microenvironment of colon cancer by regulating NK cells.

Colon cancer is a fatal malignancy of the digestive system and the prognosis remains very poor¹⁹. Currently, conventional treatment options also have limitations in improving patient survival outcomes, including surgery and chemotherapy ^20–22. At the same time, immune checkpoint inhibitor therapy offers patients better monitoring opportunities, unique treatment options, and greater hope for prolonged survival ³. Therefore, the discovery of new biomarkers associated with immunomodulation in colon cancer is crucial for its diagnosis, treatment and prognosis.

The DLD gene encodes a member of the class I pyridine nucleotide-disulfide oxidoreductase family that plays multiple roles in the decarboxylation of pyruvate and the oxidation of dihydrolipoamide and is involved in constituting α-keto acid dehydrogenase complexes (e.g. α-ketoglutarate dehydrogenase, α-ketoadipate dehydrogenase and glycine decarboxylase) ^{6, 23–25}.DLD is a mitochondrial respiration-dependent copper ion carrier induced novel regulated positive regulators of cell death that regulate copper-dependent cell death by affecting the tumor TCA cycle and the underlying biosynthetic processes that support tumor growth, which may provide new ideas for the therapeutic action of certain cancer-targeted drugs ^5,26. Previous studies have found that DLD is involved in constituting pyruvate dehydrogenase complex and α-ketoglutarate dehydrogenase complex, which are associated with several fatal metabolic diseases and have been identified as important metabolic targets in cancer^27–30. Evidence from studies suggests that DLD downregulation may affect mitochondrial metabolism, thereby decreasing the levels of downstream metabolites in the TCA cycle, inducing melanoma cell death, and inhibiting tumor progression in humans by promoting ROS production and altering energy metabolism ³¹. However, more bioactivities of DLD as a promising therapeutic target need to be found in different diseases.

Expression profiling based on the TCGA-COAD database and the GSE:39582 dataset showed that DLD expression was significantly lower in CC than in normal tissues (Fig. 1C). We obtained DLD protein expression levels in human normal colon tissues and colon cancer tissues in the HPA database, and the results were consistent with DLD mRNA expression levels (Fig. 3A). In our study, low DLD expression was associated with malignant clinicopathological features such as staging and grading. We then evaluated the prognostic value of DLD in CC using two cohorts and found that high DLD expression was significantly associated with OS prognosis of CC patients (Fig. 2B).

To reveal the hidden mechanism behind their reduced invasive growth pattern, we constructed a DLD gene co-expression network in the TCGA-COAD dataset and performed GO and KEGG enrichment analysis. In this study, the expression of CYCS, NDUFA5, SDHD, and SNX4 in CC showed the highest correlation with DLD. Copper ion-induced ROS leads to cytochrome C (CYCS) release from mitochondria into the cytoplasm, resulting in caspases activation and DNA breakage in PC12 pheochromocytoma cells ³². deubiquitination of CYCS inhibits hepatocellular carcinoma ³³. nDUFA5 (Ubiquinone Oxidoreductase Subunit A5) is involved in encoding the B13 subunit of the mitochondrial respiratory chain complex I, a protective prognostic gene in ovarian cancer ^34,35. sdhd benefits patients with COAD and is a novel biomarker ³⁶. A retrospective analysis based on GSE66229 data showed that SNX4 was associated with good survival outcomes in gastric cancer³⁷. Analysis of GO and KEGG enrichment by co-expression correlated with many classical signaling pathways such as metabolic pathways, oxidative phosphorylation and peroxisome. GSEA analysis showed that differential genes based on DLD high expression grouping were mainly enriched in some important tumor associated KEGG pathways. This study is the first to reveal that DLD is associated with pathways in CC in cancer, hedgehog signaling pathway, focal adhesion, ECM, WNT signaling, cell adhesion molecules, leukocyte transendothelial migration, regulation of actin cytoskeleton, calcium signaling pathway, and chemokine signaling pathway.

Tumor mutation burden (TMB) reflects the number of mutations in the tumor and generates immunogenic neoantigens, thus increasing the likelihood of T cell recognition and correlating with the response to immune checkpoint inhibitors (ICIs) ³⁸. Consistent with PD-L1 expression, TMB may provide a reference for the selection of ICIs for the treatment of tumor patients ^39,40. In this study, a somatic mutation analysis was performed in the TCGA database based on DLD expression levels. We listed the top 30 genes with the highest mutation rates in the high and low DLD groups (Fig. 6A, B).TTN, APC, TP53, KRAS, MUC16, and SYNE1 were the most frequently mutated genes in both groups, while PIK3CA appeared in the top 5 in the low DLD group.PIK3CA mutations identified colorectal cancer as a poor prognostic factor associated with solid organ transplantation and mismatch repair deficiency, and PIK3CA mutations predict reduced response to anti-EGFR therapy in patients with metastatic colorectal cancer ^41,42. In conclusion, low expression DLD has a higher tumor mutation rate compared to high expression DLD, which would provide some direction for the selection of immunotherapy regimens for patients with low expression DLD.

This study investigated the potential mechanisms of DLD high expression prognosis based on COAD single cell function and immune infiltration levels.The CancerSEA database reveals the functional states of different cancer cells at the single cell level, involving 25 functional states of 41,900 cancer single cells from 14 cancer types ⁴³. In this study, DLD expression was positively correlated with tumor cell "invasion" and " DNA repair" as analyzed by the CancerSEA database. In contrast, DLD expression was negatively correlated with "angiogenesis". These results suggest that high expression of DLD plays a crucial role in the suppression of colon tumors. Recent evidence suggests that DLD is highly correlated with lipid acylated protein abundance in a variety of human tumor cells and that cell lines with high levels of lipid acylated proteins are sensitive to copper-induced cell death ⁵. This may be another important factor in the inhibition of tumor invasion by DLD.

Immune infiltrating cells are an important component of the tumor microenvironment and play an important role in influencing tumor growth, progression, treatment outcome, and patient prognosis ^44–46. Higher immune infiltration in colorectal cancer is associated with improved time to recurrence, OS, and DFS ^47,48. TIMER was used to explore the correlation between DLD expression and the level of immune cell infiltration in tumors and showed that samples with high DLD expression tended to infiltrate more immunocytes. By analyzing the infiltration levels of 28 immune cells in the high and low DLD expression groups using the ssGSEA algorithm, we also observed that the infiltration levels of CD4 T cells, type 1 T helper cells, type 2 T helper cells, regulatory T cells, memory B cells, and NK cells in the high DLD group were elevated, while CD8 T cells, Tfh cells, gamma delta T cells, type 17 T helper cells, activated B cells, immature B cells, CD56dim natural killer cells, myeloid derived suppressor cell, activated dendritic cell, plasmacytoid dendritic cell, immature dendritic cell, macrophage, eosinophil, mast cell, monocyte ,neutrophil infiltration levels were reduced. A higher percentage of NK cells were activated in the high DLD group, a cell particularly known in oncology for its innate ability to recognize and spontaneously kill tumor cells ⁴⁹. Thus, a greater proportion of NK cells being activated may reduce tumor immune escape in the tumor immune microenvironment, which may be a potential reason for the prognostic benefit of high DLD expression in CC patients. Interestingly, the high DLD group also possessed a higher proportion of Tregs. it is known that Tregs contribute to suppress excessive immune activation and act as immunosuppressive cells to coordinate tumor immune escape and are considered targets of systemic immunotherapy ⁵⁰. These analyses suggest that DLD is involved in regulating the immune mechanisms of the CC tumor microenvironment, particularly the regulation of NK cells. we hypothesize that DLD plays an important role in recruiting infiltrating immune cells and regulating immunity in CC, thereby affecting prognosis. However, more studies are needed to confirm this hypothesis, especially the effect of DLD on NK cells in the CC microenvironment.

Overall, our study shows that DLD is overexpressed in CC tissues and correlates with multiple clinicopathological features. Furthermore, high DLD expression was significantly associated with beneficial prognosis in CC as validated by analysis of the TCGA database and the GEO database. In terms of biological functions, we demonstrated that DLD is involved in pathway in cancer, hedgehog signaling pathway, focal adhesion, ECM, WNT signaling pathway in CC. In addition, DLD expression correlated with the levels of tumor genetic mutation genes and various immune cells in the tumor microenvironment. Taken together, these results suggest a potential role for DLD as a prognostic marker and therapeutic target for future CC.

Ethics approval and consent to participate

Is not applicable for this article.

Consent for publication

All authors read and approved the final manuscript.

Availability of data and materials

The analyzed dataset of this study can be found on the TCGA-COAD project(http://www.cancer.gov/tcga),GEO(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE:39582/GSE81861),HPA(https://www.proteinatlas.org/),UALCAN(http://ualcan.path.uab.edu),CancerSEA(http://biocc.hrbmu.edu.cn/CancerSEA/home.jsp) ,and TIMER(http://timer.cistrome.org) websites. The data supporting the results of this study can be obtained from the corresponding author upon reasonable request.

Competing interests

The authors declare that there are no conflicts of interest in the authorship and publication of this study.

Funding

the work was supported by the Shaanxi Province Key Research and Development Program (2022JM502, 2023-YBSF-072), the Natural Science Basic Research Plan in Shaanxi Province(2022JQ-980), the Provincial Health Research Foundation in Shaanxi Province(2022E008), and the Key Laboratory of Minimally Invasive Techniques & Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou, P.R. China (21SZDSYS16, 21SZDSY10).

Authors' contributions

F., N.L., L.Z. designed the research, analysed and interpreted the data, and wrote the manuscript; M.Z., M.C., Z.Y., J.Z., J.L. performed the research, analysed the data, and reviewed the manuscript. All authors reviewed the manuscript.

Acknowledgements

Thank you to “OmicShare Tools(https://www.omicshare.com/tools)”.

H S, J F, Rl S, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians. 2021;71(3). doi:10.3322/caac.21660
H Y, J W, J Z, et al. Integrated bioinformatics analysis of key genes involved in progress of colon cancer. Molecular genetics & genomic medicine. 2019;7(4). doi:10.1002/mgg3.588
Mf T. Revolutionizing the landscape of colorectal cancer treatment: The potential role of immune checkpoint inhibitors. International journal of cancer. 2020;147(11). doi:10.1002/ijc.33056
Gt G, Lm W, Mb A. Predictive biomarkers for checkpoint inhibitor-based immunotherapy. The Lancet Oncology. 2016;17(12). doi:10.1016/S1470-2045(16)30406-5
P T, S C, B P, et al. Copper induces cell death by targeting lipoylated TCA cycle proteins. Science (New York, NY). 2022;375(6586). doi:10.1126/science.abf0529
X L, L W, Y L, et al. Tyrosine phosphorylation of dihydrolipoamide dehydrogenase as a potential cadmium target and its inhibitory role in regulating mouse sperm motility. Toxicology. 2016;357-358. doi:10.1016/j.tox.2016.06.003
W Y, Q G, H W, et al. Comprehensive analysis of the cuproptosis-related gene DLD across cancers: A potential prognostic and immunotherapeutic target. Frontiers in pharmacology. 2023;14. doi:10.3389/fphar.2023.1111462
H Q, D Z. Oncogenic role of copper‑induced cell death‑associated protein DLD in human cancer: A pan‑cancer analysis and experimental verification. Oncology letters. 2023;25(5). doi:10.3892/ol.2023.13800
L M, A de R, A D, et al. Gene expression classification of colon cancer into molecular subtypes: characterization, validation, and prognostic value. PLoS medicine. 2013;10(5). doi:10.1371/journal.pmed.1001453
Ds C, Sk K, Pk K, et al. UALCAN: An update to the integrated cancer data analysis platform. Neoplasia (New York, NY). 2022;25. doi:10.1016/j.neo.2022.01.001
Ds C, B B, Sah B, et al. UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia (New York, NY). 2017;19(8). doi:10.1016/j.neo.2017.05.002
M U, L F, Bm H, et al. Proteomics. Tissue-based map of the human proteome. Science (New York, NY). 2015;347(6220). doi:10.1126/science.1260419
M U, C Z, S L, et al. A pathology atlas of the human cancer transcriptome. Science (New York, NY). 2017;357(6352). doi:10.1126/science.aan2507
W H da, Bt S, Ra L. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature protocols. 2009;4(1). doi:10.1038/nprot.2008.211
Mi L, W H, S A. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology. 2014;15(12). doi:10.1186/s13059-014-0550-8
T L, J F, B W, et al. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer research. 2017;77(21). doi:10.1158/0008-5472.CAN-17-0307
Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2022. Nucleic acids research. 2022;50(D1). doi:10.1093/nar/gkab951
X R, N T, A H, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC bioinformatics. 2011;12. doi:10.1186/1471-2105-12-77
S P, A B, S M, et al. Impact of microRNAs on regulatory networks and pathways in human colorectal carcinogenesis and development of metastasis. BMC genomics. 2013;14. doi:10.1186/1471-2164-14-589
Ej K, Wm G, D L, et al. Colorectal cancer. Nature reviews Disease primers. 2015;1. doi:10.1038/nrdp.2015.65
Kgm B, Mj S. Progress and future direction in the management of advanced colorectal cancer. The British journal of surgery. 2018;105(6). doi:10.1002/bjs.10759
T M, K Y, H H, et al. Recent updates in the surgical treatment of colorectal cancer. Annals of gastroenterological surgery. 2018;2(2). doi:10.1002/ags3.12061
If D, J C, Mf M, et al. Dihydrolipoamide dehydrogenase, pyruvate oxidation, and acetylation-dependent mechanisms intersecting drug iatrogenesis. Cellular and molecular life sciences : CMLS. 2021;78(23). doi:10.1007/s00018-021-03996-3
G F, A D. The moonlighting activities of dihydrolipoamide dehydrogenase: Biotechnological and biomedical applications. Journal of molecular recognition : JMR. 2021;34(11). doi:10.1002/jmr.2924
H W, Y W, L T, H Y, Z S. Global Research Trends of Ferroptosis: A Rapidly Evolving Field With Enormous Potential. Frontiers in cell and developmental biology. 2021;9. doi:10.3389/fcell.2021.646311
C X, L Y, L J, et al. Prognostic and immunological role of cuproptosis-related protein FDX1 in pan-cancer. Frontiers in genetics. 2022;13. doi:10.3389/fgene.2022.962028
A A, J W, R M, et al. Structural alterations induced by ten disease-causing mutations of human dihydrolipoamide dehydrogenase analyzed by hydrogen/deuterium-exchange mass spectrometry: Implications for the structural basis of E3 deficiency. Biochimica et biophysica acta. 2016;1862(11). doi:10.1016/j.bbadis.2016.08.013
Jw K, I T, Gl S, Cv D. HIF-1-mediated expression of pyruvate dehydrogenase kinase: a metabolic switch required for cellular adaptation to hypoxia. Cell metabolism. 2006;3(3). doi:10.1016/j.cmet.2006.02.002
Gl S. HIF-1: upstream and downstream of cancer metabolism. Current opinion in genetics & development. 2010;20(1). doi:10.1016/j.gde.2009.10.009
S A, A V, E M, et al. α-ketoglutarate dehydrogenase inhibition counteracts breast cancer-associated lung metastasis. Cell death & disease. 2018;9(7). doi:10.1038/s41419-018-0802-8
S Y, Mc K, Sh O, et al. Downregulation of dihydrolipoyl dehydrogenase by UVA suppresses melanoma progression via triggering oxidative stress and altering energy metabolism. Free radical biology & medicine. 2021;162. doi:10.1016/j.freeradbiomed.2020.11.037
Q X, D Y, X C, et al. Copper-dependent autophagic degradation of GPX4 drives ferroptosis. Autophagy. Published online December 1, 2023. doi:10.1080/15548627.2023.2165323
Y Y, W M, Y G, et al. USP53 plays an antitumor role in hepatocellular carcinoma through deubiquitination of cytochrome c. Oncogenesis. 2022;11(1). doi:10.1038/s41389-022-00404-8
G S, N R, I P, et al. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Current protocols in bioinformatics. 2016;54. doi:10.1002/cpbi.5
Qf Z, Yk L, Cy C, et al. Identification and validation of a prognostic index based on a metabolic-genomic landscape analysis of ovarian cancer. Bioscience reports. 2020;40(9). doi:10.1042/BSR20201937
H N, P G, J F, et al. Comprehensive analysis of the prognosis, tumor microenvironment, and immunotherapy response of SDHs in colon adenocarcinoma. Frontiers in immunology. 2023;14. doi:10.3389/fimmu.2023.1093974
B H, G Y, X S. Identification of specific role of SNX family in gastric cancer prognosis evaluation. Scientific reports. 2022;12(1). doi:10.1038/s41598-022-14266-y
Dl J, A G, D de MG, R K. The Challenges of Tumor Mutational Burden as an Immunotherapy Biomarker. Cancer cell. 2021;39(2). doi:10.1016/j.ccell.2020.10.001
Ta C, M Y, E J, et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Annals of oncology : official journal of the European Society for Medical Oncology. 2019;30(1). doi:10.1093/annonc/mdy495
N K, M A, Wb G, E D, Mj D, H T. Burden of unique and low prevalence somatic mutations correlates with cancer survival. Scientific reports. 2019;9(1). doi:10.1038/s41598-019-41015-5
Es C, V L, H W, et al. Solid Organ Transplantation Is Associated with an Increased Rate of Mismatch Repair Deficiency and PIK3CA Mutations in Colorectal Cancer. Current oncology (Toronto, Ont). 2022;30(1). doi:10.3390/curroncol30010006
Es T, W F, Tc K, et al. Prognostic and Predictive Value of PIK3CA Mutations in Metastatic Colorectal Cancer. Targeted oncology. 2022;17(4). doi:10.1007/s11523-022-00898-7
H Y, M Y, G Z, et al. CancerSEA: a cancer single-cell state atlas. Nucleic acids research. 2019;47(D1). doi:10.1093/nar/gky939
M B, Ew R, K K, et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nature medicine. 2018;24(5). doi:10.1038/s41591-018-0014-x
Aj O, Pkh L, As U, et al. Tissue-Dependent Tumor Microenvironments and Their Impact on Immunotherapy Responses. Frontiers in immunology. 2018;9. doi:10.3389/fimmu.2018.00070
Aj G, Am N, Cl L, et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nature medicine. 2015;21(8). doi:10.1038/nm.3909
E P, Cp V, Gw M, G P. Relationships Between Immune Landscapes, Genetic Subtypes and Responses to Immunotherapy in Colorectal Cancer. Frontiers in immunology. 2020;11. doi:10.3389/fimmu.2020.00369
F P, B M, F M, et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet (London, England). 2018;391(10135). doi:10.1016/S0140-6736(18)30789-X
X Y, Jj J, Mf T, et al. Nanoparticle-Mediated Intracellular Protection of Natural Killer Cells Avoids Cryoinjury and Retains Potent Antitumor Functions. Advanced science (Weinheim, Baden-Wurttemberg, Germany). 2020;7(9). doi:10.1002/advs.201902938
La H, Md G, L K, et al. Tissue-specific Tregs in cancer metastasis: opportunities for precision immunotherapy. Cellular & molecular immunology. 2022;19(1). doi:10.1038/s41423-021-00742-4

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High expression of cuproptosis-related gene DLD in relation to good prognosis and immune cells infiltration in colon cancer

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Abstract

Figures

1 Introduction

2 Methods

2.1 Acquisition of data

2.2 DLD expression profiles in CC

2.3 Relationship between DLD expression and CC survival prognosis

2.4 Enrichment analysis of DLD gene co-expression network in CC

2.5 Gene set enrichment analysis

2.6 Tumor Mutation Load Analysis

2.7 Analysis of tumor immune infiltration

2.8 Single-cell sequencing data analysis

2.9 Statistical analysis

3 Results

3.1 Reduced expression of DLD in CC.

3.2 Relationship between DLD expression and clinical characteristics of CC patients

3.3 High DLD expression implies a better prognosis for CC patients.

3.4 Enrichment analysis of DLD co-expressed genes and DEGs in CC

3.5 DLD expression levels are associated with tumor mutation burden.

3.6 The relationship between DLD expression levels in single cells and tumor functional status

3.7 Relationship between DLD and immune cell infiltration

4 Discussion

5 Conclusion

Declarations

References

Additional Declarations

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