Pan-cancer analysis identifies telomerase-associated signatures and cancer subtypes

Male breast cancer differs from female breast cancer in molecular features that affect prognoses and drug responses

BACKGROUND

Male breast cancer (MBC) is a rare malignancy with a worse prognosis than female breast cancer (FBC). Current MBC treatment strategies are based on those for FBC. However, molecular differences between MBC and FBC with respect to prognosis and drug responses remain unclear.

METHODS

After controlling for confounding factors with propensity score matching (PSM), differences between MBC and FBC were comprehensively analyzed using many types of data: survival, immune microenvironments, sex hormone responses, drug sensitivity, transcriptomes, genomes, epigenomes, and proteomes.

RESULTS

Overall survival (OS) and cancer-specific survival (CSS) were both worse for MBC than for FBC. Differentially expressed mRNAs were enriched in numerous cancer-related functions and pathways, with SPAG16 and STOX1 being as the most important prognosis-related mRNAs for MBC. Competing endogenous RNA (ceRNA) and transcription factor (TF)-mRNA regulatory networks contain potential prognostic genes. Nine genes had higher mutation frequencies in MBC than in FBC. MBC shows a comparatively poor response to immunotherapy, with five proteins that promote breast cancer progression being highly expressed in MBC. MBC may be more responsive than FBC to estrogen. We detected six United States Food and Drug Administration (FDA)-approved therapeutic target genes as being differentially expressed between MBC and FBC.

CONCLUSION

The poor prognosis of MBC compared to FBC is due to numerous molecular differences and resulting drug responses.

Advances in prognostic models for osteosarcoma risk

The risk prognosis model is a statistical model that uses a set of features to predict whether an individual will develop a specific disease or clinical outcome. It can be used in clinical practice to stratify disease severity and assess risk or prognosis. With the advancement of large-scale second-generation sequencing technology, along Prognosis models for osteosarcoma are increasingly being developed as large-scale second-generation sequencing technology advances and clinical and biological data becomes more abundant. This expansion greatly increases the number of prognostic models and candidate genes suitable for clinical use. This article will present the predictive effects and reliability of various prognosis models, serving as a reference for their evaluation and application.

A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology

Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact:  anirban@klyuniv.ac.in.

Emerging roles of circular RNAs in regulating the hallmarks of thyroid cancer

Thyroid cancer is a prevalent endocrine malignancy with increasing incidence in recent years. Although most thyroid cancers grow slowly, they can become refractory, leading to a high mortality rate once they exhibit recurrence, metastasis, resistance to radioiodine therapy, or a lack of differentiation. However, the mechanisms underlying these malignant characteristics remain unclear. Circular RNAs, a type of closed-loop non-coding RNAs, play multiple roles in cancer. Several studies have demonstrated that circular RNAs significantly influence the development of thyroid cancers. In this review, we summarize the circular RNAs identified in thyroid cancers over the past decade according to the hallmarks of cancer. We found that eight of the 14 hallmarks of thyroid cancers are regulated by circular RNAs, whereas the other six have not been reported to be correlated with circular RNAs. This review is expected to help us better understand the roles of circular RNAs in thyroid cancers and accelerate research on the mechanisms and cure strategies for thyroid cancers.

Unlocking longevity: the role of telomeres and its targeting interventions

Average life expectancy has been steadily increasing in developed countries worldwide. These demographic changes are associated with an ever-growing social and economic strain to healthcare systems as well as society. The aging process typically manifests as a decline in physiological and cognitive functions, accompanied by a rise in chronic diseases. Consequently, strategies that both mitigate age-related diseases and promote healthy aging are urgently needed. Telomere attrition, characterized by the shortening of telomeres with each cell division, paradoxically serves as both a protective mechanism and a contributor to tissue degeneration and age-related ailments. Based on the essential role of telomere biology in aging, research efforts aim to develop approaches designed to counteract telomere attrition, aiming to delay or reduce age-related diseases. In this review, telomere biology and its role in aging and age-related diseases is summarized along with recent approaches to interfere with telomere shortening aiming at well- and healthy-aging as well as longevity. As aging research enters a new era, this review emphasizes telomere-targeting therapeutics, including telomerase activators and tankyrase inhibitors, while also exploring the effects of antioxidative and anti-inflammatory agents, along with indirectly related approaches like statins.

Ginsenosides and Tumors: A Comprehensive and Visualized Analysis of Research Hotspots and Antitumor Mechanisms

Background: Ginsenoside, the main active constituent of traditional Chinese medicine Ginseng, has been shown to play an important role in the prevention and treatment of cancer. However, the literature as well as the antitumor mechanisms of ginsenosides has not yet been systematically studied. Methods: We screened all relevant literature on ginsenosides and tumors from Web of Science during 2001-2021 and analyzed the extracted terms of these publications by VOSviewer and CiteSpace. DAVID online tool was used to perform Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathways analysis of ginsenoside-related genes. Cytoscape and String software were used to construct the interaction networks of ginsenoside-related genes and corresponding proteins. Results: A total of 919 publications were included in the study. A total of 122 identified keywords were mainly divided into 3 clusters: "pharmacological function research", "functional validation in animal models" and "anti-tumor efficacy and mechanism". The keywords of "oxidative stress" had the strongest citation burst in the past 5 years. A total of 50 genes were identified as ginsenoside-related genes in tumors. They have the function of regulating gene expression and apoptosis, and they are closely related to signaling pathways in cancers. Ginsenoside-related genes form a complex interactional network, in which TP53 and IL-6 are centrally located. Conclusions: We explored and revealed research hotspots related to the ginsenosides and tumors. More precise anti-tumor mechanism research will be promising in the future. TP53 and IL-6 may be the key points to comprehending the anti-tumor mechanism of ginsenosides.

An integrative analysis of GEO data to identify possible therapeutic biomarkers of prostate cancer and targeting potential protein through Zea mays phytochemicals by virtual screening approaches

Prostate cancer (PC) is a prevalent type of cancer among men. Delaying the treatment of patients with upgraded or upstaged cancer may lead to unmanageable circumstances. The aim of this study is to contribute to the finding of biomarkers that are specific to PC and identify drug candidates derived from plants. The information about cancer is critical for clinicians to make decisions about patient treatment in the era of precision medicine. Advances in genomics technology have opened up new possibilities for identifying genes that are associated with cancer, including PC. This study identifies novel differentially expressed genes for PC. The seven PC microarray datasets were selected from the National Center for Biotechnology Information (NCBI)/Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) were found based on a fold change of |logFC| ≥ 1 and an adjusted p-value of <0.05. The DEGs were further studied using several bioinformatics tools, including STRING, CytoHubba, SRplot, Coremine Medical database, FunRich and GeneMANIA, cBioPortal. The six new potential biomarkers, GAGE2A, GAGE12G, GAGE2E, GAGE13, GAGE12F and CSAG1 were identified. These biomarkers are associated with biological processes (BPs) such as cell division, and gene expression regulation, so these genes may have a crucial role in PC progression and may serve as potential biomarkers for PC. A total of 497 phytochemicals from corn plants have been screened against the target protein and found LTS0176591 as the best lead molecule with docking score of -6.31 kcal/mol. Further, molecular mechanics-generalized born surface area (MM-GBSA), molecular dynamics simulation, principal component analysis (PCA), free energy landscape (FEL) and molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) were carried out to validate the findings.Communicated by Ramaswamy H. Sarma.

Reconstructing the immunosenescence core pathway reveals global characteristics in pan-cancer

Immunosenescence has been demonstrated to play an important role in tumor progression. However, there is lacking comprehensive analyses of immunosenescence-related pathways. Meanwhile, the sex disparities of immunosenescence in cancer are still poorly understood. In this study, we analyzed the multi-omics data of 12,836 tumor samples, including genomics, transcriptomics, epigenomics, proteomics, and metabolomics. We systematically identified immunosenescence pathways that were disordered across cancer types. The mutations and copy number variations of immunosenescence pathways were found to be more active in pan-cancer. We reconstructed the immunosenescence core pathways (ISC-pathways) to improve the ability of prognostic stratification in 33 cancer types. We also found the head and neck squamous carcinoma (HNSC) contained abundant sex-specific immunosenescence features and showed sex differences in survival. We found that OSI-027 was a potential sex-specific drug in HNSC tumors, which tended to be more effective in male HNSC by targeting the MTOR gene in the PI3K-Akt signaling pathway. In conclusion, our study provided a systematic understanding of immunosenescence pathways and revealed the global characteristics of immunosenescence in pan-cancer. We highlighted MTOR gene could be a powerful immunosenescence biomarker of HNSC that helps to develop sex-specific immunosenescence drugs.

The potential bioactive ingredients and hub genes of five TCM prescriptions against lung adenocarcinoma were explored based on bioinformatics

Analysis of the commonness of several prescriptions of traditional Chinese medicine (TCM) in the treatment of lung adenocarcinoma (LUAD) based on bioinformatics. Searched the TCM prescriptions for the treatment of LUAD in the literature published in the database, searched ingredients in the TCM through TCMSP and Swiss target prediction databases (OB ≥ 30%, DL > 0.18, Caco-2 > 0), and predicted the potential targets. GEO database retrieved LUAD gene chip data and screened (P < 0.05, | log2 (fold change) |> 1). The biological function, hub gene selection and survival period, immune infiltration, methylation, copy number variations (CNVs), and single-nucleotide variants (SNV) of hub genes were analyzed by DAVID, STRING, Kaplan-Meier plotter database, Cytoscape software, GSCALite database, and TIMER2.0. In this study, 5 TCM prescriptions were analyzed, and a total of 173 ingredients were obtained through database search, including 35 coincidence ingredients, a total of 603 potential targets, 621 LUAD-related genes, 16 up-regulated genes, and 31 down-regulated genes. A total of 61 terms of biological process (BP), 14 terms of cellular component (CC), and 14 terms of molecular function (MF) were obtained. Twenty core genes were obtained, including 15 genes with different survival periods, which were closely related to immune cells (B cell, CD8 + T cell, CD4 + T cell, macrophage, neutrophil, and dendritic cells). The low expression of ADRB2 and MAOA and the high expression of AUARK, CDK1, KIF11, MIF, TOP2A, and TTK were associated with the survival rate of LUAD patients (P < 0.05). Baicalein, Arachidonate, Hederagenin, and hub genes may become potential drugs and potential targets for LUAD treatment. Evaluated the efficacy of TCM in the treatment of LUAD from macro to micro, mined the hub genes, and predicted the mechanism of action, so as to lay the foundation for the development of new drugs of TCM, prescription optimization, or disease control.

GEMIN4, a potential therapeutic targets for patients with basal-like subtype breast cancer

BACKGROUND

Basal-like breast cancer (BLBC) takes up about 10-20% of all breast cancer(BC), what's more, BLBC has the lowest survival rate among all BC subtypes because of lacks of efficient treatment methods. We aimed to explore the molecules that can be used as diagnostic maker for BLBC at early stage and provide optimized treatment strategies for BLBC patients in this study.

METHODS

Apply weighted gene co-expression network analysis (WGCNA) to identify gene modules related to BLBC;The functional enrichment of candidate genes related to BLBC in the red module of Go data package and KEGG analysis;Overlapping cross analysis of URGs and WGCNA to identify candidate genes in each BC subtype;Divide BCBL patients into high-risk and low-risk groups, and analyze the two groups of overall survival (OS) and relapse free survival (RFS);Screening of GEMIN4 dependent cell lines; QRT PCR was used to verify the expression of GEMIN4 transfected with siRNA; CCK8 was used to determine the effect of GEMIN4 on cell viability; Positive cell count detected by BrdU staining;GO and KEGG enrichment analysis of GEMIN4.

RESULTS

The "red module" has the highest correlation with BLBC, with 913 promising candidate genes identified from the red module;913 red module candidate genes related to BLBC participated in multiple GO terms, and KEGG enrichment analysis results mainly enriched in estrogen signaling pathways and pathways in cancer;There are 386 overlapping candidate genes among the 913 "red module" genes identified by 1893 common URG and WGCNA;In BLBC patients, 9 highly expressed genes are associated with OS. Five highly expressed genes are associated with RFS. Kaplan Meier survival analysis suggests that high GEMIN4 expression levels are associated with poor prognosis in BLBC patients;The GEMIN4 gene dependency score in HCC1143 and CAL120 cell lines is negative and low; Si-GEMIN4-1 can significantly reduce the mRNA expression of GEMIN4; Si-GEMIN4 can inhibit cell viability; Si-GEMIN4 can reduce the number of positive cells;GO enrichment analysis showed that GEMIN4 is associated with DNA metabolism processes and adenylate binding; KEGG pathway enrichment analysis shows that GEMIN4 is related to ribosome biogenesis in eukaryotes.

CONCLUSION

We hypothesized that GEMIN4 may be the potential target for the treatment of BLBC.

AhRR and PPP1R3C: Potential Prognostic Biomarkers for Serous Ovarian Cancer

The lack of effective screening and successful treatment contributes to high ovarian cancer mortality, making it the second most common cause of gynecologic cancer death. Development of chemoresistance in up to 75% of patients is the cause of a poor treatment response and reduced survival. Therefore, identifying potential and effective biomarkers for its diagnosis and prognosis is a strong critical need. Copy number alterations are frequent in cancer, and relevant for molecular tumor stratification and patients' prognoses. In this study, array-CGH analysis was performed in three cell lines and derived cancer stem cells (CSCs) to identify genes potentially predictive for ovarian cancer patients' prognoses. Bioinformatic analyses of genes involved in copy number gains revealed that AhRR and PPP1R3C expression negatively correlated with ovarian cancer patients' overall and progression-free survival. These results, together with a significant association between AhRR and PPP1R3C expression and ovarian cancer stemness markers, suggested their potential role in CSCs. Furthermore, AhRR and PPP1R3C's increased expression was maintained in some CSC subpopulations, reinforcing their potential role in ovarian cancer. In conclusion, we reported for the first time, to the best of our knowledge, a prognostic role of AhRR and PPP1R3C expression in serous ovarian cancer.

BIBR1532 Affects Endometrial Cell Proliferation, Migration, and Invasion in Endometriosis via Telomerase Inhibition and MAPK Signaling

OBJECTIVES

The effect of telomerase inhibitor BIBR1532 on endometriotic cells was investigated to explore the inhibitory effect of targeting telomerase on endometriosis.

DESIGN

In vitro primary cell culture study. Participants/Materials: Primary endometrial cells derived from eutopic and ectopic endometrium in patients with endometriosis.

SETTING

The study was conducted in the university hospital.

METHODS

Paired eutopic and ectopic endometrial cells were collected from 6 patients from January 2018 to July 2021. A TRAP assay was performed to detect the telomerase activity of the cells. MTT, cell cycle, apoptosis, migration, and invasion assays were performed to study the inhibitory effect of BIBR1532. Enrichment analysis was performed to identify the key pathways involved in endometriosis progression and telomerase action. Then, Western blotting was used to investigate the expression of related proteins.

RESULTS

BIBR1532 treatment significantly inhibited the growth of eutopic and ectopic endometrial cells, with apoptosis and cell cycle signaling involved. Migration and invasion, important characteristics for the establishment of ectopic lesions, were also inhibited by BIBR1532. The MAPK signaling cascade, related to telomerase and endometriosis, was decreased in eutopic and ectopic endometrial stromal cells with the treatment of BIBR1532.

LIMITATIONS

The severe side effects of telomerase inhibitors might be the main obstacle to clinical application, so it is necessary to find better drug delivery methods in vivo.

CONCLUSIONS

The telomerase inhibitor BIBR1532 affects endometrial cell proliferation, migration, and invasion in endometriosis.

Cancer-associated fibroblasts refine the classifications of gastric cancer with distinct prognosis and tumor microenvironment characteristics

Background

Cancer-associated fibroblasts (CAFs) are essential tumoral components of gastric cancer (GC), contributing to the development, therapeutic resistance and immune-suppressive tumor microenvironment (TME) of GC. This study aimed to explore the factors related to matrix CAFs and establish a CAF model to evaluate the prognosis and therapeutic effect of GC.

Methods

Sample information from the multiply public databases were retrieved. Weighted gene co-expression network analysis was used to identify CAF-related genes. EPIC algorithm was used to construct and verify the model. Machine-learning methods characterized CAF risk. Gene set enrichment analysis was employed to elucidate the underlying mechanism of CAF in the development of GC.

Results

A three-gene (GLT8D2, SPARC and VCAN) prognostic CAF model was established, and patients were markedly divided according to the riskscore of CAF model. The high-risk CAF clusters had significantly worse prognoses and less significant responses to immunotherapy than the low-risk group. Additionally, the CAF risk score was positively associated with CAF infiltration in GC. Moreover, the expression of the three model biomarkers were significantly associated with the CAF infiltration. GSEA revealed significant enrichment of cell adhesion molecules, extracellular matrix receptors and focal adhesions in patients at a high risk of CAF.

Conclusion

The CAF signature refines the classifications of GC with distinct prognosis and clinicopathological indicators. The three-gene model could effectively aid in determining the prognosis, drug resistance and immunotherapy efficacy of GC. Thus, this model has promising clinical significance for guiding precise GC anti-CAF therapy combined with immunotherapy.

Pan-Cancer analysis of TERT and Validation in Osteosarcoma Cell Lines

PURPOSE

To investigate the possible functions of TERT in pan-cancer and OS.

METHODS

First, differential TERT gene expression analysis was conducted using multi-omics data integrative analyses, including differential expression, prognosis, the correlation between infiltrating inflammatory immune cells, and mutation in pan-cancer. Furthermore, differential TERT gene expression analysis was conducted using mRNA expression profiles related to OS based on the GEO Datasets. Various differentially expressed genes were chosen based on a fitness threshold for further investigations. Finally, the function of the TERT gene was assessed in OS cells, including cellular proliferation, migration, and metastasis.

RESULTS

Pan-cancer research demonstrated that variable expression of TERT was not only associated with numerous types of human cancer but was also intimately linked to DNA methylation. Bioinformatic investigation revealed a link between the differential expression of TERT with immune cell infiltration in the tumor microenvironment (TME). In vitro studies indicated that inhibition of TERT decreased OS cell proliferation, motility, and metastasis.

CONCLUSION

TERT may serve as a useful genomic biomarker for the diagnosis and prediction of pan-cancer and as a prospective therapeutic target for the treatment of OS.

Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning

Cancer is one of the leading causes of death worldwide and can be caused by environmental aspects (for example, exposure to asbestos), by human behavior (such as smoking), or by genetic factors. To understand which genes might be involved in patients’ survival, researchers have invented prognostic genetic signatures: lists of genes that can be used in scientific analyses to predict if a patient will survive or not. In this study, we joined together five different prognostic signatures, each of them related to a specific cancer type, to generate a unique pan-cancer prognostic signature, that contains 207 unique probesets related to 187 unique gene symbols, with one particular probeset present in two cancer type-specific signatures (203072_at related to the MYO1E gene). We applied our proposed pan-cancer signature with the Random Forests machine learning method to 57 microarray gene expression datasets of 12 different cancer types, and analyzed the results. We also compared the performance of our pan-cancer signature with the performances of two alternative prognostic signatures, and with the performances of each cancer type-specific signature on their corresponding cancer type-specific datasets. Our results confirmed the effectiveness of our prognostic pan-cancer signature. Moreover, we performed a pathway enrichment analysis, which indicated an association between the signature genes and a protein-protein interaction analysis, that highlighted PIK3R2 and FN1 as key genes having a fundamental relevance in our signature, suggesting an important role in pan-cancer prognosis for both of them.

Genomic, epigenomic, and transcriptomic signatures for telomerase complex components: a pan-cancer analysis

Telomerase activation is required for malignant transformation. Recent advances in high-throughput technologies have enabled the generation of complex datasets, thus providing alternative approaches to exploring telomerase biology more comprehensively, which has proven to be challenging due to the need for laborious assays required to test for telomerase activity. To solve these issues, several groups have analyzed TCGA pan-cancer tumor datasets by investigating telomerase reverse transcriptase (TERT), the catalytic subunit for telomerase activity, or its surrogates. However, telomerase is a multiunit complex containing not only TERT, but also numerus cofactors required for telomerase function. Here we determined genomic and molecular alterations of 10 well-characterized telomerase components in the TCGA and CCLE datasets. We calculated a telomerase score (TS) based on their expression profiles and clustered tumors into low, high, and intermediate subtypes. To validate the in silico analysis result, we used immunoblotting and telomerase assays. High TS subtypes were significantly associated with stemness, proliferation, epithelial to mesenchymal transition, hyperactivation of oncogenic signaling pathways, shorter patient survival, and infiltration of dysfunctional T-cells or poor response to immunotherapy. Copy number alterations in 10 telomerase components were widespread and associated with the level of their expression. Surprisingly, primary tumors and cancer cell lines frequently displayed a homozygous deletion of the TCAB1 gene, encoding a telomerase protein essential for telomerase trafficking, assembling, and function, as previously reported. However, tumors or cells carrying a TCAB1 deletion still exhibited telomerase activity comparable to or even higher than their wildtype counterparts. Collectively, applying telomerase component-based TS in complex datasets provided a robust tool for telomerase analyses. Our findings also reveal a tight connection between telomerase and other oncogenic signaling pathways; TCAB1 may acts as a dispensable telomerase component. Moreover, TS may serve as a useful biomarker to predict patient outcomes and response to immunotherapy.

Identification of microsatellite instability and immune-related prognostic biomarkers in colon adenocarcinoma

Background

Colon adenocarcinoma (COAD) is a prevalent malignancy that causes significant mortality. Microsatellite instability plays a pivotal function in COAD development and immunotherapy resistance. However, the detailed underlying mechanism requires further investigation. Consequently, identifying molecular biomarkers with prognostic significance and revealing the role of MSI in COAD is important for addressing key obstacles in the available treatments.

Methods

CIBERSORT and ESTIMATE analyses were performed to evaluate immune infiltration in COAD samples, followed by correlation analysis for MSI and immune infiltration. Then, differentially expressed genes (DEGs) in MSI and microsatellite stability (MSS) samples were identified and subjected to weighted gene co-expression network analysis (WGCNA). A prognostic model was established with univariate cox regression and LASSO analyses, then evaluated with Kaplan-Meier analysis. The correlation between the prognostic model and immune checkpoint inhibitor (ICI) response was also analyzed.

Results

In total, 701 significant DEGs related to MSI status were identified, and WGCNA revealed two modules associated with the immune score. Then, a seven-gene prognostic model was constructed using LASSO and univariate cox regression analyses to predict survival and ICI response. The high-risk score patients in TCGA and GEO cohorts presented a poor prognosis, as well as a high immune checkpoint expression, so they are more likely to benefit from ICI treatment.

Conclusion

The seven-gene prognostic model constructed could predict the survival of COAD and ICI response and serve as a reference for immunotherapy decisions.

Relationship between miRNAs polymorphisms and peripheral blood leukocyte DNA telomere length in coke oven workers: A cross-sectional study

OBJECTIVE

The purpose of this study was to investigate the factors affecting telomere length (TL) in coke oven workers by analyzing the interaction between miRNAs polymorphisms and coke oven emissions (COEs) exposure.

METHODS

A total of 544 coke oven workers and 238 healthy controls were recruited. Peripheral blood was collected from the subjects, genomic DNA was extracted, leukocyte TL was detected by real-time quantitative polymerase chain reaction, and fifteen polymorphisms of eight miRNAs were genotyped by flight mass spectrometry.

RESULTS

Statistical analysis showed that the peripheral blood DNA TL in the exposure group was shorter than that in the control group (P < 0.001). Generalized linear model found that COEs-exposure [β (95%CI) = -0.427 (-0.556, -0.299), P < 0.001], genotype CC+CT for miR-612 rs1144925 [β (95%CI) = -0.367 (-0.630, -0.104), P = 0.006], and the interaction of miR-181B1 rs12039395 TT genotype and COEs-exposure [β (95% CI) = 0.564 (0.108, 1.020), P = 0.015] were associated with the shortened TL.

CONCLUSION

COEs-exposure and miR-612 rs1144925 TT could promote telomere shortening in coke oven workers. The interaction of miR-181B1 rs12039395 TT genotype and COEs-exposure could protect telomere. This provides clues for further mechanistic studies between miRNA and telomere damage.

Protective effect of tertiary lymphoid structures against hepatocellular carcinoma: New findings from a genetic perspective

Background

Tertiary lymphoid structures (TLS) have an effect on hepatocellular carcinoma (HCC), but the underlying mechanism remains to be elucidated.

Methods

Intratumoral TLS (iTLS) was classified in the Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort using pathological sections from the Cancer Digital Slide Archive. Univariate and multivariate Cox regression analyses were performed to validate the effect of iTLS on overall survival (OS), relapse-free survival (RFS), and disease-free survival (DFS). The genes differentially expressed between the iTLS-negative and iTLS-positive groups were analyzed in combination with sequencing data. Gene set enrichment analysis (GSEA) was used to explore the signaling pathways affected by these differentially expressed genes. The random forest algorithm was used to identify genes with the highest correlation with the iTLS in the training set. Multivariate logistic regression was used to build a model to predict iTLS in tissue samples. Spearman’s correlation was used to analyze the relationship between TLS-associated chemokines and signature genes, and CIBERSORT was used to calculate immune infiltration scores. Copy number variation and its relationship with immune cell infiltration and signature genes were assessed using the gene set cancer analysis (GSCA). The Correlation R package was used for gene ontology (GO), disease ontology (DO), and gene mutation analyses. The GSCA was used for drug sensitivity analysis. LASSO regression was used to build prognostic models, and external data were used to validate the models.

Results

There were 218 positive and 146 negative samples for iTLS. iTLS was significantly associated with better RFS and DFS according to Cox regression analysis. Twenty signature genes that were highly associated with iTLS positivity were identified. GO and mutation analyses revealed that the signature genes were associated with immunity. Most signature genes were sensitive to immune checkpoint inhibitors. Risk scores calculated using a characteristic gene-based prognostic model were found to be an independent prognostic factor for OS.

Conclusions

The improvement of RFS in HCC by iTLS was not limited to the early period as previously reported. iTLS improved DFS in patients. Characteristic genes are closely related to the formation of iTLS and TLS chemokines in HCC. These genes are closely related to immunity in terms of cellular infiltration, biological functions, and signaling pathways. Most are sensitive to immune checkpoint inhibitors, and their expression levels can affect prognosis.

AHNAK2 is a biomarker and a potential therapeutic target of adenocarcinomas

Adenocarcinoma is the second largest histological type of cervical cancer, second only to cervical squamous cell carcinoma. At present, despite the clinical treatment strategies of cervical adenocarcinoma and cervical squamous cell carcinoma being similar, the outcome and prognosis of cervical adenocarcinoma are significantly poor. Therefore, it is urgent to find specific biomarker and therapeutic target for cervical adenocarcinoma. In this study, we aim to reveal and verify the potential biomarkers and therapeutic targets of cervical adenocarcinoma. Weighted correlation network analysis (WGCNA) reveals the differentially-expressed genes significantly related to the histological characteristics of the two cervical cancer subtypes. We select the genes with the top 20 significance for further investigation. Through microarray and immunohistochemical (IHC) analyses of a variety of tumor tissues, we find that among these 20 genes, AHNAK2 is highly expressed not only in cervical adenocarcinoma, but also in multiple of adenocarcinoma tissues, including esophagus, breast and colon, while not in normal gland tissues. In vitro, AHNAK2 knockdown significantly inhibits cell proliferation and migration of adenocarcinoma cell lines. In vivo, AHNAK2 knockdown significantly inhibits tumor progression and metastasis of various adenocarcinomas. RNA-sequencing and bioinformatics analyses suggest that the inhibitory effect of AHNAK2 knockdown on tumor progression is achieved by regulating DNA replication and upregulating Bim expression. Together, we demonstrate that AHNAK2 is a biomarker and a potential therapeutic target for adenocarcinomas.

Bioinformatic Analysis and In Vitro and In Vivo Experiments Reveal That Fibrillarin Participates in the Promotion of Lung Metastasis in Hepatocellular Carcinoma

Lung metastasis, the most frequent metastatic pattern in hepatocellular carcinoma, is an important contributor to poor prognosis. However, the mechanisms responsible for lung metastasis in hepatocellular carcinoma remain unknown. Aiming to explore these mechanisms, weighted gene coexpression network analysis (WGCNA) was firstly used to find hub genes related to lung metastasis. Then, we obtained 67 genes related to lung metastasis in hepatocellular carcinoma which were mainly related to ribosomal pathways and functions, and a protein interaction network analysis identified that fibrillarin (FBL) might be an important hub gene. Furthermore, we found that FBL is highly expressed in hepatocellular carcinoma and that its high expression increases the rate of lung metastasis and indicates a poor prognosis. Knockdown of FBL could significantly reduce proliferation and stemness as well as inhibiting the migration and invasion of hepatocellular carcinoma cells. Moreover, we found that FBL might be involved in the regulation of MYC and E2F pathways in hepatocellular carcinoma. Finally, we demonstrated that the knockdown of FBL could suppress hepatocellular carcinoma cell growth in vivo. In conclusion, ribosome-biogenesis-related proteins, especially Fibrillarin, play important roles in lung metastasis from hepatocellular carcinoma.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01095-x.

MiRNA sequencing of Embryonic Myogenesis in Chengkou Mountain Chicken

BACKGROUND

Skeletal muscle tissue is among the largest organ systems in mammals, essential for survival and movement. Embryonic muscle development determines the quantity and quality of muscles after the birth of an individual. MicroRNAs (miRNAs) are a significant class of non-coding RNAs that bind to the 3'UTR region of mRNA to regulate gene function. Total RNA was extracted from the leg muscles of chicken embryos in different developmental stages of Chengkou Mountain Chicken and used to generate 171,407,341 clean small RNA reads. Target prediction, GO, and KEGG enrichment analyses determined the significantly enriched genes and pathways. Differential analysis determined the significantly different miRNAs between chicken embryo leg muscles at different developmental stages. Meanwhile, the weighted correlation network analysis (WGCNA) identified key modules in different developmental stages, and the hub miRNAs were screened following the KME value.

RESULTS

The clean reads contained 2047 miRNAs, including 721 existing miRNAs, 1059 known miRNAs, and 267 novel miRNAs. Many genes and pathways related to muscle development were identified, including ERBB4, MEF2C, FZD4, the Wnt, Notch, and MAPK signaling pathways. The WGCNA established the greenyellow module and gga-miR-130b-5p for E12, magenta module and gga-miR-1643-5p for E16, purple module and gga-miR-12218-5p for E19, cyan module and gga-miR-132b-5p for E21.

CONCLUSION

These results lay a foundation for further research on the molecular regulatory mechanism of embryonic muscle development in Chengkou mountain chicken and provide a reference for other poultry and livestock muscle development studies.

A novel tumor mutational burden-based risk model predicts prognosis and correlates with immune infiltration in ovarian cancer

Tumor mutational burden (TMB) has been reported to determine the response to immunotherapy, thus affecting the patient’s prognosis in many cancers. However, it is unclear whether TMB or TMB-related signature could be used as prognostic indicators for ovarian cancer (OC), as its potential association with immune infiltration remains poorly understood. Therefore, this study aimed to develop a novel TMB-related risk model (TMBrisk) to predict the prognosis of OC patients on the basis of exploring TMB-related genes, and to explore the potential association between TMB/TMBrisk and immune infiltration. The mutational landscape, TMB scores, and correlations between TMB and clinical characteristics and immune infiltration were investigated in The Cancer Genome Atlas (TCGA)-OV cohort. Differentially expressed gene (DEG) analyses and weighted gene co-expression network analysis (WGCNA) were performed to derive TMB-related genes. TMBrisk was constructed by Cox regression and further validated in Gene Expression Omnibus (GEO) datasets. The mRNA and protein expression levels and biological functions of TMBrisk hub genes were verified through Gene Expression Profiling Interactive Analysis (GEPIA), GSCA Lite, the Human Protein Atlas (HPA) database, and RT-qPCR. TMBrisk-related biological phenotypes were analyzed in function enrichment and tumor immune infiltration signature. Potential therapeutic regimens were inferred utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) database and connectivity map (CMap). According to our results, higher TMB was associated with better survival and higher CD8+ T cell, regulatory T cell, and NK cell infiltration. TMBrisk was developed based on CBWD1, ST7L, RFX5-AS1, C3orf38, LRFN1, LEMD1, and HMGB1. High TMBrisk was identified as a poor factor for prognosis in TCGA and GEO datasets; the high-TMBrisk group comprised more higher-grade (G2 and G3) and advanced clinical stage (stage III/IV) tumors. Meanwhile, higher TMBrisk was associated with an immunosuppressive phenotype, with less infiltration of a majority of immunocytes and less expression of several genes of the human leukocyte antigen (HLA) family. Moreover, a nomogram containing TMBrisk showed a strong predictive ability demonstrated by time-dependent ROC analysis. Overall, this novel TMB-related risk model (TMBrisk) could predict prognosis, evaluate immune infiltration, and discover new therapeutic regimens in OC, which is very promising in clinical promotion.

Identification of Immune-Related Markers in Hepatocellular Carcinoma Based on Gene Co-expression Network

Hepatocellular carcinoma (HCC) is a major cause of cancer-related death throughout the world, with constant increasing morbidity and mortality. Existing studies suggest that immunotherapy is beneficial to the treatment of advanced HCC. At present, it is imperative to identify biomarkers suitable for HCC immunotherapy. In this paper, the mRNA expression data of HCC were downloaded from The Cancer Genome Atlas, and Stromal Score, Immune Score and ESTIMATE Score of each sample were calculated. Weighted gene co-expression network analysis clustered the pretreated genes into eight modules. The brown module that was remarkably associated with Immune Score was identified by module eigengene-immune trait analysis, in which genes were mainly enriched in immune-related pathways. Four hub genes (CCR5, CD53, ITGB2, and TYROBP) related to tumor immunity, were screened out by intramodular gene connectivity combined with protein-protein interaction network topology analysis. Kaplan-Meier survival analysis presented a correlation between high expression of CCR5 and CD53, and better prognoses of HCC patients. TIMER analysis revealed a positive correlation between expression of each hub gene and immune cell infiltration level, and a positive correlation between the expression of each hub gene and the expression of immunosuppressive factors CTLA4 and PDCD1. Gene set enrichment analysis displayed that there was an evident difference in the activation of cytokines and the activation of immune signal transduction-related pathways between high- and low-expression groups of each hub gene. In conclusion, this study identified four potential genetic markers associated with HCC immunity and assessed their association with HCC patient's prognosis and immune microenvironment. The study results are expected to provide theoretical guidance for immunotherapy of HCC patients.

DNA comethylation analysis reveals a functional association between BRCA1 and sperm DNA fragmentation

OBJECTIVES

To identify the DNA comethylation patterns associated with sperm DNA fragmentation (SDF) and to explore the potential associations of hub genes with SDF.

DESIGN

Prospective study.

SETTING

University-affiliated reproductive medicine center.

PATIENT(S)

A total of 300 male patients consulting for couple infertility were recruited from the First Affiliated Hospital of Wenzhou Medical University.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

Comethylation network analysis based on the genome-wide methylation profile of spermatozoal DNA from 20 men was performed to identify hub modules and genes involved in SDF. Human spermatozoa were used for targeted bisulfite amplicon sequencing (267 men) or droplet digital polymerase chain reaction (45 men). The potential role of Brca1 in DNA damage was explored in mouse GC2 spermatocyte cells. Oxidative damage to spermatocytes was modeled by incubating GC2 cells with H₂O₂ (25 mM) for 90 minutes.

RESULT(S)

BRCA1 was identified as a hub gene in SDF. Promoter hypermethylation of BRCA1 was observed in those samples with a high DNA fragmentation index (DFI) compared to those with a low DFI. Concomitantly, BRCA1 mRNA expression was lower in samples with a high DFI than with a low DFI. In the GC2 cell model, Brca1 knockdown reduced cell proliferation and increased sensitivity to oxidative stress. Moreover, it increased double-strand breaks and decreased the protein levels of the DNA repair genes MRE11 and RAD51.

CONCLUSION(S)

A prominent cluster of comethylated patterns associated with SDF was identified. BRCA1 may be the hub gene involved in sperm DNA damage.

SLAMF8 Participates in Acute Renal Transplant Rejection via TLR4 Pathway on Pro-Inflammatory Macrophages

Background

Acute rejection (AR) in kidney transplantation is an established risk factor that reduces the survival rate of allografts. Despite standard immunosuppression, molecules with regulatory control in the immune pathway of AR can be used as important targets for therapeutic operations to prevent rejection.

Methods

We downloaded the microarray data of 15 AR patients and 37 non-acute rejection (NAR) patients from Gene Expression Omnibus (GEO). Gene network was constructed, and genes were classified into different modules using weighted gene co-expression network analysis (WGCNA). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Cytoscape were applied for the hub genes in the most related module to AR. Different cell types were explored by xCell online database and single-cell RNA sequencing. We also validated the SLAMF8 and TLR4 levels in Raw264.7 and human kidney tissues of TCMR.

Results

A total of 1,561 differentially expressed genes were filtered. WGCNA was constructed, and genes were classified into 12 modules. Among them, the green module was most closely associated with AR. These genes were significantly enriched in 20 pathway terms, such as cytokine–cytokine receptor interaction, chemokine signaling pathway, and other important regulatory processes. Intersection with GS > 0.4, MM > 0.9, the top 10 MCC values and DEGs in the green module, and six hub genes (DOCK2, NCKAP1L, IL2RG, SLAMF8, CD180, and PTPRE) were identified. Their expression levels were all confirmed to be significantly elevated in AR patients in GEO, Nephroseq, and quantitative real-time PCR (qRT-PCR). Single-cell RNA sequencing showed that AR patient had a higher percentage of native T, CD1C+_B DC, NKT, NK, and monocytes in peripheral blood mononuclear cells (PBMCs). Xcell enrichment scores of 20 cell types were significantly different (p<0.01), mostly immune cells, such as B cells, CD4+ Tem, CD8+ T cells, CD8+ Tcm, macrophages, M1, and monocytes. GSEA suggests that highly expressed six hub genes are correlated with allograft rejection, interferon γ response, interferon α response, and inflammatory response. In addition, SLAMF8 is highly expressed in human kidney tissues of TCMR and in M1 phenotype macrophages of Raw264.7 cell line WGCNA accompanied by high expression of TLR4.

Conclusion

This study demonstrates six hub genes and functionally enriched pathways related to AR. SLAMF8 is involved in the M1 macrophages via TLR4, which contributed to AR process.

A mutation-based gene set predicts survival benefit after immunotherapy across multiple cancers and reveals the immune response landscape

Background

Immune checkpoint inhibitor (ICI) therapy has revolutionized the treatment of many cancers. However, the limited population that benefits from ICI therapy makes it necessary to screen predictive biomarkers for stratifying patients. Currently, many biomarkers, such as tumor mutational burden (TMB), have been used in the clinic as indicative biomarkers. However, some high-TMB patients with mutations in genes that are closely related to immunotherapeutic resistance are not sensitive to ICI therapy. Thus, there is a need to move beyond TMB and identify specific genetic determinants of the response to ICI therapy. In this study, we established a comprehensive mutation-based gene set across different tumor types to predict the efficacy of ICI therapy.

Methods

We constructed and validated a mutational signature to predict the prognosis of patients treated with ICI therapy. Then, the underlying immune response landscapes of different subtypes were investigated with multidimensional data.

Results

This study included genomic and clinical data for 12,647 patients. An eleven-gene mutation-based gene set was generated to divide patients into a high-risk group and a low-risk group in a training cohort (1572 patients with 9 types of cancers who were treated with ICI therapy). Validation was performed in a validation cohort (932 patients with 5 types of cancers who were treated with ICI therapy). Mutations in these 11 genes were associated with a better response to ICI therapy. In addition, the mutation-based gene set was demonstrated to be an independent prognostic factor after ICI therapy. We further explored the role of the immune context in determining the benefits of immunotherapy in 10,143 patients with 33 types of cancers and found distinct immune landscapes for the high- and low-risk groups.

Conclusions

The mutation-based gene set developed in this study can be used to reliably predict survival benefit across cancers in patients receiving ICI therapy. The close interplay between the extrinsic and intrinsic immune landscapes in the identified patient subgroups and the subgroups’ differing responses to ICI therapy could guide immunotherapy treatment decisions for cancer patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01024-y.

Bioinformatics Analysis and Experimental Study of Exonuclease 1 Gene in Lung Adenocarcinoma

The objective of this study is to examine the role of Human Exonuclease 1(EXO1) gene in the diagnosis and prognosis of lung adenocarcinoma (LUAD), and predict the signal pathways EXO1 involved in. The clinical parameters and EXO1 expression datasets of LUAD patients were obtained from The Cancer Genome Atlas (TCGA), Oncomine and Gene Expression Omnibus (GEO) database. Wilcoxon rank-sum test was performed to determine whether EXO1 expression was upregulated in LUAD. The correlation between EXO1 expression and clinicopathological parameters was analyzed by Chi-square test, and Kaplan-Meier survival analysis and COX regression models were adopted to analyze and verify the correlation of EXO1 expression with OS of LUAD patients for the exploration of prognostic value of EXO1 in LUAD patients. The signaling pathway related to EXO1 was predicted by gene set enrichment analysis (GSEA). In addition, sera from LUAD patients and healthy subjects were collected, and real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) was conducted to detect EXO1 expression. EXO1 expression was upregulated in LUAD patients with respect to normal individuals. EXO1 expression was negatively correlated with the prognosis and thus could independently predict the prognosis of LUAD patients. EXO1 gene was involved in 128 signal pathways, of which 9 pathways may be closely related. EXO1 was highly expressed in the blood of LUAD patients. High EXO1 expression can serve as an independent risk factor for poor prognosis, and the expression of serum EXO1 has certain diagnostic value for LUAD.

The association of telomere maintenance and TERT expression with susceptibility to human papillomavirus infection in cervical epithelium

The role of telomerase reverse transcriptase (TERT) induction and telomere maintenance in carcinogenesis including cervical cancer (CC) pathogenesis has been well established. However, it remains unclear whether they affect infection of high-risk human papillomavirus (hrHPV), an initiating event for CC development. Similarly, genetic variants at the TERT locus are shown to be associated with susceptibility to CC, but it is unclear whether these SNPs modify the risk for cervical HPV infection. Here we show that in CC-derived HeLa cells, TERT overexpression inhibits, while its depletion upregulates expression of Syndecan-1 (SDC-1), a key component for HPV entry receptors. The TCGA cohort of CC analyses reveals an inverse correlation between TERT and SDC-1 expression (R = -0.23, P = 0.001). We further recruited 1330 females (520 non-HPV and 810 hrHPV-infected) without CC or high-grade cervical intraepithelial neoplasia to analyze telomeres in cervical epithelial cells and SNPs at rs2736098, rs2736100 and rs2736108, previously identified TERT SNPs for CC risk. Non-infected females exhibited age-related telomere shortening in cervical epithelial cells and their telomeres were significantly longer than those in hrHPV-infected group (1.31 ± 0.62 vs 1.19 ± 0.48, P < 0.001). There were no differences in rs2736098 and rs2736100 genotypes, but non-infected individuals had significantly a higher C-allele frequency (associated with higher TERT expression) while lower T-allele levels at rs2736108 compared with those in the hrHPV group (P = 0.020). Collectively, appropriate telomere maintenance and TERT expression in normal cervical cells may prevent CC by modulating hrHPV infection predisposition, although they are required for CC development and progression.

Posttranslational modifications in pathogenesis of PCOS

Polycystic ovary syndrome (PCOS) is a lifelong reproductive, metabolic, and psychiatric disorder that affects 5-18% of women, which is associated with a significantly increased lifetime risk of concomitant diseases, including type 2 diabetes, psychiatric disorders, and gynecological cancers. Posttranslational modifications (PTMs) play an important role in changes in protein function and are necessary to maintain cellular viability and biological processes, thus their maladjustment can lead to disease. Growing evidence suggests the association between PCOS and posttranslational modifications. This article mainly reviews the research status of phosphorylation, methylation, acetylation, and ubiquitination, as well as their roles and molecular mechanisms in the development of PCOS. In addition, we briefly summarize research and clinical trials of PCOS therapy to advance our understanding of agents that can be used to target phosphorylated, methylated, acetylated, and ubiquitinated PTM types. It provides not only ideas for future research on the mechanism of PCOS but also ideas for PCOS treatments with therapeutic potential.

Secretory autophagy-induced bladder tumour-derived extracellular vesicle secretion promotes angiogenesis by activating the TPX2-mediated phosphorylation of the AURKA-PI3K-AKT axis

Tumour angiogenesis is an independent risk factor for bladder cancer (BCa) progression, but viable and promising antiangiogenic targets are understudied. Secretory autophagy has received increasing interest recently, while the roles and executing mechanisms in the tumour microenvironment (TME) remain unclear. Herein, we found that active cathepsin B (CTSB) was upregulated in tumour tissues and serum EVs of 241 BCa patients from four cohorts and was significantly associated with poor prognosis. Starving TME (STME)-induced conventional autophagy in BCa cells elevated active CTSB levels by facilitating the expression and nuclear translocation of NFATC2. In addition, STME-induced secretory autophagy simultaneously led to markedly increased secretion of LC3-conjugated EVs loaded with active CTSB (EV-CTSB) into the TME. The increased exogenous active CTSB in endothelial cells by directly ingesting EV-CTSB prominently activated the TPX2-mediated phosphorylation of the AURKA-PI3K-AKT axis, increased VEGFA expression, and promoted angiogenesis. Our findings not only verify that EV-CTSB can be a promising target for antiangiogenic strategies in bladder cancer, but also reveal a novel action pattern based on secretory autophagy-induced EV secretion which is enlightening to explore crosstalk in the TME from various perspectives.

Telomerase subunit Est2 marks internal sites that are prone to accumulate DNA damage

Background

The main function of telomerase is at the telomeres but under adverse conditions telomerase can bind to internal regions causing deleterious effects as observed in cancer cells.

Results

By mapping the global occupancy of the catalytic subunit of telomerase (Est2) in the budding yeast Saccharomyces cerevisiae, we reveal that it binds to multiple guanine-rich genomic loci, which we termed “non-telomeric binding sites” (NTBS). We characterize Est2 binding to NTBS. Contrary to telomeres, Est2 binds to NTBS in G1 and G2 phase independently of Est1 and Est3. The absence of Est1 and Est3 renders telomerase inactive at NTBS. However, upon global DNA damage, Est1 and Est3 join Est2 at NTBS and telomere addition can be observed indicating that Est2 occupancy marks NTBS regions as particular risks for genome stability.

Conclusions

Our results provide a novel model of telomerase regulation in the cell cycle using internal regions as “parking spots” of Est2 but marking them as hotspots for telomere addition.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-021-01167-1.

Identification of Immune-Cell-Related Prognostic Biomarkers of Esophageal Squamous Cell Carcinoma Based on Tumor Microenvironment

Background

Esophageal squamous cell carcinoma (ESCC) is one of the most fatal cancers in the world. The 5-year survival rate of ESCC is <30%. However, few biomarkers can accurately predict the prognosis of patients with ESCC. We aimed to identify potential survival-associated biomarkers for ESCC to improve its poor prognosis.

Methods

ImmuneAI analysis was first used to access the immune cell abundance of ESCC. Then, ESTIMATE analysis was performed to explore the tumor microenvironment (TME), and differential analysis was used for the selection of immune-related differentially expressed genes (DEGs). Weighted gene coexpression network analysis (WGCNA) was used for selecting the candidate DEGs. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to build the immune-cell-associated prognostic model (ICPM). Kaplan–Meier curve of survival analysis was performed to evaluate the efficacy of the ICPM.

Results

Based on the ESTIMATE and ImmuneAI analysis, we obtained 24 immune cells’ abundance. Next, we identified six coexpression module that was associated with the abundance. Then, LASSO regression models were constructed by selecting the genes in the module that is most relevant to immune cells. Two test dataset was used to testify the model, and we finally, obtained a seven-genes survival model that performed an excellent prognostic efficacy.

Conclusion

In the current study, we filtered seven key genes that may be potential prognostic biomarkers of ESCC, and they may be used as new factors to improve the prognosis of cancer.

A Pan-Cancer Analysis of Transcriptome and Survival Reveals Prognostic Differentially Expressed LncRNAs and Predicts Novel Drugs for Glioblastoma Multiforme Therapy

Numerous studies have identified various prognostic long non-coding RNAs (LncRNAs) in a specific cancer type, but a comprehensive pan-cancer analysis for prediction of LncRNAs that may serve as prognostic biomarkers is of great significance to be performed. Glioblastoma multiforme (GBM) is the most common and aggressive malignant adult primary brain tumor. There is an urgent need to identify novel therapies for GBM due to its poor prognosis and universal recurrence. Using available LncRNA expression data of 12 cancer types and survival data of 30 cancer types from online databases, we identified 48 differentially expressed LncRNAs in cancers as potential pan-cancer prognostic biomarkers. Two candidate LncRNAs were selected for validation in GBM. By the expression detection in GBM cell lines and survival analysis in GBM patients, we demonstrated the reliability of the list of pan-cancer prognostic LncRNAs obtained above. By constructing LncRNA-mRNA-drug network in GBM, we predicted novel drug-target interactions for GBM correlated LncRNA. This analysis has revealed common prognostic LncRNAs among cancers, which may provide insights into cancer pathogenesis and novel drug target in GBM.

Ultrasensitive Dual-Signal Detection of Telomerase and MiR-21 Based on Boolean Logic Operations

Telomerase and micro-RNAs (miRNAs) are simultaneously upregulated in a variety of tumor cells and have emerged as promising tumor markers. However, sensitive detection of telomerase and miRNAs in situ remains a great challenge due to their low expression levels. Here, we designed a Boolean logic "AND" signal amplification strategy based on functionalized ordered mesoporous nanoparticles (FOMNs) to achieve ultrasensitive detection of telomerase and miR-21 in living tumor cells. Briefly, the strategy uses telomerase as an input to enable the release of DNA3-ROX-BHQ hairpins by making the wrapping DNA1 form a DNA-a hairpin with the joint participation of dNTPs. Subsequently, DNA2-Ag, DNA3-ROX-BHQ, and the second input miR-21 participated in hybridization chain reaction to amplify fluorescence and Raman signals. Experimental results showed the intensity of output dual signals relevant to the expression levels of telomerase and miR-21. The Ag nanoparticles (AgNPs) not only enhanced the fluorescence signals but also allowed to obtain more sensitive Raman signals. Therefore, even if expression of tumor markers is at a low level, the FOMN-based dual-signal logic operation strategy can still achieve sensitive detection of telomerase and miR-21 in situ. Furthermore, FOMNs can detect miR-21 expression levels in a short time. Consequently, this strategy has a potential clinical application value in detection of tumor markers and the assessment of tumor treatment efficacy.

Pan-Cancer Analysis of Clinical Relevance via Telomere Maintenance Mechanism

Understanding the telomere maintenance mechanism (TMM) in immortal cancer cells is vital for TMM-targeted therapies in clinical settings. In this study, we classified four telomere maintenance mechanisms into telomerase, ALT, telomerase + ALT, and non-defined telomere maintenance mechanism (NDTMM) across 31 cancer types using 10,704 transcriptomic datasets from The Cancer Genome Atlas. Our results demonstrated that approximately 50% of the total cohort displayed ALT activity with high telomerase activity in most cancer types. We confirmed significant patient prognoses according to distinct TMMs in six cancer types: adrenocortical carcinoma (ACC), PAAD, HNSC, SARC, GBM, and metastatic cancer. Patients with metastasis had a poor prognosis in the ALT group (p < 0.006) subjected to RAS protein signal transduction. Glioblastoma patients had poor prognosis in NDTMM (p < 0.0043) and showed high levels of myeloid leukocyte activation. Pancreatic adenocarcinoma (p < 0.04) and head and neck squamous cell carcinoma (p < 0.046) patients had a good prognosis in the ALT group with high immune cell activation. Furthermore, we showed that master transcriptional regulators might affect the selection of the TMM pathway and explained why different telomere maintenance mechanisms exist. Furthermore, they can be used to segregate patients and predict responders to different TMM-targeted therapeutics.

Network analysis with biological data of cancer patients: A scoping review

BACKGROUND & OBJECTIVE

Network Analysis (NA) is a mathematical method that allows exploring relations between units and representing them as a graph. Although NA was initially related to social sciences, the past two decades was introduced in Bioinformatics. The recent growth of the networks' use in biological data analysis reveals the need to further investigate this area. In this work, we attempt to identify the use of NA with biological data, and specifically: (a) what types of data are used and whether they are integrated or not, (b) what is the purpose of this analysis, predictive or descriptive, and (c) the outcome of such analyses, specifically in cancer diseases.

METHODS & MATERIALS

The literature review was conducted on two databases, PubMed & IEEE, and was restricted to journal articles of the last decade (January 2010 - December 2019). At a first level, all articles were screened by title and abstract, and at a second level the screening was conducted by reading the full text article, following the predefined inclusion & exclusion criteria leading to 131 articles of interest. A table was created with the information of interest and was used for the classification of the articles. The articles were initially classified to analysis studies and studies that propose a new algorithm or methodology. Each one of these categories was further screened by the following clustering criteria: (a) data used, (b) study purpose, (c) study outcome. Specifically for the studies proposing a new algorithm, the novelty presented in each one was detected.

RESULTS

& Conclusions: In the past five years researchers are focusing on creating new algorithms and methodologies to enhance this field. The articles' classification revealed that only 25% of the analyses are integrating multi-omics data, although 50% of the new algorithms developed follow this integrative direction. Moreover, only 20% of the analyses and 10% of the newly developed methodologies have a predictive purpose. Regarding the result of the works reviewed, 75% of the studies focus on identifying, prognostic or not, gene signatures. Concluding, this review revealed the need for deploying predictive and multi-omics integrative algorithms and methodologies that can be used to enhance cancer diagnosis, prognosis and treatment.

Identification of Potential Prognostic Biomarkers Associated With Cancerometastasis in Skin Cutaneous Melanoma

Skin cutaneous melanoma (SKCM) is a highly aggressive tumor. The mortality and drug resistance among it are high. Thus, exploring predictive biomarkers for prognosis has become a priority. We aimed to find immune cell-based biomarkers for survival prediction. Here 321 genes were differentially expressed in immune-related groups after ESTIMATE analysis and differential analysis. Two hundred nineteen of them were associated with the metastasis of SKCM via weighted gene co-expression network analysis. Twenty-six genes in this module were hub genes. Twelve of the 26 genes were related to overall survival in SKCM patients. After a multivariable Cox regression analysis, we obtained six of these genes (PLA2G2D, IKZF3, MS4A1, ZC3H12D, FCRL3, and P2RY10) that were independent prognostic signatures, and a survival model of them performed excellent predictive efficacy. The results revealed several essential genes that may act as significant prognostic factors of SKCM, which could deepen our understanding of the metastatic mechanisms and improve cancer treatment.

SDF4 Is a Prognostic Factor for 28-Days Mortality in Patients With Sepsis via Negatively Regulating ER Stress

Sepsis is a heterogeneous syndrome induced by infection and results in high mortality. Even though more than 100 biomarkers for sepsis prognosis were evaluated, prediction of patient outcomes in sepsis continues to be driven by clinical signs because of unsatisfactory specificity and sensitivity of these biomarkers. This study aimed to elucidate the key candidate genes involved in sepsis response and explore their downstream effects based on weighted gene co-expression network analysis (WGCNA). The dataset GSE63042 with sepsis outcome information was obtained from the Gene Expression Omnibus (GEO) database and then consensus WGCNA was conducted. We identified the hub gene SDF4 (stromal cell derived factor 4) from the M6 module, which was significantly associated with mortality. Subsequently, two datasets (GSE54514 and E-MTAB-4421) and cohort validation (n=89) were performed. Logistic regression analysis was used to build a prediction model and the combined score resulting in a satisfactory prognosis value (area under the ROC curve=0.908). The model was subsequently tested by another sepsis cohort (n=70, ROC= 0.925). We next demonstrated that endoplasmic reticulum (ER) stress tended to be more severe in patients PBMCs with negative outcomes compared to those with positive outcomes and SDF4 was related to this phenomenon. In addition, our results indicated that adenovirus-mediated Sdf4 overexpression attenuated ER stress in cecal ligation and puncture (CLP) mice lung. In summary, our study indicates that incorporation of SDF4 can improve clinical parameters predictive value for the prognosis of sepsis, and decreased expression levels of SDF4 contributes to excessive ER stress, which is associated with worsened outcomes, whereas overexpression of SDF4 attenuated such activation.

A five-gene signature is a prognostic biomarker in pan-cancer and related with immunologically associated extracellular matrix

The tumor microenvironment (TME) is related to extracellular matrix (ECM) dynamics and has a broad fundamental and mechanistic role in tumorigenesis and cancer progression. We hypothesized that ECM regulators might play an essential role in pan‐cancer attribution by causing a generic effect through its regulation of the dynamics of ECM alteration. By analyzing data from TCGA using GSEA and univariate Cox regression analysis, we found that ECM regulator genes were significantly enriched and contributed to mortality in various cancer types. Notably, UMAP analysis revealed that ECM regulator genes dominated the differences between tumor and adjacent normal tissues based on 59 or 31 pan‐survival‐related ECM gene sets. Subsequently, a five‐gene signature consisting of the predominant ECM regulators ADAM12, MMP1, SERPINE1, PLOD3, and P4HA3 was identified. We found that this five‐gene signature was pro‐mortality in 18 types of cancer in TCGA, and validated eleven other cancer types in TCGA and seven types in the TARGET and CoMMpass databases using overall survival analysis. KEGG pathway enrichment and Pearson correlation analysis indicated that these five component genes that were correlated with specific ECM proteins involved in tumorigenesis from the ECM receptor interaction gene set. Additionally, the fitted results of a linear model were applied to strengthen the discovery, demonstrating that the five genes were correlated with immune infiltration score and especially associated with typically immunologically “cold” tumors. We thus conclude that the ADAM12, MMP1, SERPINE1, PLOD3, and P4HA3 signature showed a close association with a pan‐cancer effect on prognosis and is related to ECM proteins in the TME which corresponding with immunologically “cold” cancer types.

KDM4 Orchestrates Epigenomic Remodeling of Senescent Cells and Potentiates the Senescence-Associated Secretory Phenotype

Cellular senescence restrains the expansion of neoplastic cells through several layers of regulation. We report that the histone H3-specific demethylase KDM4 is expressed as human stromal cells undergo senescence. In clinical oncology, upregulated KDM4 and diminished H3K9/H3K36 methylation correlate with poorer survival of prostate cancer patients post-chemotherapy. Global chromatin accessibility mapping via ATAC-seq, and expression profiling through RNA-seq, reveal global changes of chromatin openness and spatiotemporal reprogramming of the transcriptomic landscape, which underlie the senescence-associated secretory phenotype (SASP). Selective targeting of KDM4 dampens the SASP of senescent stromal cells, promotes cancer cell apoptosis in the treatment-damaged tumor microenvironment (TME), and prolongs survival of experimental animals. Our study supports dynamic changes of H3K9/H3K36 methylation during senescence, identifies an unusually permissive chromatin state, and unmasks KDM4 as a key SASP modulator. KDM4 targeting presents a novel therapeutic avenue to manipulate cellular senescence and limit its contribution to age-related pathologies including cancer.

Analysis of TERT Isoforms across TCGA, GTEx and CCLE Datasets

Reactivation of the multi-subunit ribonucleoprotein telomerase is the primary telomere maintenance mechanism in cancer, but it is rate-limited by the enzymatic component, telomerase reverse transcriptase (TERT). While regulatory in nature, TERT alternative splice variant/isoform regulation and functions are not fully elucidated and are further complicated by their highly diverse expression and nature. Our primary objective was to characterize TERT isoform expression across 7887 neoplastic and 2099 normal tissue samples using The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression Project (GTEx), respectively. We confirmed the global overexpression and splicing shift towards full-length TERT in neoplastic tissue. Stratifying by tissue type we found uncharacteristic TERT expression in normal brain tissue subtypes. Stratifying by tumor-specific subtypes, we detailed TERT expression differences potentially regulated by subtype-specific molecular characteristics. Focusing on β-deletion splicing regulation, we found the NOVA1 trans-acting factor to mediate alternative splicing in a cancer-dependent manner. Of relevance to future tissue-specific studies, we clustered cancer cell lines with tumors from related origin based on TERT isoform expression patterns. Taken together, our work has reinforced the need for tissue and tumour-specific TERT investigations, provided avenues to do so, and brought to light the current technical limitations of bioinformatic analyses of TERT isoform expression.

Multi-omics characterization and validation of invasiveness-related molecular features across multiple cancer types

Background

Tumor invasiveness reflects many biological changes associated with tumorigenesis, progression, metastasis, and drug resistance. Therefore, we performed a systematic assessment of invasiveness-related molecular features across multiple human cancers.

Materials and methods

Multi-omics data, including gene expression, miRNA, DNA methylation, and somatic mutation, in approximately 10,000 patients across 30 cancer types from The Cancer Genome Atlas, Gene Expression Omnibus, PRECOG, and our institution were enrolled in this study.

Results

Based on a robust gene signature, we established an invasiveness score and found that the score was significantly associated with worse prognosis in almost all cancers. Then, we identified common invasiveness-associated dysregulated molecular features between high- and low-invasiveness score group across multiple cancers, as well as investigated their mutual interfering relationships thus determining whether the dysregulation of invasiveness-related genes was caused by abnormal promoter methylation or miRNA expression. We also analyzed the correlations between the drug sensitivity data from cancer cell lines and the expression level of 685 invasiveness-related genes differentially expressed in at least ten cancer types. An integrated analysis of the correlations among invasiveness-related genetic features and drug response were conducted in esophageal carcinoma patients to outline the complicated regulatory mechanism of tumor invasiveness status in multiple dimensions. Moreover, functional enrichment suggests the invasiveness score might serve as a predictive biomarker for cancer patients receiving immunotherapy.

Conclusion

Our pan-cancer study provides a comprehensive atlas of tumor invasiveness and may guide more precise therapeutic strategies for tumor patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02773-x

Thyroid Carcinoma: Phenotypic Features, Underlying Biology and Potential Relevance for Targeting Therapy

Thyroid carcinoma consists a group of phenotypically heterogeneous cancers. Recent advances in biological technologies have been advancing the delineation of genetic, epigenetic, and non-genetic factors that contribute to the heterogeneities of these cancers. In this review article, we discuss new findings that are greatly improving the understanding of thyroid cancer biology and facilitating the identification of novel targets for therapeutic intervention. We review the phenotypic features of different subtypes of thyroid cancers and their underlying biology. We discuss recent discoveries in thyroid cancer heterogeneities and the critical mechanisms contributing to the heterogeneity with emphases on genetic and epigenetic factors, cancer stemness traits, and tumor microenvironments. We also discuss the potential relevance of the intratumor heterogeneity in understanding therapeutic resistance and how new findings in tumor biology can facilitate designing novel targeting therapies for thyroid cancer.

Integrated analysis of telomerase enzymatic activity unravels an association with cancer stemness and proliferation

Active telomerase is essential for stem cells and most cancers to maintain telomeres. The enzymatic activity of telomerase is related but not equivalent to the expression of TERT, the catalytic subunit of the complex. Here we show that telomerase enzymatic activity can be robustly estimated from the expression of a 13-gene signature. We demonstrate the validity of the expression-based approach, named EXTEND, using cell lines, cancer samples, and non-neoplastic samples. When applied to over 9,000 tumors and single cells, we find a strong correlation between telomerase activity and cancer stemness. This correlation is largely driven by a small population of proliferating cancer cells that exhibits both high telomerase activity and cancer stemness. This study establishes a computational framework for quantifying telomerase enzymatic activity and provides new insights into the relationships among telomerase, cancer proliferation, and stemness.

Telomere-related Markers for Cancer

Telomeres are structurally nucleoprotein complexes at termini of linear chromosomes and essential to chromosome stability/integrity. In normal human cells, telomere length erodes progressively with each round of cell divisions, which serves as an important barrier to uncontrolled proliferation and malignant transformation. In sharp contrast, telomere maintenance is a key feature of human malignant cells and required for their infinite proliferation and maintenance of other cancer hallmarks as well. Thus, a telomere-based anti-cancer strategy has long been suggested. However, clinically efficient and specific drugs targeting cancer telomere-maintenance have still been in their infancy thus far. To achieve this goal, it is highly necessary to elucidate how exactly cancer cells maintain functional telomeres. In the last two decades, numerous studies have provided profound mechanistic insights, and the identified mechanisms include the aberrant activation of telomerase or the alternative lengthening of telomere pathway responsible for telomere elongation, dysregulation and mutation of telomere-associated factors, and other telomere homeostasis-related signaling nodes. In the present review, these various strategies employed by malignant cells to regulate their telomere length, structure and function have been summarized, and potential implications of these findings in the rational development of telomere-based cancer therapy and other clinical applications for precision oncology have been discussed.

TAF1A and ZBTB41 serve as novel key genes in cervical cancer identified by integrated approaches

Cervical cancer (CC) is the second most common cancer and the leading cause of cancer mortality in women. Numerous studies have found that the development of CC was associated with multiple genes. However, the mechanisms on gene level are enigmatic, hindering the understanding of its functional roles. This study sought to identify prognostic biomarkers of CC, and explore their biological functions. Here we conducted an integrated analysis to screen potential vital genes. Candidate genes were further tested by experiments in clinical specimens and cancer cell line. Then, molecular modeling was used to predict the three-dimensional structure of candidate genes’ proteins, and the interaction pattern was analyzed by docking simulation technique. Among the potential genes identified, we found that TAF1A and ZBTB41 were highly correlated. Furthermore, there was a definite interaction between the proteins of TAF1A and ZBTB41, which was affected by the activity of the p53 signaling pathway. In conclusion, our findings identified TAF1A and ZBTB41 could serve as biomarkers of CC. We confirmed their biological function and deciphered their interaction for the first time, which may be helpful for developing further researches.