TelNet - a database for human and yeast genes involved in telomere maintenance

How telomere maintenance affects endometriosis development: a preliminary study

Background: Endometriosis results in dysmenorrhea, dyspareunia, chronic pelvic pain and infertility in reproductive-age women. However, no effective treatment methods have been applied to the disease, and the pathogenesis of endometriosis is unclear. Purpose: This study was performed to investigate the association between telomere maintenance and endometriosis development. Materials and methods: The telomere length of the postmenopausal endometria, eutopic endometria and their matched ectopic lesions in the proliferative and secretory phases was detected using fluorescence in situ hybridization (FISH) methods, and the effect of telomere length maintenance on the proliferation of endometrial cells derived from endometriotic patients was determined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay with BIBR1532 treatment. Then all of the telomere maintenance genes were extracted from the Telnet database, and bioinformatics analysis was performed to uncover the role of telomere maintenance genes in endometriosis development. Results: Telomere length was longer in endometriotic patients' eutopic endometria during the proliferative and secretory phases, and treatment with a telomerase inhibitor inhibited the proliferation of epithelial cells and stromal cells. Furthermore, the telomere maintenance genes were enriched in several hormone-related pathways, with several genes differentially expressed between normal endometria and endometria derived from endometriotic patients. The nomogram constructed based on telomere maintenance genes also displayed good predictive value. Conclusions: Telomere maintenance may contribute to the development of endometriosis, with several related genes involved.

Develop a prognostic and drug therapy efficacy prediction model for hepatocellular carcinoma based on telomere maintenance-associated genes

Background

Hepatocellular carcinoma (HCC) poses a substantial global health challenge because of its grim prognosis and limited therapeutic options. Telomere maintenance mechanisms (TMM) significantly influence cancer progression, yet their prognostic value in HCC remains largely unexamined. This research aims to establish a telomere maintenance-associated genes(TMGs)-based prognostic model using transcriptomic and clinical data to evaluate its effectiveness in predicting patient outcomes in HCC.

Methods

The identified differentially expressed genes (DEGs) were derived from the analysis of transcriptomic and clinical information sourced from the database of the Cancer Genome Atlas (TCGA) and were cross-referenced with TMGs. Candidate risk factors were initially assessed using univariate Cox regression, subsequently followed by LASSO, and then refined through multivariate Cox regression to establish a risk prediction model. This model's predictive accuracy was validated through Kaplan-Meier(K-M) survival analysis, with external validation in the Gene Expression Omnibus (GEO) dataset. Additionally, a nomogram incorporating age and tumor stage was developed. Tumor mutation burden (TMB), immune profile, and drug sensitivity in HCC were also analyzed. Furthermore, we employed RT-PCR to confirm the expression levels of the genes related to TMGs in HepG2 cell lines.

Results

A prognostic model comprising 3 core genes was constructed, with high-risk individuals showing significantly lower overall survival (OS). The association between elevated TMB and diminished survival in high-risk patients was uncovered through TMB analysis. Immune profiling indicated notable disparities in immune infiltration among these groups, with high-risk patients displaying elevated Tumor Immune Dysfunction and Exclusion (TIDE) scores, suggesting potential immune evasion.

Conclusion

In short, our prognosis model based on TMGs effectively categorized HCC patients using risk scores, enabling dependable prognostic forecasts and identification of potential therapeutic targets for personalized treatment in HCC management. Future studies should explore integrating this model into clinical practice to improve patient outcomes.

Development and validation of a prognostic and drug sensitivity model for gastric cancer utilizing telomere-related genes

BACKGROUND

Gastric cancer (GC) poses a major global health challenge because of its unfavorable prognosis. Elevated telomerase activity has been linked to the rapid growth and invasiveness of GC tumors. Investigating the expression profiles of telomerase could improve our understanding of the mechanisms underlying telomere-related GC advancement and its applicability as potential targets for diverse therapeutic strategies for GC.

METHODS

The TCGA and GEO databases were utilized to access transcriptome and clinical data related to GC. After assessing differentially expressed genes (DEGs), a prognostic risk model was developed through Cox univariate regression, LASSO-Cox regression. The prognostic risk model was validated using data from the GSE62254 cohort. The significant influence of the risk model on the tumor immune microenvironment (TIME) and its sensitivity to various drugs was assessed.

RESULTS

Differential expression analysis identified 328 significantly telomere-related DEGs in GC, with 35 of them showing a significant association with GC prognosis. A predictive risk model composed of four telomere-related genes (TRGs) was established, enabling the accurate stratification of GC patients into two distinct prognostic groups. The LASSO risk model demonstrated notable variations in immune-cell infiltration and drug sensitivity patterns between high- and low-risk groups.

CONCLUSIONS

The study establishes suggestive relationships between four TRGs (LRRN1, SNCG, GAMT, and PDE1B) and the prognosis of GC. The comprehensive characterization of the TRG model reveals their possible roles in the prognosis, TIME, and drug sensitivity in GC.

Evaluating the prognostic potential of telomerase signature in breast cancer through advanced machine learning model

Background

Breast cancer prognosis remains a significant challenge due to the disease's molecular heterogeneity and complexity. Accurate predictive models are critical for improving patient outcomes and tailoring personalized therapies.

Methods

We developed a Machine Learning-assisted Telomerase Signature (MLTS) by integrating multi-omics data from nine independent breast cancer datasets. Using multiple machine learning algorithms, we identified six telomerase-related genes significantly associated with patient survival. The predictive performance of MLTS was evaluated against 66 existing breast cancer prognostic models across diverse cohorts.

Results

The MLTS demonstrated superior predictive accuracy, stability, and reliability compared to other models. Patients with high MLTS scores exhibited increased tumor mutational burden, chromosomal instability, and poor survival outcomes. Single-cell RNA sequencing analysis further revealed higher MLTS scores in aneuploid tumor cells, suggesting a role in cancer progression. Immune profiling indicated distinct tumor microenvironment characteristics associated with MLTS scores, potentially guiding therapeutic decisions.

Conclusions

Our findings highlight the utility of MLTS as a robust prognostic biomarker for breast cancer. The ability of MLTS to predict patient outcomes and its association with key genomic and cellular features underscore its potential as a target for personalized therapy. Future research may focus on integrating MLTS with additional molecular signatures to enhance its clinical application in precision oncology.

Proteomic basis for pancreatic acinar cell carcinoma and pancreatoblastoma as similar yet distinct entities

Acinar cell carcinoma (ACC) and pancreatoblastoma (PBL) are rare pancreatic malignancies with acinar differentiation. Proteogenomic profiling of ACC and PBL revealed distinct protein expression patterns compared to pancreatic ductal adenocarcinoma (PDAC) and benign pancreas. ACC and PBL exhibited similarities, with enrichment in proteins related to RNA processing, chromosome organization, and the mitoribosome, while PDACs overexpressed proteins associated with actin-based processes, extracellular matrix, and immune-active stroma. Pathway activity differences in metabolic adaptation, epithelial-to-mesenchymal transition, and DNA repair were characterized between these diseases. PBL showed upregulation of Wnt-CTNNB1 and IGF2 pathways. Seventeen ACC-specific proteins suggested connections to metabolic diseases with mitochondrial dysfunction, while 34 PBL-specific proteins marked this pediatric cancer with an embryonic stem cell phenotype and alterations in chromosomal proteins and the cell cycle. This study provides novel insights into the proteomic landscapes of ACC and PBL, offering potential targets for diagnostic and therapeutic development.

Telomere-related prognostic signature for survival assessments in lung adenocarcinoma

Background

Telomere-related genes (TRGs) are important in many different types of cancers. However, there is a lack of research on the relationship between their expression and prognosis in lung adenocarcinoma (LUAD) patients. This study is to investigate the prognostic value of TRGs in LUAD and to develop a TRG signature that can predict patient survival.

Methods

A total of 2,086 TRGs were obtained from a database of genes involved in telomere maintenance (TelNet), while the clinical information and tumor RNA expression profiles of 513 LUAD patients were acquired from The Cancer Genome Atlas (TCGA) database. Statistical methodologies, such as least absolute shrinkage and selection operator (LASSO)-Cox, were employed to construct a prognostic model with predictive capabilities.

Results

We analyzed 1,339 telomere-associated differentially expressed genes and identified a ten-gene predictive signature for LUAD. This signature exhibited effective prognostic classification capabilities across multiple datasets, including GSE3141 (58 samples), GSE8894 (63 samples), GSE50081 (127 samples), and GSE72094 (398 samples). Furthermore, we screened tumor-sensitive drugs targeting this signature. High telomere levels were associated with reduced survival in lung cancer patients who underwent surgery. Compared to the traditional TNM (tumor node metastasis classification) grading method, our telomere-associated gene panel demonstrated superior prediction accuracy. Notably, patients in the high-risk group, defined by the telomere-associated signature, exhibited improved responses to immunotherapy, suggesting potential benefits for this subgroup of patients.

Conclusions

This study presents a comprehensive molecular signature comprising TRGs, which holds potential for functional and therapeutic investigations. Additionally, it serves as an integrated tool to identify crucial molecules for immunotherapy in lung cancer.

Identification of telomere-related lncRNAs and immunological analysis in ovarian cancer

Background

Ovarian cancer (OC) is a global malignancy characterized by metastatic invasiveness and recurrence. Long non-coding RNAs (lncRNAs) and Telomeres are closely connected with several cancers, but their potential as practical prognostic markers in OC is less well-defined.

Methods

Relevant mRNA and clinical data for OC were sourced from The Cancer Genome Atlas (TCGA) database. The telomere-related lncRNAs (TRLs) prognostic model was established by univariate/LASSO/multivariate regression analyses. The effectiveness of the TRLs model was evaluated and measured via the nomogram. Additionally, immune infiltration, tumor mutational load (TMB), and drug sensitivity were evaluated. We validated the expression levels of prognostic genes. Subsequently, PTPRD-AS1 knockdown was utilized to perform the CCK8 assay, colony formation assay, transwell assay, and wound healing assay of CAOV3 cells.

Results

A six-TRLs prognostic model (PTPRD-AS1, SPAG5-AS1, CHRM3-AS2, AC074286.1, FAM27E3, and AC018647.3) was established, which can effectively predict patient survival rates and was successfully validated using external datasets. According to the nomogram, the model could effectively predict prognosis. Furthermore, we detected the levels of regulatory T cells and M2 macrophages were comparatively higher in the high-risk TRLs group, but the levels of activated CD8 T cells and monocytes were the opposite. Finally, the low-risk group was more sensitive to anti-cancer drugs. The mRNA levels of PTPRD-AS1, SPAG5-AS1, FAM27E3, and AC018647.3 were significantly over-expressed in OC cell lines (SKOV3, A2780, CAOV3) in comparison to normal IOSE-80 cells. AC074286.1 were over-expressed in A2780 and CAOV3 cells and CHRM3-AS2 only in A2780 cells. PTPRD-AS1 knockdown decreased the proliferation, cloning, and migration of CAOV3 cells.

Conclusion

Our study identified potential biomarkers for the six-TRLs model related to the prognosis of OC.

As a novel prognostic model for breast cancer, the identification and validation of telomere-related long noncoding RNA signatures

BACKGROUND

Telomeres are a critical component of chromosome integrity and are essential to the development of cancer and cellular senescence. The regulation of breast cancer by telomere-associated lncRNAs is not fully known, though. The goals of this study were to describe predictive telomere-related LncRNAs (TRL) in breast cancer and look into any possible biological roles for these RNAs.

METHODS

We obtained RNA-seq data, pertinent clinical data, and a list of telomere-associated genes from the cancer genome atlas and telomere gene database, respectively. We subjected differentially expressed TRLs to co-expression analysis and univariate Cox analysis to identify a prognostic TRL. Using LASSO regression analysis, we built a prognostic model with 14 TRLs. The accuracy of the model's prognostic predictions was evaluated through the utilization of Kaplan-Meier (K-M) analysis as well as receiver operating characteristic (ROC) curve analysis. Additionally, immunological infiltration and immune drug prediction were done using this model. Patients with breast cancer were divided into two subgroups using cluster analysis, with the latter analyzed further for variations in response to immunotherapy, immune infiltration, and overall survival, and finally, the expression of 14-LncRNAs was validated by RT-PCR.

RESULTS

We developed a risk model for the 14-TRL, and we used ROC curves to demonstrate how accurate the model is. The model may be a standalone prognostic predictor for patients with breast cancer, according to COX regression analysis. The immune infiltration and immunotherapy results indicated that the high-risk group had a low level of PD-1 sensitivity and a high number of macrophages infiltrating. In addition, we've discovered a number of small-molecule medicines with considerable for use in treating high-risk groups. The cluster 2 subtype showed the highest immune infiltration, the highest immune checkpoint expression, and the worst prognosis among the two subtypes defined by cluster analysis, which requires more attention and treatment.

CONCLUSION

As a possible biomarker, the proposed 14-TRL signature could be utilized to evaluate clinical outcomes and treatment efficacy in breast cancer patients.

Deciphering the causal association and underlying transcriptional mechanisms between telomere length and abdominal aortic aneurysm

Background

The purpose of this study is to investigate the causal effect and potential mechanisms between telomere length and abdominal aortic aneurysm (AAA).

Methods

Summary statistics of telomere length and AAA were derived from IEU open genome-wide association studies and FinnGen R9, respectively. Bi-directional Mendelian randomization (MR) analysis was conducted to reveal the causal relationship between AAA and telomere length. Three transcriptome datasets were retrieved from the Gene Expression Omnibus database and telomere related genes was down-loaded from TelNet. The overlapping genes of AAA related differentially expressed genes (DEGs), module genes, and telomere related genes were used for further investigation. Telomere related diagnostic biomarkers of AAA were selected with machine learning algorisms and validated in datasets and murine AAA model. The correlation between biomarkers and immune infiltration landscape was established.

Results

Telomere length was found to have a suggestive negative associations with AAA [IVW, OR 95%CI = 0.558 (0.317-0.701), P < 0.0001], while AAA showed no suggestive effect on telomere length [IVW, OR 95%CI = 0.997 (0.990-1.004), P = 0.4061]. A total of 40 genes was considered as telomere related DEGs of AAA. PLCH2, PRKCQ, and SMG1 were selected as biomarkers after multiple algorithms and validation. Immune infiltration analysis and single cell mRNA analysis revealed that PLCH2 and PRKCQ were mainly expressed on T cells, while SMG1 predominantly expressed on T cells, B cells, and monocytes. Murine AAA model experiments further validated the elevated expression of biomarkers.

Conclusion

We found a suggestive effect of telomere length on AAA and revealed the potential biomarkers and immune mechanism of telomere length on AAA. This may shed new light for diagnosis and therapeutics on AAA.

Development and validation of a glioma prognostic model based on telomere-related genes and immune infiltration analysis

Background

Gliomas are the most prevalent primary brain tumors, and patients typically exhibit poor prognoses. Increasing evidence suggests that telomere maintenance mechanisms play a crucial role in glioma development. However, the prognostic value of telomere-related genes in glioma remains uncertain. This study aimed to construct a prognostic model of telomere-related genes and further elucidate the potential association between the two.

Methods

We acquired RNA-seq data for low-grade glioma (LGG) and glioblastoma (GBM), along with corresponding clinical information from The Cancer Genome Atlas (TCGA) database, and normal brain tissue data from the Genotype-Tissue Expression (GTEX) database for differential analysis. Telomere-related genes were obtained from TelNet. Initially, we conducted a differential analysis on TCGA and GTEX data to identify differentially expressed telomere-related genes, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on these genes. Subsequently, univariate Cox analysis and log-rank tests were employed to obtain prognosis-related genes. Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis were sequentially utilized to construct prognostic models. The model's robustness was demonstrated using receiver operating characteristic (ROC) curve analysis, and multivariate Cox regression of risk scores for clinical characteristics and prognostic models were calculated to assess independent prognostic factors. The aforementioned results were validated using the Chinese Glioma Genome Atlas (CGGA) dataset. Finally, the CIBERSORT algorithm analyzed differences in immune cell infiltration levels between high- and low-risk groups, and candidate genes were validated in the Human Protein Atlas (HPA) database.

Results

Differential analysis yielded 496 differentially expressed telomere-related genes. GO and KEGG pathway analyses indicated that these genes were primarily involved in telomere-related biological processes and pathways. Subsequently, a prognostic model comprising ten telomere-related genes was constructed through univariate Cox regression analysis, log-rank test, LASSO regression analysis, and multivariate Cox regression analysis. Patients were stratified into high-risk and low-risk groups based on risk scores. Kaplan-Meier (K-M) survival analysis revealed worse outcomes in the high-risk group compared to the low-risk group, and establishing that this prognostic model was a significant independent prognostic factor for glioma patients. Lastly, immune infiltration analysis was conducted, uncovering notable differences in the proportion of multiple immune cell infiltrations between high- and low-risk groups, and eight candidate genes were verified in the HPA database.

Conclusions

This study successfully constructed a prognostic model of telomere-related genes, which can more accurately predict glioma patient prognosis, offer potential targets and a theoretical basis for glioma treatment, and serve as a reference for immunotherapy through immune infiltration analysis.

Proteogenomic characterization of primary colorectal cancer and metastatic progression identifies proteome-based subtypes and signatures

Metastatic progression of colorectal adenocarcinoma (CRC) remains poorly understood and poses significant challenges for treatment. To overcome these challenges, we performed multiomics analyses of primary CRC and liver metastases. Genomic alterations, such as structural variants or copy number alterations, were enriched in oncogenes and tumor suppressor genes and increased in metastases. Unsupervised mass spectrometry-based proteomics of 135 primary and 123 metastatic CRCs uncovered distinct proteomic subtypes, three each for primary and metastatic CRCs, respectively. Integrated analyses revealed that hypoxia, stemness, and immune signatures characterize these 6 subtypes. Hypoxic CRC harbors high epithelial-to-mesenchymal transition features and metabolic adaptation. CRC with a stemness signature shows high oncogenic pathway activation and alternative telomere lengthening (ALT) phenotype, especially in metastatic lesions. Tumor microenvironment analysis shows immune evasion via modulation of major histocompatibility complex (MHC) class I/II and antigen processing pathways. This study characterizes both primary and metastatic CRCs and provides a large proteogenomics dataset of metastatic progression.

Identification of telomere-associated gene signatures to predict prognosis and drug sensitivity in glioma

OBJECTIVE

Gliomas are a heterogeneous group of brain tumors with distinct biological and clinical properties, leading to significant mortality and morbidity. Emerging evidence shows telomere maintenance has implicated in glioma susceptibility and prognosis. In this study, we comprehensively analyzed gene signatures related to telomere maintenance in glioma and their predictive values for predicting the prognosis and drug sensitivity in glioma.

METHODS

We initially identified telomere-related genes differentially expressed between low-grade glioma (LGG) and glioblastoma (GBM) and accordingly developed a risk model by univariate and multivariate Cox analysis to assess the expressions of telomere-related genes across the risk groups. Finally, to assess these genes in immune function the anti-tumor medications often used in the clinical treatment of glioma, we computed immune cell infiltration analysis and drug sensitivity analysis.

RESULTS

The consensus clustering analysis identified 20 telomere-related genes which split LGG patients into two distinct subtypes. The patient survival, the expressions of key telomere-related DEGs, and immune cell infiltration significantly differed between Cluster 1 and Cluster 2. The LASSO risk model [riskScore=(0.086)*HOXA7+(0.242)*WEE1+(0.247)*IGF2BP3+(0.052)*DUSP10] showed significant differences regarding the 1-, 3-, 5-year overall survival, immune cell infiltration, and drug sensitivity between high- and low-risk groups. The predictive nomogram constructed to quantify the survival probability of each sample at 1, 3, and 5 years was consistent with the actual patient survival.

CONCLUSION

Our comprehensive characterization of telomere-associated gene signatures in glioma reveals their possible roles in the development, tumor microenvironment, and prognosis. The study provides some suggestive relationships between four telomere-related genes (HOXA7, WEE1, IGF2BP3, and DUSP10) and glioma prognosis.

Big data analysis identified a telomere-related signature predicting the prognosis and drug sensitivity in lung adenocarcinoma

Telomeres exert a critical role in chromosome stability and aberrant regulation of telomerase may result in telomeres dysfunction and genomic instability, which are involved in the occurrence of cancers. However, limited studies have been performed to fully clarify the immune infiltration and clinical significance of telomeres-related genes (TRGs) in lung adenocarcinoma (LUAD). The number of clusters of LUAD was determined by consensus clustering analysis. The prognostic signature was constructed and verified using TCGA and GSE42127 dataset with Least Absolute Shrinkage and Selection Operator cox regression analysis. The correlation between different clusters and risk-score and drug therapy response was analyzed using TIDE and IMvigor210 dataset. Using several miRNA and lncRNA related databases, we constructed a lncRNA-miRNA-mRNA regulatory axis. We identified 2 telomeres-related clusters in LUAD, which had distinct differences in prognostic stratification, TMB score, TIDE score, immune characteristics and signal pathways and biological effects. A prognostic model was developed based on 21 TRGs, which had a better performance in risk stratification and prognosis prediction compared with other established models. TRGs-based risk score could serve as an independent risk factor for LUAD. Survival prediction nomogram was also developed to promote the clinical use of TRGs risk score. Moreover, LUAD patients with high risk score had a high TMB score, low TIDE score and IC50 value of common drugs, suggesting that high risk score group might benefit from receiving immunotherapy, chemotherapy and target therapy. We also developed a lncRNA KCNQ1QT1/miR-296-5p/PLK1 regulatory axis. Our study identified 2 telomeres-related clusters and a prognostic model in LUAD, which could be helpful for risk stratification, prognosis prediction and treatment approach selection.

A novel telomere-related genes model for predicting prognosis and treatment responsiveness in diffuse large B-cell lymphoma

Diffuse large B cell lymphoma (DLBCL) is a highly heterogeneous disease with diverse clinical and molecular features. Telomere maintenance is widely present in tumors, but there is a lack of relevant reports on the role of telomere-related genes (TRGs) in DLBCL. In this study, we used consensus clustering based on TRGs expression to identify two molecular clusters with distinct prognoses and immune cell infiltration. We developed a TRGs scoring model using univariate Cox regression and LASSO regression in the GSE10846 training cohort. DLBCL patients in the high-risk group had a worse prognosis than those in the low-risk group, as revealed by Kaplan-Meier curves. The scoring model was validated in the GSE10846 testing cohort and GSE87371 cohort, respectively. The high-risk group was characterized by elevated infiltration of activated DCs, CD56 dim natural killer cells, myeloid-derived suppressor cells, monocytes, and plasmacytoid DCs, along with reduced infiltration of activated CD4 T cells, Type 2 T helper cells, γδ T cells, NK cells, and neutrophils. Overexpression of immune checkpoints, such as PDCD1, CD274, and LAG3, was observed in the high-risk group. Furthermore, high-risk DLBCL patients exhibited increased sensitivity to bortezomib, rapamycin, AZD6244, and BMS.536924, while low-risk DLBCL patients showed sensitivity to cisplatin and ABT.263. Using RT-qPCR, we found that three protective model genes, namely TCEAL7, EPHA4, and ELOVL4, were down-regulated in DLBCL tissues compared with control tissues. In conclusion, our novel TRGs-based model has great predictive value for the prognosis of DLBCL patients and provides a promising direction for treatment optimization.

An alternative extension of telomeres related prognostic model to predict survival in lower grade glioma

OBJECTIVE

The alternative extension of the telomeres (ALT) mechanism is activated in lower grade glioma (LGG), but the role of the ALT mechanism has not been well discussed. The primary purpose was to demonstrate the significance of the ALT mechanism in prognosis estimation for LGG patients.

METHOD

Gene expression and clinical data of LGG patients were collected from the Chinese Glioma Genome Atlas (CGGA) and the Cancer Genome Atlas (TCGA) cohort, respectively. ALT-related genes obtained from the TelNet database and potential prognostic genes related to ALT were selected by LASSO regression to calculate an ALT-related risk score. Multivariate Cox regression analysis was performed to construct a prognosis signature, and a nomogram was used to represent this signature. Possible pathways of the ALT-related risk score are explored by enrichment analysis.

RESULT

The ALT-related risk score was calculated based on the LASSO regression coefficients of 22 genes and then divided into high-risk and low-risk groups according to the median. The ALT-related risk score is an independent predictor of LGG (HR and 95% CI in CGGA cohort: 5.70 (3.79, 8.58); in TCGA cohort: 1.96 (1.09, 3.54)). ROC analysis indicated that the model contained ALT-related risk score was superior to conventional clinical features (AUC: 0.818 vs 0.729) in CGGA cohorts. The results in the TCGA cohort also shown a powerful ability of ALT-related risk score (AUC: 0.766 vs 0.691). The predicted probability and actual probability of the nomogram are consistent. Enrichment analysis demonstrated that the ALT mechanism was involved in the cell cycle, DNA repair, immune processes, and others.

CONCLUSION

ALT-related risk score based on the 22-gene is an important factor in predicting the prognosis of LGG patients.

A comprehensive prognostic and immunological analysis of telomere-related lncRNAs in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is one of the most prevalent malignant tumors of the urinary system, with a high recurrence and metastasis rate. Telomeres and long non-coding RNAs (lncRNAs) have been documented playing critical roles in cancer progression. However, the prognostic significance of telomere-related lncRNA (TRLs) in KIRC is less well-defined. The Cancer Genome Atlas database was applied to retrieve the expression profiles and corresponding clinical information of KIRC patients. To create the TRLs prognostic signature, univariate Cox regression, least absolute shrinkage and selection operator analyses were performed. The prognostic signature, comprised of nine prognostic TRLs, was developed and demonstrated superior prognostic ability for KIRC patients. Additionally, the risk score acted as an independent prognostic indicator. A nomogram incorporating age, grade, stage, and signature-based risk scores was also developed and exhibited excellent predictive accuracy. Several immune activities were associated with the signature, as determined by gene function analysis. Further analysis revealed differences in the status of immunity and the tumor microenvironment between low- and high-risk groups. Notably, KIRC patients with high-risk scores were more responsive to immunotherapy and chemotherapy. To summarize, our study developed a new prognostic signature consisting of nine telomere-related lncRNA that can precisely predict the prognosis of KIRC patients. The signature was shown to be of substantial value for the tumor microenvironment and tumor mutation burden, thereby contributing to a framework for the individualized treatment of patients.

The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma

Background

Gliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.

Methods

This study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.

Results

The TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (P<0.001). This finding could be attributed to distinct tumor somatic mutations and cancer cellular signaling pathways. GO analysis indicated that the TM_low+TME_high subgroup is associated with the neuronal system and modulation of chemical synaptic transmission. Conversely, the TM_high+TME_low subgroup showed a strong association with cell cycle and DNA metabolic processes. Furthermore, the classifier significantly differentiated overall survival in the TCGA LGG cohort and served as an independent prognostic factor for LGG patients in both the TCGA cohort (P<0.001) and the CGGA cohort (P<0.001).

Conclusion

Overall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG.

A novel telomere-related gene prognostic signature for survival and drug treatment efficiency prediction in lung adenocarcinoma

OBJECTIVE

Telomere-related genes (TRGs) play a critical role in various types of tumors. However, there is a lack of comprehensive exploration of their relevance in lung cancer. This research aimed to verify the relationship between TRGs gene expression and the prognosis of patients with lung adenocarcinoma (LUAD), as well as the prediction of drug treatment efficiency.

METHODS

A total of 2093 TRGs were acquired from TelNet. The clinical information including age, tumor stage, follow up and outcome (death/survival) and TRGs expression profile of LUAD were obtained from the patients in The Cancer Genome Atlas (TCGA) database and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. The two databases were used to construct and verify a prognostic model based on the expression of hubTRGs. The tumor mutation burden, immune infiltration and subtypes, as well as IC50 prediction of multiple targeted drugs were also evaluated in TRGs-divided risk groups.

RESULTS

A total of 335 TRGs were significantly differentially expressed in LUAD as compared with normal control. Among them, 9 TRGs (ABCC2, ABCC8, ALDH2, FOXP3, GNMT, JSRP1, MACF1, PLCD3, SULT4A1) were finally identified as hubGenes and used to construct a TRG risk score. The TRG risk score showed favorable performance in constructing a prognostic nomogram in predicting survival of LUAD, and the ROC curves at 1, 3 and 5 years were plotted and the AUROC values were 0.743, 0.754 and 0.735, respectively. Higher TRGs risk score correlated with worse immune subtypes and higher tumor mutation burden in LUAD tissues. In addition, the patients in TRG high risk group harbored a lower TIDE score which indicated potentially better response to immunotherapy.

CONCLUSION

This study proposed a broad molecular signature of telomere-related genes that can be used in further functional and therapeutic investigations, and also represents an integrated modality for characterizing critical molecules when exploring novel targets for lung cancer immunotherapy.

Telomere-related prognostic biomarkers for survival assessments in pancreatic cancer

Human telomeres are linked to genetic instability and a higher risk of developing cancer. Therefore, to improve the dismal prognosis of pancreatic cancer patients, a thorough investigation of the association between telomere-related genes and pancreatic cancer is required. Combat from the R package "SVA" was performed to correct the batch effects between the TCGA-PAAD and GTEx datasets. After differentially expressed genes (DEGs) were assessed, we constructed a prognostic risk model through univariate Cox regression, LASSO-Cox regression, and multivariate Cox regression analysis. Data from the ICGC, GSE62452, GSE71729, and GSE78229 cohorts were used as test cohorts for validating the prognostic signature. The major impact of the signature on the tumor microenvironment and its response to immune checkpoint drugs was also evaluated. Finally, PAAD tissue microarrays were fabricated and immunohistochemistry was performed to explore the expression of this signature in clinical samples. After calculating 502 telomere-associated DEGs, we constructed a three-gene prognostic signature (DSG2, LDHA, and RACGAP1) that can be effectively applied to the prognostic classification of pancreatic cancer patients in multiple datasets, including TCGA, ICGC, GSE62452, GSE71729, and GSE78229 cohorts. In addition, we have screened a variety of tumor-sensitive drugs targeting this signature. Finally, we also found that protein levels of DSG2, LDHA, and RACGAP1 were upregulated in pancreatic cancer tissues compared to normal tissues by immunohistochemistry analysis. We established and validated a telomere gene-related prognostic signature for pancreatic cancer and confirmed the upregulation of DSG2, LDHA, and RACGAP1 expression in clinical samples, which may provide new ideas for individualized immunotherapy.

Construction of a telomere-related gene signature to predict prognosis and immune landscape for glioma

Background

Glioma is one of the commonest malignant tumors of the brain. However, glioma present with a poor clinical prognosis. Therefore, specific detection markers and therapeutic targets need to be explored as a way to promote the survival rate of BC patients. Therefore, we need to search for quality immune checkpoints to support the efficacy of immunotherapy for glioma.

Methods

We first recognized differentially expressed telomere-related genes (TRGs) and accordingly developed a risk model by univariate and multivariate Cox analysis. The accuracy of the model is then verified. We evaluated the variations in immune function and looked at the expression levels of immune checkpoint genes. Finally, to assess the anti-tumor medications often used in the clinical treatment of glioma, we computed the half inhibitory concentration of pharmaceuticals.

Results

We finally identified nine TRGs and built a risk model. Through the validation of the model, we found good agreement between the predicted and observed values. Then, we found 633 differentially expressed genes between various risk groups to identify the various molecular pathways between different groups. The enrichment of CD4+ T cells, CD8+ T cells, fibroblasts, endothelial cells, macrophages M0, M1, and M2, mast cells, myeloid dendritic cells, and neutrophils was favorably correlated with the risk score, but the enrichment of B cells and NK cells was negatively correlated with the risk score. The expression of several immune checkpoint-related genes differed significantly across the risk groups. Finally, in order to create individualized treatment plans for diverse individuals, we searched for numerous chemotherapeutic medications for patients in various groups.

Conclusion

The findings of this research provide evidence that TRGs may predict a patient's prognosis for glioma, assist in identifying efficient targets for glioma immunotherapy, and provide a foundation for an efficient, customized approach to treating glioma patients.

Telomere-related genes as potential biomarkers to predict endometriosis and immune response: Development of a machine learning-based risk model

Introduction

Endometriosis (EM) is an aggressive, pleomorphic, and common gynecological disease. Its clinical presentation includes abnormal menstruation, dysmenorrhea, and infertility, which seriously affect the patient's quality of life. However, the pathogenesis underlying EM and associated regulatory genes are unknown.

Methods

Telomere-related genes (TRGs) were uploaded from TelNet. RNA-sequencing (RNA-seq) data of EM patients were obtained from three datasets (GSE5108, GSE23339, and GSE25628) in the GEO database, and a random forest approach was used to identify telomere signature genes and build nomogram prediction models. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis were used to identify the pathways involved in the action of the signature genes. Finally, the CAMP database was used to screen drugs for potential use in EM treatment.

Results

Fifteen total genes were screened as EM–telomere differentially expressed genes. Further screening by machine learning obtained six genes as characteristic predictive of EM. Immuno-infiltration analysis of the telomeric genes showed that expressions including macrophages and natural killer cells were significantly higher in cluster A. Further enrichment analysis showed that the differential genes were mainly enriched in biological pathways like cell cycle and extracellular matrix. Finally, the Connective Map database was used to screen 11 potential drugs for EM treatment.

Discussion

TRGs play a crucial role in EM development, and are associated with immune infiltration and act on multiple pathways, including the cell cycle. Telomere signature genes can be valuable predictive markers for EM.

AgeAnno: a knowledgebase of single-cell annotation of aging in human

Aging is a complex process that accompanied by molecular and cellular alterations. The identification of tissue-/cell type-specific biomarkers of aging and elucidation of the detailed biological mechanisms of aging-related genes at the single-cell level can help to understand the heterogeneous aging process and design targeted anti-aging therapeutics. Here, we built AgeAnno (https://relab.xidian.edu.cn/AgeAnno/#/), a knowledgebase of single cell annotation of aging in human, aiming to provide comprehensive characterizations for aging-related genes across diverse tissue-cell types in human by using single-cell RNA and ATAC sequencing data (scRNA and scATAC). The current version of AgeAnno houses 1 678 610 cells from 28 healthy tissue samples with ages ranging from 0 to 110 years. We collected 5580 aging-related genes from previous resources and performed dynamic functional annotations of the cellular context. For the scRNA data, we performed analyses include differential gene expression, gene variation coefficient, cell communication network, transcription factor (TF) regulatory network, and immune cell proportionc. AgeAnno also provides differential chromatin accessibility analysis, motif/TF enrichment and footprint analysis, and co-accessibility peak analysis for scATAC data. AgeAnno will be a unique resource to systematically characterize aging-related genes across diverse tissue-cell types in human, and it could facilitate antiaging and aging-related disease research.

Shared and unique phosphoproteomics responses in skeletal muscle from exercise models and in hyperammonemic myotubes

Skeletal muscle generation of ammonia, an endogenous cytotoxin, is increased during exercise. Perturbations in ammonia metabolism consistently occur in chronic diseases, and may blunt beneficial skeletal muscle molecular responses and protein homeostasis with exercise. Phosphorylation of skeletal muscle proteins mediates cellular signaling responses to hyperammonemia and exercise. Comparative bioinformatics and machine learning-based analyses of published and experimentally derived phosphoproteomics data identified differentially expressed phosphoproteins that were unique and shared between hyperammonemic murine myotubes and skeletal muscle from exercise models. Enriched processes identified in both hyperammonemic myotubes and muscle from exercise models with selected experimental validation included protein kinase A (PKA), calcium signaling, mitogen-activated protein kinase (MAPK) signaling, and protein homeostasis. Our approach of feature extraction from comparative untargeted "omics" data allows for selection of preclinical models that recapitulate specific human exercise responses and potentially optimize functional capacity and skeletal muscle protein homeostasis with exercise in chronic diseases.

Telomere-related gene risk model for prognosis and drug treatment efficiency prediction in kidney cancer

Kidney cancer is one of the most common urological cancers worldwide, and kidney renal clear cell cancer (KIRC) is the major histologic subtype. Our previous study found that von-Hippel Lindau (VHL) gene mutation, the dominant reason for sporadic KIRC and hereditary kidney cancer-VHL syndrome, could affect VHL disease-related cancers development by inducing telomere shortening. However, the prognosis role of telomere-related genes in kidney cancer has not been well discussed. In this study, we obtained the telomere-related genes (TRGs) from TelNet. We obtained the clinical information and TRGs expression status of kidney cancer patients in The Cancer Genome Atlas (TCGA) database, The International Cancer Genome Consortium (ICGC) database, and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Totally 353 TRGs were differential between tumor and normal tissues in the TCGA-KIRC dataset. The total TCGA cohort was divided into discovery and validation TCGA cohorts and then using univariate cox regression, lasso regression, and multivariate cox regression method to conduct data analysis sequentially, ten TRGs (ISG15, RFC2, TRIM15, NEK6, PRKCQ, ATP1A1, ELOVL3, TUBB2B, PLCL1, NR1H3) risk model had been constructed finally. The kidney patients in the high TRGs risk group represented a worse outcome in the discovery TCGA cohort (p<0.001), and the result was validated by these four cohorts (validation TCGA cohort, total TCGA cohort, ICGC cohort, and CPTAC cohort). In addition, the TRGs risk score is an independent risk factor for kidney cancer in all these five cohorts. And the high TRGs risk group correlated with worse immune subtypes and higher tumor mutation burden in cancer tissues. In addition, the high TRGs risk group might benefit from receiving immune checkpoint inhibitors and targeted therapy agents. Moreover, the proteins NEK6, RF2, and ISG15 were upregulated in tumors both at the RNA and protein levels, while PLCL1 and PRKCQ were downregulated. The other five genes may display the contrary expression status at the RNA and protein levels. In conclusion, we have constructed a telomere-related genes risk model for predicting the outcomes of kidney cancer patients, and the model may be helpful in selecting treatment agents for kidney cancer patients.

The telomere maintenance mechanism spectrum and its dynamics in gliomas

BACKGROUND

The activation of the telomere maintenance mechanism (TMM) is one of the critical drivers of cancer cell immortality. In gliomas, TERT expression and TERT promoter mutation are considered to reliably indicate telomerase activation, while ATRX mutation and/or loss indicates an alternative lengthening of telomeres (ALT). However, these relationships have not been extensively validated in tumor tissues.

METHODS

Telomerase repeated amplification protocol (TRAP) and C-circle assays were used to profile and characterize the TMM cross-sectionally (n = 412) and temporally (n = 133) across glioma samples. WES, RNA-seq, and NanoString analyses were performed to identify and validate the genetic characteristics of the TMM groups.

RESULTS

We show through the direct measurement of telomerase activity and ALT in a large set of glioma samples that the TMM in glioma cannot be defined solely by the combination of telomerase activity and ALT, regardless of TERT expression, TERT promoter mutation, and ATRX loss. Moreover, we observed that a considerable proportion of gliomas lacked both telomerase activity and ALT. This telomerase activation-negative and ALT negative group exhibited evidence of slow growth potential. By analyzing a set of longitudinal samples from a separate cohort of glioma patients, we discovered that the TMM is not fixed and can change with glioma progression.

CONCLUSIONS

This study suggests that the TMM is dynamic and reflects the plasticity and oncogenicity of tumor cells. Direct measurement of telomerase enzyme activity and evidence of ALT should be considered when defining TMM. An accurate understanding of the TMM in glioma is expected to provide important information for establishing cancer management strategies.

Profiles of telomeric repeats in Insecta reveal diverse forms of telomeric motifs in Hymenopterans

Telomeres consist of highly conserved simple tandem telomeric repeat motif (TRM): (TTAGG)n in arthropods, (TTAGGG)n in vertebrates, and (TTTAGGG)n in most plants. TRM can be detected from chromosome-level assembly, which typically requires long-read sequencing data. To take advantage of short-read data, we developed an ultra-fast Telomeric Repeats Identification Pipeline and evaluated its performance on 91 species. With proven accuracy, we applied Telomeric Repeats Identification Pipeline in 129 insect species, using 7 Tbp of short-read sequences. We confirmed (TTAGG)n as the TRM in 19 orders, suggesting it is the ancestral form in insects. Systematic profiling in Hymenopterans revealed a diverse range of TRMs, including the canonical 5-bp TTAGG (bees, ants, and basal sawflies), three independent losses of tandem repeat form TRM (Ichneumonoids, hunting wasps, and gall-forming wasps), and most interestingly, a common 8-bp (TTATTGGG)n in Chalcid wasps with two 9-bp variants in the miniature wasp (TTACTTGGG) and fig wasps (TTATTGGGG). Our results identified extraordinary evolutionary fluidity of Hymenopteran TRMs, and rapid evolution of TRM and repeat abundance at all evolutionary scales, providing novel insights into telomere evolution.

Telomere maintenance mechanism dysregulation serves as an early predictor of adjuvant therapy response and a potential therapeutic target in human cancers

Telomere maintenance mechanisms (TMMs) rescue cells from telomere crisis, endow cells immortal property, stabilize genomic integrity. However, TMM-associated molecular profiles and their clinical outcomes in cancer remain elusive. Here, we performed a pan-cancer and integrated analysis of TMM gene expression profiles from 10,107 unique samples with clinicopathological, molecular and outcome features across 7 malignancies from the same microarray platform (Affymetrix GPL570 platform). This resource was divided into Case-Control datasets for obtaining dysregulated TMM genes and Survival datasets for evaluating clinical outcomes. Multidimensional data from The Cancer Genome Atlas (TCGA) were used to elucidate associations between TMM dysregulation and survival, genomic instability. Our results demonstrated that TMMs had a consistent dysregulation spectrum across cancers, based on which we developed the TMM-dysregulation signature TMScore that was positively associated with various tumor adverse features. Two opposite prognostic patterns of TMScore independent of clinicopathological and molecular characteristics were identified, which might be explained by genomic instability: breast and lung cancer patients with elevated TMScore had inferior outcomes, suggesting TMScore-related genes as potential therapeutic targets, on the contrary, colon and stomach cancer patients had superior outcomes. Most important, the prognostic value of TMScore was still significant regardless of whether patients had received adjuvant therapy, which was valuable for discriminating non-responders from responders, and could predict the effectiveness of adjuvant therapy. In summary, our resources delineate TMMs dysregulated landscape across cancers, shed light on the impact of TMMs dysregulation on patient outcomes and adjuvant therapy, and provide novel therapeutic opportunities for cancer treatment.

PITX1 Is a Regulator of TERT Expression in Prostate Cancer with Prognostic Power

Simple Summary

Most prostate cancer is of an indolent form and is curable. However, some prostate cancer belongs to rather aggressive subtypes leading to metastasis and death, and immediate therapy is mandatory. However, for these, the therapeutic options are highly invasive, such as radical prostatectomy, radiation or brachytherapy. Hence, a precise diagnosis of these tumor subtypes is needed, and the thus far applied diagnostic means are insufficient for this. Besides this, for their endless cell divisions, prostate cancer cells need the enzyme telomerase to elongate their telomeres (chromatin endings). In this study, we developed a gene regulatory model based on large data from transcription profiles from prostate cancer and chromatin-immuno-precipitation studies. We identified the developmental regulator PITX1 regulating telomerase. Besides observing experimental evidence of PITX1′s functional role in telomerase regulation, we also found PITX1 serving as a prognostic marker, as concluded from an analysis of more than 15,000 prostate cancer samples.

Abstract

The current risk stratification in prostate cancer (PCa) is frequently insufficient to adequately predict disease development and outcome. One hallmark of cancer is telomere maintenance. For telomere maintenance, PCa cells exclusively employ telomerase, making it essential for this cancer entity. However, TERT, the catalytic protein component of the reverse transcriptase telomerase, itself does not suit as a prognostic marker for prostate cancer as it is rather low expressed. We investigated if, instead of TERT, transcription factors regulating TERT may suit as prognostic markers. To identify transcription factors regulating TERT, we developed and applied a new gene regulatory modeling strategy to a comprehensive transcriptome dataset of 445 primary PCa. Six transcription factors were predicted as TERT regulators, and most prominently, the developmental morphogenic factor PITX1. PITX1 expression positively correlated with telomere staining intensity in PCa tumor samples. Functional assays and chromatin immune-precipitation showed that PITX1 activates TERT expression in PCa cells. Clinically, we observed that PITX1 is an excellent prognostic marker, as concluded from an analysis of more than 15,000 PCa samples. PITX1 expression in tumor samples associated with (i) increased Ki67 expression indicating increased tumor growth, (ii) a worse prognosis, and (iii) correlated with telomere length.

Identification of Key Proteins from the Alternative Lengthening of Telomeres-Associated Promyelocytic Leukemia Nuclear Bodies Pathway

Simple Summary

The alternative lengthening of telomeres is a telomere maintenance mechanism used by some cancer types to elongate their telomeres without the aid of telomerase. This mechanism contributes to the proliferation and immortality of cancer cells. One of the hallmarks of this mechanism is the interaction with promyelocytic leukemia nuclear bodies, which are suspected to be the key places where telomere extension occurs. Despite the discovery of some mechanisms, elements, key genes, and proteins from the pathway, the alternative lengthening of telomeres mechanism is still poorly understood, and it is highly associated with a poor prognosis. In this study, we combined multiomics approaches with genomic, transcriptomic, and proteomic analyses of 71 genes/proteins related to promyelocytic leukemia nuclear bodies in more than 10,000 cancer samples from The Cancer Genome Atlas Consortium. As a result, 13 key proteins were proposed as candidates for future experimental studies that will validate these proteins as therapeutic markers, which will improve the understanding and treatment of these type of cancers.

Abstract

Alternative lengthening of telomeres-associated promyelocytic leukemia nuclear bodies (APBs) are a hallmark of telomere maintenance. In the last few years, APBs have been described as the main place where telomeric extension occurs in ALT-positive cancer cell lines. A different set of proteins have been associated with APBs function, however, the molecular mechanisms behind their assembly, colocalization, and clustering of telomeres, among others, remain unclear. To improve the understanding of APBs in the ALT pathway, we integrated multiomics analyses to evaluate genomic, transcriptomic and proteomic alterations, and functional interactions of 71 APBs-related genes/proteins in 32 Pan-Cancer Atlas studies from The Cancer Genome Atlas Consortium (TCGA). As a result, we identified 13 key proteins which showed distinctive mutations, interactions, and functional enrichment patterns across all the cancer types and proposed this set of proteins as candidates for future ex vivo and in vivo analyses that will validate these proteins to improve the understanding of the ALT pathway, fill the current research gap about APBs function and their role in ALT, and be considered as potential therapeutic targets for the diagnosis and treatment of ALT-positive cancers in the future.

Immunity Depletion, Telomere Imbalance, and Cancer-Associated Metabolism Pathway Aberrations in Intestinal Mucosa upon Short-Term Caloric Restriction

Systems cancer biology analysis of calorie restriction (CR) mechanisms and pathways has not been carried out, leaving therapeutic benefits unclear. Using metadata analysis, we studied gene expression changes in normal mouse duodenum mucosa (DM) response to short-term (2-weeks) 25% CR as a biological model. Our results indicate cancer-associated genes consist of 26% of 467 CR responding differential expressed genes (DEGs). The DEGs were enriched with over-expressed cell cycle, oncogenes, and metabolic reprogramming pathways that determine tissue-specific tumorigenesis, cancer, and stem cell activation; tumor suppressors and apoptosis genes were under-expressed. DEG enrichments suggest telomeric maintenance misbalance and metabolic pathway activation playing dual (anti-cancer and pro-oncogenic) roles. The aberrant DEG profile of DM epithelial cells is found within CR-induced overexpression of Paneth cells and is coordinated significantly across GI tract tissues mucosa. Immune system genes (ISGs) consist of 37% of the total DEGs; the majority of ISGs are suppressed, including cell-autonomous immunity and tumor-immune surveillance. CR induces metabolic reprogramming, suppressing immune mechanics and activating oncogenic pathways. We introduce and argue for our network pro-oncogenic model of the mucosa multicellular tissue response to CR leading to aberrant transcription and pre-malignant states. These findings change the paradigm regarding CR's anti-cancer role, initiating specific treatment target development. This will aid future work to define critical oncogenic pathways preceding intestinal lesion development and biomarkers for earlier adenoma and colorectal cancer detection.

Telomere Maintenance Pathway Activity Analysis Enables Tissue- and Gene-Level Inferences

Telomere maintenance is one of the mechanisms ensuring indefinite divisions of cancer and stem cells. Good understanding of telomere maintenance mechanisms (TMM) is important for studying cancers and designing therapies. However, molecular factors triggering selective activation of either the telomerase dependent (TEL) or the alternative lengthening of telomeres (ALT) pathway are poorly understood. In addition, more accurate and easy-to-use methodologies are required for TMM phenotyping. In this study, we have performed literature based reconstruction of signaling pathways for the ALT and TEL TMMs. Gene expression data were used for computational assessment of TMM pathway activities and compared with experimental assays for TEL and ALT. Explicit consideration of pathway topology makes bioinformatics analysis more informative compared to computational methods based on simple summary measures of gene expression. Application to healthy human tissues showed high ALT and TEL pathway activities in testis, and identified genes and pathways that may trigger TMM activation. Our approach offers a novel option for systematic investigation of TMM activation patterns across cancers and healthy tissues for dissecting pathway-based molecular markers with diagnostic impact.

Alternative lengthening of telomeres in childhood neuroblastoma from genome to proteome

Telomere maintenance by telomerase activation or alternative lengthening of telomeres (ALT) is a major determinant of poor outcome in neuroblastoma. Here, we screen for ALT in primary and relapsed neuroblastomas (n = 760) and characterize its features using multi-omics profiling. ALT-positive tumors are molecularly distinct from other neuroblastoma subtypes and enriched in a population-based clinical sequencing study cohort for relapsed cases. They display reduced ATRX/DAXX complex abundance, due to either ATRX mutations (55%) or low protein expression. The heterochromatic histone mark H3K9me3 recognized by ATRX is enriched at the telomeres of ALT-positive tumors. Notably, we find a high frequency of telomeric repeat loci with a neuroblastoma ALT-specific hotspot on chr1q42.2 and loss of the adjacent chromosomal segment forming a neo-telomere. ALT-positive neuroblastomas proliferate slowly, which is reflected by a protracted clinical course of disease. Nevertheless, children with an ALT-positive neuroblastoma have dismal outcome.

Telomere Length Dynamics and DNA Damage Responses Associated with Long-Duration Spaceflight

Telomere length dynamics and DNA damage responses were assessed before, during, and after one-year or shorter duration missions aboard the International Space Station (ISS) in a comparatively large cohort of astronauts (n = 11). Although generally healthy individuals, astronauts tended to have significantly shorter telomeres and lower telomerase activity than age- and sex-matched ground controls before and after spaceflight. Although telomeres were longer during spaceflight irrespective of mission duration, telomere length shortened rapidly upon return to Earth, and overall astronauts had shorter telomeres after spaceflight than they did before; inter-individual differences were identified. During spaceflight, all crewmembers experienced oxidative stress, which positively correlated with telomere length dynamics. Significantly increased frequencies of chromosomal inversions were observed during and after spaceflight; changes in cell populations were also detected. We propose a telomeric adaptive response to chronic oxidative damage in extreme environments, whereby the telomerase-independent Alternative Lengthening of Telomeres (ALT) pathway is transiently activated in normal somatic cells.

Investigating the Role of Telomere and Telomerase Associated Genes and Proteins in Endometrial Cancer

Endometrial cancer (EC) is the commonest gynaecological malignancy. Current prognostic markers are inadequate to accurately predict patient survival, necessitating novel prognostic markers, to improve treatment strategies. Telomerase has a unique role within the endometrium, whilst aberrant telomerase activity is a hallmark of many cancers. The aim of the current in silico study is to investigate the role of telomere and telomerase associated genes and proteins (TTAGPs) in EC to identify potential prognostic markers and therapeutic targets. Analysis of RNA-seq data from The Cancer Genome Atlas identified differentially expressed genes (DEGs) in EC (568 TTAGPs out of 3467) and ascertained DEGs associated with histological subtypes, higher grade endometrioid tumours and late stage EC. Functional analysis demonstrated that DEGs were predominantly involved in cell cycle regulation, while the survival analysis identified 69 DEGs associated with prognosis. The protein-protein interaction network constructed facilitated the identification of hub genes, enriched transcription factor binding sites and drugs that may target the network. Thus, our in silico methods distinguished many critical genes associated with telomere maintenance that were previously unknown to contribute to EC carcinogenesis and prognosis, including NOP56, WFS1, ANAPC4 and TUBB4A. Probing the prognostic and therapeutic utility of these novel TTAGP markers will form an exciting basis for future research.

TCGA Pan-Cancer Genomic Analysis of Alternative Lengthening of Telomeres (ALT) Related Genes

Telomere maintenance mechanisms (TMM) are used by cancer cells to avoid apoptosis, 85–90% reactivate telomerase, while 10–15% use the alternative lengthening of telomeres (ALT). Due to anti-telomerase-based treatments, some tumors switch from a telomerase-dependent mechanism to ALT; in fact, the co-existence between both mechanisms has been observed in some cancers. Although different elements in the ALT pathway are uncovered, some molecular mechanisms are still poorly understood. Therefore, with the aim to identify potential molecular markers for the study of ALT, we combined in silico approaches in a 411 telomere maintenance gene set. As a consequence, we conducted a genomic analysis of these genes in 31 Pan-Cancer Atlas studies from The Cancer Genome Atlas and found 325,936 genomic alterations; from which, we identified 20 genes highly mutated in the cancer studies. Finally, we made a protein-protein interaction network and enrichment analysis to observe the main pathways of these genes and discuss their role in ALT-related processes, like homologous recombination and homology directed repair. Overall, due to the lack of understanding of the molecular mechanisms of ALT cancers, we proposed a group of genes, which after ex vivo validations, could represent new potential therapeutic markers in the study of ALT.

Changes in the Expression of Pre-Replicative Complex Genes in hTERT and ALT Pediatric Brain Tumors

Background: The up-regulation of a telomere maintenance mechanism (TMM) is a common feature of cancer cells and a hallmark of cancer. Routine methods for detecting TMMs in tumor samples are still missing, whereas telomerase targeting treatments are becoming available. In paediatric cancers, alternative lengthening of telomeres (ALT) is found in a subset of sarcomas and malignant brain tumors. ALT is a non-canonical mechanism of telomere maintenance developed by cancer cells with no-functional telomerase. Methods: To identify drivers and/or markers of ALT, we performed a differential gene expression analysis between two zebrafish models of juvenile brain tumors, that differ only for the telomere maintenance mechanism adopted by tumor cells: one is ALT while the other is telomerase-dependent. Results: Comparative analysis of gene expression identified five genes of the pre-replicative complex, ORC4, ORC6, MCM2, CDC45 and RPA3 as upregulated in ALT. We searched for a correlation between telomerase levels and expression of the pre-replicative complex genes in a cohort of paediatric brain cancers and identified a counter-correlation between telomerase expression and the genes of the pre-replicative complex. Moreover, the analysis of ALT markers in a group of 20 patients confirmed the association between ALT and increased RPA and decreased H3K9_me3 localization at telomeres. Conclusions: Our study suggests that telomere maintenance mechanisms may act as a driver of telomeric DNA replication and chromatin status in brain cancers and identifies markers of ALT that could be exploited for precise prognostic and therapeutic purposes.

Uncovering genetic mechanisms of kidney aging through transcriptomics, genomics, and epigenomics

Nephrons scar and involute during aging, increasing the risk of chronic kidney disease. Little is known, however, about genetic mechanisms of kidney aging. We sought to define the signatures of age on the renal transcriptome using 563 human kidneys. The initial discovery analysis of 260 kidney transcriptomes from the TRANScriptome of renaL humAn TissuE Study (TRANSLATE) and the Cancer Genome Atlas identified 37 age-associated genes. For 19 of those genes, the association with age was replicated in 303 kidney transcriptomes from the Nephroseq resource. Surveying 42 nonrenal tissues from the Genotype–Tissue Expression project revealed that, for approximately a fifth of the replicated genes, the association with age was kidney-specific. Seventy-three percent of the replicated genes were associated with functional or histological parameters of age-related decline in kidney health, including glomerular filtration rate, glomerulosclerosis, interstitial fibrosis, tubular atrophy, and arterial narrowing. Common genetic variants in four of the age-related genes, namely LYG1, PPP1R3C, LTF and TSPYL5, correlated with the trajectory of age-related changes in their renal expression. Integrative analysis of genomic, epigenomic, and transcriptomic information revealed that the observed age-related decline in renal TSPYL5 expression was determined both genetically and epigenetically. Thus, this study revealed robust molecular signatures of the aging kidney and new regulatory mechanisms of age-related change in the kidney transcriptome.

Genomic footprints of activated telomere maintenance mechanisms in cancer

Cancers require telomere maintenance mechanisms for unlimited replicative potential. They achieve this through TERT activation or alternative telomere lengthening associated with ATRX or DAXX loss. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we dissect whole-genome sequencing data of over 2500 matched tumor-control samples from 36 different tumor types aggregated within the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium to characterize the genomic footprints of these mechanisms. While the telomere content of tumors with ATRX or DAXX mutations (ATRX/DAXXtrunc) is increased, tumors with TERT modifications show a moderate decrease of telomere content. One quarter of all tumor samples contain somatic integrations of telomeric sequences into non-telomeric DNA. This fraction is increased to 80% prevalence in ATRX/DAXXtrunc tumors, which carry an aberrant telomere variant repeat (TVR) distribution as another genomic marker. The latter feature includes enrichment or depletion of the previously undescribed singleton TVRs TTCGGG and TTTGGG, respectively. Our systematic analysis provides new insight into the recurrent genomic alterations associated with telomere maintenance mechanisms in cancer.

Using Transcriptional Signatures to Find Cancer Drivers with LURE

Cancer genome projects have produced multidimensional datasets on thousands of samples. Yet, depending on the tumor type, 5-50% of samples have no known driving event. We introduce a semi-supervised method called Learning UnRealized Events (LURE) that uses a progressive label learning framework and minimum spanning analysis to predict cancer drivers based on their altered samples sharing a gene expression signature with the samples of a known event. We demonstrate the utility of the method on the TCGA Pan-Cancer Atlas dataset for which it produced a high-confidence result relating 59 new connections to 18 known mutation events including alterations in the same gene, family, and pathway. We give examples of predicted drivers involved in TP53, telomere maintenance, and MAPK/RTK signaling pathways. LURE identifies connections between genes with no known prior relationship, some of which may offer clues for targeting specific forms of cancer. Code and Supplemental Material are available on the LURE website: https://sysbiowiki.soe.ucsc.edu/lure.

Integrative genomic and transcriptomic analysis of leiomyosarcoma

Leiomyosarcoma (LMS) is an aggressive mesenchymal malignancy with few therapeutic options. The mechanisms underlying LMS development, including clinically actionable genetic vulnerabilities, are largely unknown. Here we show, using whole-exome and transcriptome sequencing, that LMS tumors are characterized by substantial mutational heterogeneity, near-universal inactivation of TP53 and RB1, widespread DNA copy number alterations including chromothripsis, and frequent whole-genome duplication. Furthermore, we detect alternative telomere lengthening in 78% of cases and identify recurrent alterations in telomere maintenance genes such as ATRX, RBL2, and SP100, providing insight into the genetic basis of this mechanism. Finally, most tumors display hallmarks of "BRCAness", including alterations in homologous recombination DNA repair genes, multiple structural rearrangements, and enrichment of specific mutational signatures, and cultured LMS cells are sensitive towards olaparib and cisplatin. This comprehensive study of LMS genomics has uncovered key biological features that may inform future experimental research and enable the design of novel therapies.