Telomere Maintenance Patterns Exhibit Distinct Prognostic and Immune Landscapes in Cutaneous Malignant Melanoma

doi:10.21203/rs.3.rs-4612915/v1

Download PDF

Article

Telomere Maintenance Patterns Exhibit Distinct Prognostic and Immune Landscapes in Cutaneous Malignant Melanoma

https://doi.org/10.21203/rs.3.rs-4612915/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

Cutaneous malignant melanoma (CMM) is notorious for its ability to metastasize or spread to other parts of the body, rendering it one of the most perilous forms of skin cancer. This study aims to investigate the prognostic significance of telomere maintenance in CMM.

Materials & Methods

A total of 593 CMM samples were collected from TCGA and GEO datasets. CMM samples were clustered into two telomere maintenance clusters using nonnegative matrix factorization (NMF). Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were employed to investigate the biological difference between distinct TM modification patterns. The immune landscape of CMM was visualized through single-sample gene-set enrichment analysis (ssGSEA) and CIBERSORT. Additionally, a risk score was identified using least absolute shrinkage and selection operator (LASSO), and hub-genes were determined via multi-variate COX analysis. Survival analyses were performed using the Kaplan-Meier method, and differences in survival were assessed using the log-rank test. Single-cell sequencing validation was conducted using the Tumor Immune Single Cell Hub on GSE123139 dataset(TISCH).

Results

Two telomere maintenance patterns were identified based on CMM samples. There were 6 hub-genes were screened by LASSO and multi-variate COX analyses and risk score was calculated based on the expression and coefficient of each hub-gene. CMM samples were classified into low- and high-risk groups. The overall survival (OS) of low-risk group was significantly higher than the high-risk group. A prognostic signature was identified based on risk score and patients’ clinical characteristics and visualized by nomogram.

Conclusions

Our study explored the potential prognostic value of telomere maintenance in CMM. We have developed a prognostic signature for CMM based on telomere maintenance related genes. telomere maintenance may aid in risk stratification and treatment strategy for patients with CMM.

Biological sciences/Computational biology and bioinformatics/Genome informatics

Biological sciences/Cancer/Cancer microenvironment

Biological sciences/Cancer/Tumour biomarkers

Health sciences/Oncology

Telomere maintenance

Cutaneous malignant melanoma

Immune infiltration

Single-cell sequencing

Prognosis

Cutaneous malignant melanoma (CMM) is a type of skin cancer that originates in melanocytes, the cells responsible for producing melanin, which gives skin its color. Melanoma can occur anywhere on the body, but it is most commonly found on areas exposed to the sun, such as the face, neck, arms, and legs¹. This form of cancer typically develops when unrepaired DNA damage to skin cells triggers mutations that lead the cells to multiply rapidly and form malignant tumors². While excessive exposure to ultraviolet (UV) radiation from the sun or tanning beds is a major risk factor, genetics and family history also play a role in predisposing individuals to melanoma³. Melanoma often presents as a new mole or an existing mole that changes in size, shape, or color. It may also appear as an unusual growth or spot on the skin. Early detection is crucial for successful treatment, as melanoma can spread quickly to other parts of the body if left untreated⁴. The prognosis of melanoma is relatedly poor compared with other cancers. The 5-year overall survival (OS) rate is typically high, often exceeding 90% in stage 0–1. However, for patients with advanced melanoma, the cancer often has spread to distant organs such as the liver, lungs, brain, or bones, with 5-year survival rate around 25% or less⁵. Treatment options for cutaneous malignant melanoma depend on the stage of the cancer and may include surgical removal of the tumor, chemotherapy, radiation therapy, immunotherapy, tTMeted therapy, or a combination of these approaches⁶. Hence, there is an urgent need for novel methods to assess the risk of patients with cutaneous malignant melanoma (CMM) in order to guide clinical treatment effectively.

Telomere maintenance is a crucial cellular process that ensures the stability and integrity of chromosomes. Telomeres are repetitive DNA sequences located at the ends of chromosomes, protecting the genetic material from degradation and fusion with neighboring chromosomes⁷. During each round of cell division, telomeres undergo shortening due to the end-replication problem, where the DNA polymerase cannot fully replicate the ends of linear chromosomes⁸. To counteract telomere shortening, cells employ various mechanisms for telomere maintenance. One prominent mechanism is through the action of telomerase, a specialized enzyme that adds repetitive DNA sequences to the ends of chromosomes, thus counteracting telomere shortening⁹. Another mechanism, known as alternative lengthening of telomeres (ALT), relies on homologous recombination and DNA replication processes to elongate telomeres in the absence of telomerase activity¹⁰.

Telomere maintenance is crucial for cellular longevity and is implicated in various biological processes, including aging, cancer, and stem cell function^11–13. Dysregulation of telomere maintenance mechanisms can lead to genomic instability, cellular senescence, and disease pathogenesis. For example, TM shows a strong positive correlation with tumor progression in sarcomas¹⁴. In addition, TM is closely associated with the occurrence and development of gastrointestinal cancers¹⁵. However, the potential value of TM in CMM has not been fully investigated, further analysis regarding its significance in CMM remains necessary.

Our study gathered CMM samples from the Cancer Genome Atlas (TCGA) and the Gene-Expression Omnibus (GEO) to investigate the significance of telomere maintenance in CMM. We developed a scoring system to stratify the risk of CMM samples based on tTMGs. Additionally, we explored the prognostic value of telomere maintenance by constructing a nomogram. The study flowchart is depicted in Figure. 1.

2.1 Dataset acquisition

A total of 3182 telomere maintenance genes (TMGs) were identified from Genecards with a relevance score > 4. The differentially expressed telomere maintenance pathway genes (DETMGs) between normal and CMM tissues was identified via GSE15605¹⁶ dataset which was analyzed using the "limma" package¹⁷ with the criteria set as |logFC| > 1 and adjusted p-value < 0.05. The genes we focused were the intersection of TMGs from GSE15605 and Genecards and 346 TMGs were eventually confirmed. The gene expression and corresponding clinical characteristics of 418 CMM samples from the TCGA dataset, 43 CMM samples from the GSE19234¹⁸, 55 CMM samples from GSE22153¹⁹, 77 CMM samples from GSE54467²⁰, were integrated and analyzed. The "limma" package was employed to control batch effects. Finally, 593 CMM patients were included and the baseline characteristics was shown in Table. 1.

2.2 Identification of telomere maintenance patterns

The “Consensus Cluster Plus” package was utilized to identify distinct modification patterns of TM in CMM patients using Nonnegative Matrix Factorization (NMF) clustering. CMM patients were classified into distinct clusters based on their expression of TMGs. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were performed to access the biological difference between distinct TM modification patterns.

2.3 Estimation of immune infiltration

The quantification of immune cell (IC) infiltration into the TME of CMM and estimation of immune infiltration between different TM modification patterns were analyzed via single-sample gene-set enrichment analysis (ssGSEA). The dataset for the types of ICs, containing 23 types, was obtained from Charoentong's research ²¹. The correlation between the DETMGs and ICs as well as the correlation between hub-genes and ICs were both calculated using CYBERSORT tool²².

2.4 Establishment of risk score

The risk score was identified as follows. First, the 593 CMM patients were randomly divided into a training set and a testing set with a ratio of 1:1. The gene expression of DETMGs and survival labels were first obtained. Subsequently, the LASSO algorithm was applied to the training set to identify hub genes and mitigate overfitting in the prognostic model. Finally, through multi-variate Cox regression, the coefficients of the hub genes were determined, and a risk score was calculated using the following formula:

where Ci represented the coefficient of corresponding TMGs and TMGi represented the expression of each TMG.

Two risk group called low- and high risk group were determined based on the median of risk score.

2.5 Validation of hub-genes by single-cell sequencing

A single-cell sequencing dataset (GSE151214²³ ) consisting of 12 CMM samples was used to confirm the expression of key genes in immune cells (ICs). The Tumor Immune Single Cell Hub (TISCH, http://tisch.comp-genomics.org)²⁴ was utilized to perform various data quality checks, remove batch effects, cluster the cells, annotate cell types, classify malignant cells, and perform differential expression analysis.

2.6 Identification of prognostic nomogram

To assess the impact of clinical features and risk scores on prognosis, we conducted multivariate analysis that included factors such as age, gender, stage, and risk. The results were visualized and quantified using a nomogram, which estimated the probability of overall survival (OS) in patients with cutaneous malignant melanoma (CMM) at 1, 3, and 5 years. Additionally, the overall clinical benefit was evaluated using decision curve analysis (DCA), which compared the net benefit provided by the risk scores and clinical characteristics and both.

2.7 Statistical analysis

One-way ANCMMA and Kruskal-Wallis tests compared group differences, with subgroups defined by the "survminer" package based on risk factor relationships²⁵. Spearman and distance correlation analyses determined correlations between immune cell expression in the tumor microenvironment (TME) and TMGs. Optimal parameters were identified by iteratively testing all potential cut points using the "surv-cutpoint" function. Prognostic analysis employed the Kaplan-Meier method, with significance confirmed via log-rank tests. Prognostic analysis for CMM patients was visualized using the "forestplot" package. Receiver Operating Characteristic (RCMM) curves and area under the curve (AUC) were plotted using the "timeRCMM" package ²⁶. package was used to plot Receiver Operating Characteristic (RCMM) curves and calculate the area under the curve (AUC). A significance level of P value< 0.05 was applied for all statistical tests. The analyses and figures were conducted using R software (version 4.0.3)²⁷.

3.1 Screening of TMGs

A total of 1437 differentially expressed genes (DEGs) were identified from the GSE15605 dataset, which includes 16 normal skin samples and 58 CMM samples. These DEGs were selected based on the criteria of |logFC| > 1 and adjusted p-value < 0.05 (Figure 2A). By intersecting this set of DEGs with the 3182 TMGs obtained from Genecards, we narrowed down to 346 DEGs for further investigation (Figure 2B). Next, we performed univariate Cox analysis on these 346 DEGs, and showed the correlation between TMGs in CMM(Figure 2C). Among them, 97 DEGs were found to have a significant impact on the OS of CMM patients. These 97 DEGs were used to construct TM patterns (Figure 2D). Additionally, we explored the correlations between these 97 DEGs, as illustrated in Figure 3D.

3.2 Construction of telomere maintenance patterns

Based on the expression of TMGs, patients were categorized into two distinct TM modification patterns using the "ConsensusClusterPlus"²⁸ package, These patterns are referred to as TM cluster A and TM cluster B (Figure 3A). The CMM samples exhibited clear separation into two clusters based on various techniques: principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE) and Uniform Manifold Approximation and Projection (UAMP) (Figure 3B-3D). The differentially expressed TMGs between TM cluster A and TM cluster B are visualized in Figure 3E. Notably, TM cluster A demonstrated significantly better OS compared to TM cluster B (Figure 3F). The survival analysis revealed that TM cluster A showed a significantly better OS compared to TM cluster B (Figure 3F). Further analysis of both TM clusters and the clinical features of patients was conducted using a heatmap, revealing significant differences in transcriptional profiles between the distinct TM clusters (Figure 3G). Additionally, the investigation of innate immune cell (IC) infiltration in the tumor microenvironment (TME) highlighted substantial differences in infiltration levels. Specifically, Activated B Cells, Activated CD4 T cells, Activated CD8 T cells, Activated dendritic cells, Eosinophils, and CD56dim natural killer cells exhibited varying levels of infiltration among the two TM clusters (Figure 4A). Most ICs showed decreased infiltration in TM cluster A, suggesting that lower IC infiltrations may correlate with better OS in CMM. To explore the biological behavior of the two TM clusters, we conducted Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA). As depicted in Figures 4B and 4C, both TM clusters displayed differences in the enrichment of biological pathways.

3.3 Identification of risk score

The LASSO algorithm was employed to analyze TM modification patterns based on OS-related TMGs (Figure 5A and 5B). Six hub-genes were considered by LASSO: TAP2, SOCS3, SLC20A1, PROM1, KPNA2, and CENPO (Figure 5C). Subsequently, multivariate Cox analysis was utilized to assess the coefficients of these hub-genes. Survival analyses were conducted on the training, testing, and entire patient cohorts, revealing a higher OS rate among patients in the low-risk group (refer to Figure 5D-F). To further evaluate the performance of risk scores, receiver operating characteristic (RCMM) curves and area under the curve (AUC) values were utilized. The results demonstrated strong predictive capabilities for survival at 1, 3, and 5 years across all three patient groups (see Figure 5G-I).

3.4 Immune landscape between risk groups

To investigate the relationship between hub-genes and IC infiltration, we employed the CIBERSORT algorithm to quantify the proportion of ICs. Additionally, we conducted an in-depth analysis of the correlation between ICs and hub-genes (Figure 6A-D). Figure 6A illustrated the composition of ICs in each CMM sample, revealing significant differences in IC infiltration across risk groups. Notably, positive correlations were observed between the risk score and T cells CD8 as well as T cells CD4 memory activated (Figure 6E-6F). Conversely, negative correlations were found with Mast cells resting and Macrophages M0, suggesting a potential immunological mechanism underlying the risk score (Figure 6G-6H).

3.5 Validation of hub-genes by single-cell sequencing

The single-cell sequencing dataset GSE123139²⁹ was meticulously employed to validate the expression profiles of the hub genes. Subsequently, this dataset underwent a comprehensive assessment using TISCH, a robust tool proficient in batch effect correction, clustering, and precise cell-type annotation (Figure 7A-7C).

The detailed evaluation of the relative abundance of hub genes across diverse cell types is visually presented in Figure 7D-7O. Notably, KPNA2, SLC20A1, SCMMS3, and TAP2 exhibited remarkable expression in various cell types, which may provide compelling evidence suggesting that the hub-genes probably influence the composition and dynamics of the TME.

3.6 Establishment of prognostic signature

The TM clusters and their correlation with risk scores have undergone thorough investigation. Patients in TM cluster A predominantly exhibited low risk scores (Figure 8A). This alignment with the results from survival analysis (Figure 3F, 5D-5F) underscored the reliability of risk scores in predicting the OS of CMM. A comprehensive multivariate analysis was conducted to assess the impact of risk score and clinical features, including age, gender and stage, on the OS of CMM. What’s more, the cumulative analysis also confirmed the stratification ability of risk score (Figure 8B). The survival outcomes of CMM patients were vividly presented and quantified using a nomogram plot (Figure 8D). The cumulative score from each covariate provides a valuable tool for predicting individual patient OS rates at 1, 3, and 5 years. Additionally, the calibration curve in Figure 8C showcased that the signature's performance was well-controlled to prevent overfitting. The decision curve analyses (DCAs) at 1 year (Figure 8E), 3 years (Figure 8F), and 5 years (Figure 8G) underscored that the nomogram yielded a substantially higher net clinical benefit, further affirming its prognostic value in CMM.

Recently, studies have explored the role of TM in CMM^30,31, but the potential value of TM has not been fully investigated. In our study, we identified 6 TMGs in the construction of risk score and they may be served as novel biomarkers in the prognosis of CMM. Thus, understanding their functions may lead to novel therapeutic approaches for melanoma treatment.

The TAP2 protein assembles with another protein called TAP1 (produced from the TAP1 gene) to form a complex known as the transporter associated with antigen processing (TAP) complex. TAP2 protein is a key player in antigen presentation, facilitating immune responses by ensuring that the right peptides are displayed to immune cells for recognition and defense against foreign threats^32–35. Studies has indicated that downregulation of TAP1 and TAP2 in primary CMM lesions has been associated with greater lesion thickness, higher disease stage, diminished time to disease progression and reduced OS³⁶. Our study also found that decreased expression of TAP2 was associated with high risk and reduced OS (Fig. 5C-5F).

SOCS3 (Suppressor of Cytokine Signaling 3) is a crucial protein encoded by the SOCS3 gene in humans, which acts as a cytosolic inhibitor of cytokine signaling³⁷. Silenced SOCS-1 may inhibit of melanoma cell metastasis and reduced cell motility and decrease melanoma cell invasion via downregulating of epidermal growth factor receptor (EGFR), Ins-Rα, and fibroblast growth factor receptor³⁸. Given its expression in melanoma cell lines and its relation to tumor invasion, thickness, and disease stage, SOCS-1 could be a potential target for CMM therapy.

SLC20A1, also known as PiT1, is an inorganic phosphate transporter with diverse functions beyond its role in phosphate transport³⁹. High SLC20A1 expression is associated with poor prognoses in Claudin-low and basal-like breast cancers as well as in head and neck cancers^40,41. However, the relationship between SLC20A1 and CMM remains unclear. Further studies are still required to clarify the role of SLC20A1 in CMM.

PROM1, also known as Prominin 1 or CD133, is a glycoprotein encoded by the PROM1 gene and is a pentaspan transmembrane glycoprotein that localizes to membrane protrusions⁴². High expression level of PROM1 may be associated with enhanced tumourigenic potential in CMM⁴³. Furthermore, PROM1 plays a role in enhancing the pro-metastatic properties of melanoma cells. Emerging evidence suggests that it could serve as a potential molecular target to impede the colonization and growth of prominin-1-expressing melanomas in lymph nodes and distant organs⁴⁴. The intriguing findings underscore the importance of investigating PROM1’s impact on CMM progression and its potential therapeutic implications.

KPNA2, also known as Karyopherin alpha 2 or importinα-1, is a nuclear transport protein that accounts for nucleocytoplasmic transport⁴⁵. KPNA2 expression is relatively high in melanoma tissues and cells and Melanoma patients with higher KPNA2 expression tend to have lower overall survival rates and disease-free survival rates⁴⁶. This is concordant with our finding that high expression is characterized with high risk (Fig. 5C). KPNA2 may also promote proliferation ability in melanoma cells through activating NF-κB/p65 signaling pathways in melanoma cells⁴⁷.

CENPO (centromere protein O) can prevent the separation of sister chromatids and cell death after spindle injury. Its expression has been studied across various cancers. A pan-cancer analysis demonstrated that highly expressed in most cancers and upregulation of CENPO is associated with poor prognosis in many cancer types⁴⁸.

There were several limitations should be acknowledged in this study. First, our study primarily relies on clinical characteristics such as age, gender, and disease stage due to the constraints of the available database. To enhance the robustness of future research, larger-scale datasets with more comprehensive clinical information are essential. Second, although we successfully identified hub-genes for predicting the OS rate in CMM, we have yet to elucidate the underlying mechanisms. Further investigations are warranted to explore the intricate interactions between TMGs and CMM. Finally, the data used in our study were sourced from public databases, which may introduce bias. Consequently, the application value of our gene signature is probably affected. Efforts to validate its reliability should consider potential inconsistencies in the data. To continually assess the relevance of TMGs in CMM, additional prospective studies are necessary providing more robust evidence and validate the clinical utility of the identified genes.

Our study has investigated the potential value of telomere maintenance patterns in cutaneous malignant melanoma. Through this investigation, we have identified a prognostic nomogram based on telomere maintenance related genes. This nomogram may hold promise as a novel biomarker for guiding melanoma therapy.

Ethics approval and consent to participate

All data in our research was acquired from public datasets and did not require ethical review or informed consent. All humans were not directly involved in the study.

Consent for publication

All authors have given their consent for publication.

Availability of data and materials

All data in our research can be acquired from TCGA datasets (https://portal.gdc.cancer.gov/) and GEO datasets (https://www.ncbi.nlm.nih.gov/). All figures were generated by an open source software R

Competing interests

All authors claim that there are no potential conflicts of interest in the study.

Funding

This study was supported by the 2021 hospital-level incubation Project of the Second People's Hospital of Yibin (Grant number: 2021FY16). The authors declare no conflicts of interests.

Authors' contributions

All authors contributed to the article and approved the submitted version. LL, CY and FX contributed to the conception and design of the study. DK and TH searched and reviewed studies, extracted and analyzed the data, and wrote the first draft of the manuscript. YL, YJ, KL reviewed and edited the manuscript.

Acknowledgements

All authors contributed to the article and approved the submitted version. There was no third party support in conducting this research, analysing the data, or preparing the manuscript for submission.

Long, G. V., Swetter, S. M., Menzies, A. M., Gershenwald, J. E. & Scolyer, R. A. Cutaneous melanoma. Lancet (London, England) 402, 485-502, doi:10.1016/s0140-6736(23)00821-8 (2023).
Ross, P. M. & Carter, D. M. Actinic DNA damage and the pathogenesis of cutaneous malignant melanoma. J Invest Dermatol 92, 293s-296s, doi:10.1111/1523-1747.ep13076718 (1989).
Leonardi, G. C. et al. Cutaneous melanoma: From pathogenesis to therapy (Review). Int J Oncol 52, 1071-1080, doi:10.3892/ijo.2018.4287 (2018).
Strashilov, S. & Yordanov, A. Aetiology and Pathogenesis of Cutaneous Melanoma: Current Concepts and Advances. Int J Mol Sci 22, doi:10.3390/ijms22126395 (2021).
Abbas, O., Miller, D. D. & Bhawan, J. Cutaneous malignant melanoma: update on diagnostic and prognostic biomarkers. Am J Dermatopathol 36, 363-379, doi:10.1097/DAD.0b013e31828a2ec5 (2014).
Hartman, R. I. & Lin, J. Y. Cutaneous Melanoma-A Review in Detection, Staging, and Management. Hematol Oncol Clin North Am 33, 25-38, doi:10.1016/j.hoc.2018.09.005 (2019).
Ebata, H., Loo, T. M. & Takahashi, A. Telomere Maintenance and the cGAS-STING Pathway in Cancer. Cells 11, doi:10.3390/cells11121958 (2022).
Lundblad, V. Telomere maintenance without telomerase. Oncogene 21, 522-531, doi:10.1038/sj.onc.1205079 (2002).
Giardini, M. A., Segatto, M., da Silva, M. S., Nunes, V. S. & Cano, M. I. Telomere and telomerase biology. Prog Mol Biol Transl Sci 125, 1-40, doi:10.1016/b978-0-12-397898-1.00001-3 (2014).
Gao, J. & Pickett, H. A. Targeting telomeres: advances in telomere maintenance mechanism-specific cancer therapies. Nat Rev Cancer 22, 515-532, doi:10.1038/s41568-022-00490-1 (2022).
Lansdorp, P. M. Telomeres, Telomerase and Cancer. Arch Med Res 53, 741-746, doi:10.1016/j.arcmed.2022.10.004 (2022).
Liu, J., Wang, L., Wang, Z. & Liu, J. P. Roles of Telomere Biology in Cell Senescence, Replicative and Chronological Ageing. Cells 8, doi:10.3390/cells8010054 (2019).
Sarkar, J. & Liu, Y. Fanconi anemia proteins in telomere maintenance. DNA Repair (Amst) 43, 107-112, doi:10.1016/j.dnarep.2016.02.007 (2016).
Johnson, J. E. & Broccoli, D. Telomere maintenance in sarcomas. Curr Opin Oncol 19, 377-382, doi:10.1097/CCO.0b013e3281214423 (2007).
Basu, N. et al. Telomeres and telomere dynamics: relevance to cancers of the GI tract. Expert Rev Gastroenterol Hepatol 7, 733-748, doi:10.1586/17474124.2013.848790 (2013).
Raskin, L. et al. Transcriptome profiling identifies HMGA2 as a biomarker of melanoma progression and prognosis. J Invest Dermatol 133, 2585-2592, doi:10.1038/jid.2013.197 (2013).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43, e47, doi:10.1093/nar/gkv007 (2015).
Bogunovic, D. et al. Immune profile and mitotic index of metastatic melanoma lesions enhance clinical staging in predicting patient survival. Proc Natl Acad Sci U S A 106, 20429-20434, doi:10.1073/pnas.0905139106 (2009).
Jönsson, G. et al. Gene expression profiling-based identification of molecular subtypes in stage IV melanomas with different clinical outcome. Clin Cancer Res 16, 3356-3367, doi:10.1158/1078-0432.Ccr-09-2509 (2010).
Jayawardana, K. et al. Determination of prognosis in metastatic melanoma through integration of clinico-pathologic, mutation, mRNA, microRNA, and protein information. Int J Cancer 136, 863-874, doi:10.1002/ijc.29047 (2015).
Charoentong, P. et al. Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep 18, 248-262, doi:10.1016/j.celrep.2016.12.019 (2017).
Chen, B., Khodadoust, M. S., Liu, C. L., Newman, A. M. & Alizadeh, A. A. Profiling Tumor Infiltrating Immune Cells with CIBERSORT. Methods Mol Biol 1711, 243-259, doi:10.1007/978-1-4939-7493-1_12 (2018).
Dinh, H. Q. et al. Single-cell transcriptomics identifies gene expression networks driving differentiation and tumorigenesis in the human fallopian tube. Cell Rep 35, 108978, doi:10.1016/j.celrep.2021.108978 (2021).
Sun, D. et al. TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment. Nucleic Acids Res 49, D1420-d1430, doi:10.1093/nar/gkaa1020 (2021).
Hazra, A. & Gogtay, N. Biostatistics Series Module 3: Comparing Groups: Numerical Variables. Indian journal of dermatology 61, 251-260, doi:10.4103/0019-5154.182416 (2016).
Blanche, P., Dartigues, J. F. & Jacqmin-Gadda, H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med 32, 5381-5397, doi:10.1002/sim.5958 (2013).
R. C. T. R: A language and environment for statistical computing. R Foundation for Statistical Computing.
Wilkerson, M. D. & Hayes, D. N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26, 1572-1573, doi:10.1093/bioinformatics/btq170 (2010).
Sudmeier, L. J. et al. Distinct phenotypic states and spatial distribution of CD8(+) T cell clonotypes in human brain metastases. Cell Rep Med 3, 100620, doi:10.1016/j.xcrm.2022.100620 (2022).
Burke, L. S. et al. Telomere length and the risk of cutaneous malignant melanoma in melanoma-prone families with and without CDKN2A mutations. PLoS One 8, e71121, doi:10.1371/journal.pone.0071121 (2013).
Guo, Y. et al. TERT Promoter Mutations and Telomerase in Melanoma. J Oncol 2022, 6300329, doi:10.1155/2022/6300329 (2022).
Eggensperger, S. & Tampé, R. The transporter associated with antigen processing: a key player in adaptive immunity. Biol Chem 396, 1059-1072, doi:10.1515/hsz-2014-0320 (2015).
Leonhardt, R. M., Keusekotten, K., Bekpen, C. & Knittler, M. R. Critical role for the tapasin-docking site of TAP2 in the functional integrity of the MHC class I-peptide-loading complex. J Immunol 175, 5104-5114, doi:10.4049/jimmunol.175.8.5104 (2005).
Neefjes, J. J., Momburg, F. & Hämmerling, G. J. Selective and ATP-dependent translocation of peptides by the MHC-encoded transporter. Science 261, 769-771, doi:10.1126/science.8342042 (1993).
Parcej, D. & Tampé, R. ABC proteins in antigen translocation and viral inhibition. Nat Chem Biol 6, 572-580, doi:10.1038/nchembio.410 (2010).
Agrawal, S., Reemtsma, K., Bagiella, E., Oluwole, S. F. & Braunstein, N. S. Role of TAP-1 and/or TAP-2 antigen presentation defects in tumorigenicity of mouse melanoma. Cell Immunol 228, 130-137, doi:10.1016/j.cellimm.2004.04.006 (2004).
Gao, Y., Zhao, H., Wang, P., Wang, J. & Zou, L. The roles of SOCS3 and STAT3 in bacterial infection and inflammatory diseases. Scand J Immunol 88, e12727, doi:10.1111/sji.12727 (2018).
Scutti, J. A. et al. Role of SOCS-1 Gene on Melanoma Cell Growth and Tumor Development. Transl Oncol 4, 101-109, doi:10.1593/tlo.10250 (2011).
Correia-Branco, A. et al. SLC20a1/PiT-1 is required for chorioallantoic placental morphogenesis. Vasc Biol 5, doi:10.1530/vb-22-0018 (2023).
Onaga, C. et al. High SLC20A1 Expression Is Associated With Poor Prognoses in Claudin-low and Basal-like Breast Cancers. Anticancer Res 41, 43-54, doi:10.21873/anticanres.14750 (2021).
Qian, X. et al. SLC20A1 is a prospective prognostic and therapy response predictive biomarker in head and neck squamous cell carcinoma. Aging (Albany NY) 16, 4423-4444, doi:10.18632/aging.205597 (2024).
Bahn, M. S. & Ko, Y. G. PROM1-mediated cell signal transduction in cancer stem cells and hepatocytes. BMB Rep 56, 65-70, doi:10.5483/BMBRep.2022-0203 (2023).
Monzani, E. et al. Melanoma contains CD133 and ABCG2 positive cells with enhanced tumourigenic potential. Eur J Cancer 43, 935-946, doi:10.1016/j.ejca.2007.01.017 (2007).
Rappa, G. et al. Wnt interaction and extracellular release of prominin-1/CD133 in human malignant melanoma cells. Exp Cell Res 319, 810-819, doi:10.1016/j.yexcr.2013.01.003 (2013).
Han, Y. & Wang, X. The emerging roles of KPNA2 in cancer. Life Sci 241, 117140, doi:10.1016/j.lfs.2019.117140 (2020).
Winnepenninckx, V. et al. Gene expression profiling of primary cutaneous melanoma and clinical outcome. J Natl Cancer Inst 98, 472-482, doi:10.1093/jnci/djj103 (2006).
Yang, F., Li, S., Cheng, Y., Li, J. & Han, X. Karyopherin α 2 promotes proliferation, migration and invasion through activating NF-κB/p65 signaling pathways in melanoma cells. Life Sci 252, 117611, doi:10.1016/j.lfs.2020.117611 (2020).
Shi, T., Hu, Z., Tian, L. & Yang, Y. Pan-cancer landscape of CENPO and its underlying mechanism in LUAD. Respir Res 24, 113, doi:10.1186/s12931-023-02408-3 (2023).

Table 1. Baseline clinical characteristics of CMM patients

	Melanoma samples (n=593)
Characteristics	TCGA-OV (n=418)	GSE19234 (n=43)	GSE22153 (n=55)	GSE54467 (n=77)

Age
≤65	266	22	37	55
>65	152	21	18	22
Gender
Male	260	28	30	48
Female	158	15	25	29
Stage
I	87	0	0	28
II	139	0	0	29
III	170	38	0	20
IV	22	5	55	0

No competing interests reported.

Download PDF

Reviews received at journal
03 Aug, 2024
Reviewers agreed at journal
25 Jul, 2024
Reviewers agreed at journal
23 Jul, 2024
Reviewers invited by journal
09 Jul, 2024
Editor assigned by journal
09 Jul, 2024
Editor invited by journal
24 Jun, 2024
Submission checks completed at journal
24 Jun, 2024
First submitted to journal
20 Jun, 2024

You are reading this latest preprint version

Telomere Maintenance Patterns Exhibit Distinct Prognostic and Immune Landscapes in Cutaneous Malignant Melanoma

Status:

Version 1

Abstract

Background

Materials & Methods

Results

Conclusions

Figures

1 Introduction

2 Materials and methods

2.1 Dataset acquisition

2.2 Identification of telomere maintenance patterns

2.3 Estimation of immune infiltration

2.4 Establishment of risk score

2.5 Validation of hub-genes by single-cell sequencing

2.6 Identification of prognostic nomogram

2.7 Statistical analysis

3 Results

3.1 Screening of TMGs

3.2 Construction of telomere maintenance patterns

3.3 Identification of risk score

3.4 Immune landscape between risk groups

3.5 Validation of hub-genes by single-cell sequencing

3.6 Establishment of prognostic signature

4 Discussion

5. Conclusions

Declarations

References

Table 1

Additional Declarations

Status:

Version 1