New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx

Transcriptomic analysis reveals a tissue-specific loss of identity during ageing and cancer

INTRODUCTION

Understanding changes in cell identity in cancer and ageing is of great importance. In this work, we analyzed how gene expression changes in human tissues are associated with tissue specificity during cancer and ageing using transcriptome data from TCGA and GTEx.

RESULTS

We found significant downregulation of tissue-specific genes during ageing in 40% of the tissues analyzed, which suggests loss of tissue identity with age. For most cancer types, we have noted a consistent pattern of downregulation in genes that are specific to the tissue from which the tumor originated. Moreover, we observed in cancer an activation of genes not usually expressed in the tissue of origin as well as an upregulation of genes specific to other tissues. These patterns in cancer were associated with patient survival. The age of the patient, however, did not influence these patterns.

CONCLUSION

We identified loss of cellular identity in 40% of the tissues analysed during human ageing, and a clear pattern in cancer, where during tumorigenesis cells express genes specific to other organs while suppressing the expression of genes from their original tissue. The loss of cellular identity observed in cancer is associated with prognosis and is not influenced by age, suggesting that it is a crucial stage in carcinogenesis.

NK Receptors Replace CD28 As the Dominant Source of Signal 2 for Cognate Recognition of Cancer Cells by TAA-specific Effector CD8+ T Cells

CD28-driven "signal 2" is critical for naïve CD8+ T cell responses to dendritic cell (DC)-presented weak antigens, including non-mutated tumor-associated antigens (TAAs). However, it is unclear how DC-primed cytotoxic T lymphocytes (CTLs) respond to the same TAAs presented by cancer cells which lack CD28 ligands. Here, we show that NK receptors (NKRs) DNAM-1 and NKG2D replace CD28 during CTL re-activation by cancer cells presenting low levels of MHC I/TAA complexes, leading to enhanced proximal TCR signaling, immune synapse formation, CTL polyfunctionality, release of cytolytic granules and antigen-specific cancer cell killing. Double-transduction of T cells with recombinant TCR and NKR constructs or upregulation of NKR-ligand expression on cancer cells by chemotherapy enabled effective recognition and killing of poorly immunogenic tumor cells by CTLs. Operational synergy between TCR and NKRs in CTL recognition explains the ability of cancer-expressed self-antigens to serve as tumor rejection antigens, helping to develop more effective therapies.

Prognostic Model and Tumor Immune Microenvironment Analysis of Complement-Related Genes in Gastric Cancer

Introduction

The complement system is integral to the innate and adaptive immune response, helping antibodies eliminate pathogens. However, the potential role of complement and its modulators in the tumor microenvironment (TME) of gastric cancer (GC) remains unclear.

Methods

This study assessed the expression, frequency of somatic mutations, and copy number variations of complement family genes in GC derived from The Cancer Genome Atlas (TCGA). Lasso and Cox regression analyses were conducted to develop a prognostic model based on the complement genes family, with the training and validation sets taken from the TCGA-GC cohort (n=371) and the International Gene Expression Omnibus (GEO) cohort (n=433), correspondingly. The nomogram assessment model was used to predict patient outcomes. Additionally, the link between immune checkpoints, immune cells, and the prognostic model was investigated.

Results

In contrast to patients at low risk, those at high risk had a less favorable outcome. The prognostic model-derived risk score was shown to serve as a prognostic marker of GC independently, as per the multivariate Cox analysis. Nomogram assessment showed that the model had high reliability for predicting the survival of patients with GC in the 1, 3, 5 years. Additionally, the risk score was positively linked to the expression of immune checkpoints, notably CTLA4, LAG3, PDCD1, and CD274, according to an analysis of immune processes. The core gene C5aR1 in the prognostic model was found to be upregulated in GC tissues in contrast to adjoining normal tissues, and patients with elevated expressed levels of C5aR1 had lower 10-year overall survival (OS) rates.

Conclusion

Our work reveals that complement genes are associated with the diversity and complexity of TME. The complement prognosis model help improves our understanding of TME infiltration characteristics and makes immunotherapeutic strategies more effective.

β-catenin/CBP activation of mTORC1 signaling promotes partial epithelial-mesenchymal states in head and neck cancer

Head and neck cancers, which include oral squamous cell carcinoma (OSCC) as a major subsite, exhibit cellular plasticity that includes features of an epithelial-mesenchymal transition (EMT), referred to as partial-EMT (p-EMT). To identify molecular mechanisms contributing to OSCC plasticity, we performed a multiphase analysis of single cell RNA sequencing (scRNAseq) data from human OSCC. This included a multiresolution characterization of cancer cell subgroups to identify pathways and cell states that are heterogeneously represented, followed by casual inference analysis to elucidate activating and inhibitory relationships between these pathways and cell states. This approach revealed signaling networks associated with hierarchical cell state transitions, which notably included an association between β-catenin-driven CREB-binding protein (CBP) activity and mTORC1 signaling. This network was associated with subpopulations of cancer cells that were enriched for markers of the p-EMT state and poor patient survival. Functional analyses revealed that β-catenin/CBP induced mTORC1 activity in part through the transcriptional regulation of a raptor-interacting protein, chaperonin containing TCP1 subunit 5 (CCT5). Inhibition of β-catenin-CBP activity through the use of the orally active small molecule, E7386, reduced the expression of CCT5 and mTORC1 activity in vitro, and inhibited p-EMT-associated markers and tumor development in a murine model of OSCC. Our study highlights the use of multiresolution network analyses of scRNAseq data to identify targetable signals for therapeutic benefit, thus defining an underappreciated association between β-catenin/CBP and mTORC1 signaling in head and neck cancer plasticity.

Disclosing transcriptomics network-based signatures of glioma heterogeneity using sparse methods

Gliomas are primary malignant brain tumors with poor survival and high resistance to available treatments. Improving the molecular understanding of glioma and disclosing novel biomarkers of tumor development and progression could help to find novel targeted therapies for this type of cancer. Public databases such as The Cancer Genome Atlas (TCGA) provide an invaluable source of molecular information on cancer tissues. Machine learning tools show promise in dealing with the high dimension of omics data and extracting relevant information from it. In this work, network inference and clustering methods, namely Joint Graphical lasso and Robust Sparse K-means Clustering, were applied to RNA-sequencing data from TCGA glioma patients to identify shared and distinct gene networks among different types of glioma (glioblastoma, astrocytoma, and oligodendroglioma) and disclose new patient groups and the relevant genes behind groups' separation. The results obtained suggest that astrocytoma and oligodendroglioma have more similarities compared with glioblastoma, highlighting the molecular differences between glioblastoma and the others glioma subtypes. After a comprehensive literature search on the relevant genes pointed our from our analysis, we identified potential candidates for biomarkers of glioma. Further molecular validation of these genes is encouraged to understand their potential role in diagnosis and in the design of novel therapies.

PanomiR: a systems biology framework for analysis of multi-pathway targeting by miRNAs

Charting microRNA (miRNA) regulation across pathways is key to characterizing their function. Yet, no method currently exists that can quantify how miRNAs regulate multiple interconnected pathways or prioritize them for their ability to regulate coordinate transcriptional programs. Existing methods primarily infer one-to-one relationships between miRNAs and pathways using differentially expressed genes. We introduce PanomiR, an in silico framework for studying the interplay of miRNAs and disease functions. PanomiR integrates gene expression, mRNA-miRNA interactions and known biological pathways to reveal coordinated multi-pathway targeting by miRNAs. PanomiR utilizes pathway-activity profiling approaches, a pathway co-expression network and network clustering algorithms to prioritize miRNAs that target broad-scale transcriptional disease phenotypes. It directly resolves differential regulation of pathways, irrespective of their differential gene expression, and captures co-activity to establish functional pathway groupings and the miRNAs that may regulate them. PanomiR uses a systems biology approach to provide broad but precise insights into miRNA-regulated functional programs. It is available at https://bioconductor.org/packages/PanomiR.

A workflow to study mechanistic indicators for driver gene prediction with Moonlight

Prediction of driver genes (tumor suppressors and oncogenes) is an essential step in understanding cancer development and discovering potential novel treatments. We recently proposed Moonlight as a bioinformatics framework to predict driver genes and analyze them in a system-biology-oriented manner based on -omics integration. Moonlight uses gene expression as a primary data source and combines it with patterns related to cancer hallmarks and regulatory networks to identify oncogenic mediators. Once the oncogenic mediators are identified, it is important to include extra levels of evidence, called mechanistic indicators, to identify driver genes and to link the observed gene expression changes to the underlying alteration that promotes them. Such a mechanistic indicator could be for example a mutation in the regulatory regions for the candidate gene. Here, we developed new functionalities and released Moonlight2 to provide the user with a mutation-based mechanistic indicator as a second layer of evidence. These functionalities analyze mutations in a cancer cohort to classify them into driver and passenger mutations. Those oncogenic mediators with at least one driver mutation are retained as the final set of driver genes. We applied Moonlight2 to the basal-like breast cancer subtype, lung adenocarcinoma and thyroid carcinoma using data from The Cancer Genome Atlas. For example, in basal-like breast cancer, we found four oncogenes (COPZ2, SF3B4, KRTCAP2 and POLR2J) and nine tumor suppressor genes (KIR2DL4, KIF26B, ARL15, ARHGAP25, EMCN, GMFG, TPK1, NR5A2 and TEK) containing a driver mutation in their promoter region, possibly explaining their deregulation. Moonlight2R is available at https://github.com/ELELAB/Moonlight2R.

Analysis of High-Risk Neuroblastoma Transcriptome Reveals Gene Co-Expression Signatures and Functional Features

Neuroblastoma represents a neoplastic expansion of neural crest cells in the developing sympathetic nervous system and is childhood's most common extracranial solid tumor. The heterogeneity of gene expression in different types of cancer is well-documented, and genetic features of neuroblastoma have been described by classification, development stage, malignancy, and progression of tumors. Here, we aim to analyze RNA sequencing datasets, publicly available in the GDC data portal, of neuroblastoma tumor samples from various patients and compare them with normal adrenal gland tissue from the GTEx data portal to elucidate the gene expression profile and regulation networks they share. Our results from the differential expression, weighted correlation network, and functional enrichment analyses that we performed with the count data from neuroblastoma and standard normal gland samples indicate that the analysis of transcriptome data from 58 patients diagnosed with high-risk neuroblastoma shares the expression pattern of 104 genes. More importantly, our analyses identify the co-expression relationship and the role of these genes in multiple biological processes and signaling pathways strongly associated with this disease phenotype. Our approach proposes a group of genes and their biological functions to be further investigated as essential molecules and possible therapeutic targets of neuroblastoma regardless of the etiology of individual tumors.

Sertraline as a potential cancer therapeutic approach: Biological relevance of TCTP in breast cancer cell lines and tumors

PURPOSE

This study aimed to evaluate the role of Translationally Controlled Tumor Protein (TCTP) in breast cancer (BC) and investigate the effects of sertraline, a serotonin selective reuptake inhibitor (SSRI), on BC cells. The objective was to assess the potential of sertraline as a therapeutic agent in BC treatment by examining its ability to inhibit TCTP expression and exert antitumor effects.

MATERIAL AND METHODS

We utilized five different BC cell lines representing the molecular heterogeneity and distinct subtypes of BC, including luminal, normal-like, HER2-positive, and triple-negative BC. These subtypes play a crucial role in determining clinical treatment strategies and prognosis.

RESULTS

The highest levels of TCTP were observed in triple-negative BC cell lines, known for their aggressive behavior. Sertraline treatment reduced TCTP expression in BC cell lines, significantly impacting cell viability, clonogenicity, and migration. Additionally, sertraline sensitized triple-negative BC cell lines to cytotoxic chemotherapeutic drugs (doxorubicin and cisplatin) suggesting its potential as an adjunctive therapy to enhance the chemotherapeutic response. Bioinformatic analysis of TCTP mRNA levels in TCGA BC data revealed a negative correlation between TCTP levels and patient survival, as well as between TCTP/tpt1 and Ki67. These findings contradict our data and previous studies indicating a correlation between TCTP protein levels and aggressiveness and poor prognosis in BC.

CONCLUSIONS

Sertraline shows a promise as a potential therapeutic option for BC, particularly in triple-negative BC. Its ability to inhibit TCTP expression, enhance chemotherapeutic response, highlights its potential clinical utility in BC treatment, specifically in triple-negative BC subtype.

MacroH2A restricts inflammatory gene expression in melanoma cancer-associated fibroblasts by coordinating chromatin looping

MacroH2A has established tumour suppressive functions in melanoma and other cancers, but an unappreciated role in the tumour microenvironment. Using an autochthonous, immunocompetent mouse model of melanoma, we demonstrate that mice devoid of macroH2A variants exhibit increased tumour burden compared with wild-type counterparts. MacroH2A-deficient tumours accumulate immunosuppressive monocytes and are depleted of functional cytotoxic T cells, characteristics consistent with a compromised anti-tumour response. Single cell and spatial transcriptomics identify increased dedifferentiation along the neural crest lineage of the tumour compartment and increased frequency and activation of cancer-associated fibroblasts following macroH2A loss. Mechanistically, macroH2A-deficient cancer-associated fibroblasts display increased myeloid chemoattractant activity as a consequence of hyperinducible expression of inflammatory genes, which is enforced by increased chromatin looping of their promoters to enhancers that gain H3K27ac. In summary, we reveal a tumour suppressive role for macroH2A variants through the regulation of chromatin architecture in the tumour stroma with potential implications for human melanoma.

Tumor-derived exosomes RNA expression profiling identifies the prognosis, immune characteristics, and treatment in HR+/HER2-breast cancer

Exosomes play crucial roles in intercellular communication and are involved in the onset and progression of various types of cancers, including breast cancer. However, the RNA composition of breast cancer-derived exosomes has not been comprehensively explored. We conducted microarray assays on exosomes isolated from breast cancer and healthy breast epithelial cells from three patients with hormone receptor (HR) +/ human epidermal growth factor receptor (HER2) - breast cancer and identified 817 differentially expressed genes (DEGs). Among these, 315 upregulated tumor-derived exosome genes (UTEGs) were used to classify HR+/HER2- breast cancers into two categories, revealing a difference in survival rates between the groups. We developed and validated a novel prognostic exosome score (ES) model consisting of four UTEGs that provides a refined prognosis prediction in HR+/HER2-breast cancer. ES reflects various immune-related features, including somatic variation, immunogenicity, and tumor immune infiltrate composition. Our findings indicate a considerable positive correlation between the ES and drug sensitivity values for vincristine, paclitaxel, and docetaxel. However, ES was remarkably higher in the endocrine therapy non-responder group than in the responder group. Immunohistochemistry confirmed the remarkable expression of the four model genes in tumor tissues, and their expression in MCF-7 cell exosomes was higher than that in MCF10A cells, as verified via qPCR. In summary, tumor-derived exosome genes provide novel insights into the subtyping, prognosis, and treatment of HR+/HER2-breast cancer.

Identification of Long Non-Coding RNA Profiles and Potential Therapeutic Agents for Fibrolamellar Carcinoma Based on RNA-Sequencing Data

BACKGROUND

Fibrolamellar carcinoma (FLC) is a rare type of liver cancer that primarily affects adolescents and young adults without prior liver disease or viral infections. Patients with FLC generally have non-specific symptoms, are often diagnosed at a later stage, and experience a higher frequency of metastases compared to patients with other liver cancers. A fusion transcript of DNAJB1 and PRKACA, which can lead to increased activity of PKA and cellular proliferation, has been identified in all FLC patients, but the exact mechanism through which FLC develops remains unclear. In this study, we investigated common lncRNA profiles in various FLC samples using bioinformatics analyses.

METHODS

We analyzed differentially expressed (DE) lncRNAs from three RNA sequencing datasets. Using lncRNAs and DE mRNAs, we predicted potential lncRNA target genes and performed Gene Ontology (GO) and KEGG analyses with the DE lncRNA target genes. Moreover, we screened for small-molecule compounds that could act as therapeutic targets for FLC.

RESULTS

We identified 308 DE lncRNAs from the RNA sequencing datasets. In addition, we performed a trans-target prediction analysis and identified 454 co-expressed pairs in FLC. The GO analysis showed that the lncRNA-related up-regulated mRNAs were enriched in the regulation of protein kinase C signaling and cAMP catabolic processes, while lncRNA-related down-regulated mRNAs were enriched in steroid, retinol, cholesterol, and xenobiotic metabolic processes. The analysis of small-molecule compounds for FLC treatment identified vitexin, chlorthalidone, triamterene, and amiloride, among other compounds.

CONCLUSIONS

We identified potential therapeutic targets for FLC, including lncRNA target genes as well as small-molecule compounds that could potentially be used as treatments. Our findings could contribute to furthering our understanding of FLC and providing potential avenues for diagnosis and treatment.

Comprehensive analyses of partially methylated domains and differentially methylated regions in esophageal cancer reveal both cell-type- and cancer-specific epigenetic regulation

BACKGROUND

As one of the most common malignancies, esophageal cancer has two subtypes, squamous cell carcinoma and adenocarcinoma, arising from distinct cells-of-origin. Distinguishing cell-type-specific molecular features from cancer-specific characteristics is challenging.

RESULTS

We analyze whole-genome bisulfite sequencing data on 45 esophageal tumor and nonmalignant samples from both subtypes. We develop a novel sequence-aware method to identify large partially methylated domains (PMDs), revealing profound heterogeneity at both methylation level and genomic distribution of PMDs across tumor samples. We identify subtype-specific PMDs that are associated with repressive transcription, chromatin B compartments and high somatic mutation rate. While genomic locations of these PMDs are pre-established in normal cells, the degree of loss is significantly higher in tumors. We find that cell-type-specific deposition of H3K36me2 may underlie genomic distribution of PMDs. At a smaller genomic scale, both cell-type- and cancer-specific differentially methylated regions (DMRs) are identified for each subtype. Using binding motif analysis within these DMRs, we show that a cell-type-specific transcription factor HNF4A maintains the binding sites that it generates in normal cells, while establishing new binding sites cooperatively with novel partners such as FOSL1 in esophageal adenocarcinoma. Finally, leveraging pan-tissue single-cell and pan-cancer epigenomic datasets, we demonstrate that a substantial fraction of cell-type-specific PMDs and DMRs identified here in esophageal cancer are actually markers that co-occur in other cancers originating from related cell types.

CONCLUSIONS

These findings advance our understanding of DNA methylation dynamics at various genomic scales in normal and malignant states, providing novel mechanistic insights into cell-type- and cancer-specific epigenetic regulations.

Construction of stemness gene score by bulk and single-cell transcriptome to characterize the prognosis of breast cancer

Breast cancer (BC) is a heterogeneous disease characterized by significant differences in prognosis and therapy response. Numerous prognostic tools have been developed for breast cancer. Usually these tools are based on bulk RNA-sequencing (RNA-Seq) and ignore tumor heterogeneity. Consequently, the goal of this study was to construct a single-cell level tool for predicting the prognosis of BC patients. In this study, we constructed a stemness-risk gene score (SGS) model based on single-sample gene set enrichment analysis (ssGSEA). Patients were divided into two groups based on the median SGS. Patients with a high SGS scores had a significantly worse prognosis than those with a low SGS, and these groups exhibited differences in several tumor characteristics, such as immune infiltration, gene mutations, and copy number variants. Our results indicate that the SGS is a reliable tool for predicting prognosis and response to immunotherapy in BC patients.

YWHAZ and TBP are potential reference gene candidates for qPCR analysis of response to radiation therapy in colorectal cancer

The expression profiles of conventional reference genes (RGs), including ACTB and GAPDH, used in quantitative real-time PCR (qPCR), vary depending on tissue types and environmental conditions. We searched for suitable RGs for qPCR to determine the response to radiotherapy in colorectal cancer (CRC) cell lines, organoids, and patient-derived tissues. Ten CRC cell lines (Caco-2, COLO 205, DLD-1, HCT116, HCT-15, HT-29, RKO, SW1116, SW480, and SW620) and organoids were selected and irradiated with 2, 10 or 21 grays (Gy) based on the previous related studies conducted over the last decade. The expression stability of 14 housekeeping genes (HKGs; ACTB, B2M, G6PD, GAPDH, GUSB, HMBS, HPRT1, IPO8, PGK1, PPIA, TBP, TFRC, UBC, and YWHAZ) after irradiation was evaluated using RefFinder using raw quantification cycle (Cq) values obtained from samples before and after irradiation. The expression stability of HKGs were also evaluated for paired fresh frozen tissues or formalin-fixed, paraffin-embedded samples obtained from CRC patients before and after chemoradiotherapy. The expression of YWHAZ and TBP encoding 14-3-3-zeta protein and TATA-binding protein were more stable than the other 12 HKGs in CRC cell lines, organoids, and patient-derived tissues after irradiation. The findings suggest that YWHAZ and TBP are potential RG candidates for normalizing qPCR results in CRC radiotherapy experiments.

Noncanonical HPV carcinogenesis drives radiosensitization of head and neck tumors

We analyzed transcriptional data from 104 HPV+ (Human papillomavirus) HNSCC (head and neck squamous cell carcinoma) tumors together with two publicly available sources to identify highly robust transcriptional programs (modules) which could be detected consistently despite heterogeneous sequencing and quantification methodologies. Among 22 modules identified, we found a single module that naturally subclassifies HPV+ HNSCC tumors based on a bimodal pattern of gene expression, clusters all atypical features of HPV+ HNSCC biology into a single subclass, and predicts patient outcome in four independent cohorts. The subclass-defining gene set was strongly correlated with Nuclear factor kappa B (NF-κB) target expression. Tumors with high expression of this NF-κB module were rarely associated with activating PIK3CA alterations or viral integration, and also expressed higher levels of HPHPV E2 and had decreased APOBEC mutagenesis. Alternatively, they harbored inactivating alterations of key regulators of NF-κB, TNF receptor associated factor 3 (TRAF3), and cylindromatosis (CYLD), as well as retinoblastoma protein (RB1). HPV+ HNSCC cells in culture with experimental depletion of TRAF3 or CYLD displayed increased expression of the subclass-defining genes, as well as robust radio-sensitization, thus recapitulating both the tumor transcriptional state and improved treatment response observed in patient data. Across all gene sets investigated, methylation to expression correlations were the strongest for the subclass-defining, NF-κB-related genes. Increased tumor-infiltrating CD4+ T cells and increased Estrogen receptors alpha (ERα) expression were identified in NF-κB active tumors. Based on the relatively high rates of cure in HPV+ HNSCC, deintensification of therapy to reduce treatment-related morbidity is being studied at many institutions. Tumor subclassification based on oncogenic subtypes may help guide the selection of therapeutic intensity or modality for patients with HPV+ HNSCC.

A role for SETD2 loss in tumorigenesis through DNA methylation dysregulation

SETD2-dependent H3 Lysine-36 trimethylation (H3K36me3) has been recently linked to the deposition of de-novo DNA methylation. SETD2 is frequently mutated in cancer, however, the functional impact of SETD2 loss and depletion on DNA methylation across cancer types and tumorigenesis is currently unknown. Here, we perform a pan-cancer analysis and show that both SETD2 mutation and reduced expression are associated with DNA methylation dysregulation across 21 out of the 24 cancer types tested. In renal cancer, these DNA methylation changes are associated with altered gene expression of oncogenes, tumour suppressors, and genes involved in neoplasm invasiveness, including TP53, FOXO1, and CDK4. This suggests a new role for SETD2 loss in tumorigenesis and cancer aggressiveness through DNA methylation dysregulation. Moreover, using a robust machine learning methodology, we develop and validate a 3-CpG methylation signature which is sufficient to predict SETD2 mutation status with high accuracy and correlates with patient prognosis.

Independent Tissue-Based Biomarkers in Endometrioid Endometrial Cancer: Tumor Budding in Microsatellite Instability and WHO Grading in Copy-Number-Low Patients

The molecular characterization of endometrial endometrioid adenocarcinomas has provided major advances in its prognostic stratification. However, risk assessment of microsatellite instability (MSI) and copy-number (CN)-low cases remains a challenge. Thus, we aimed to identify tissue-based morphologic biomarkers that might help in the prognostic stratification of these cases. Histomorphologic parameters (WHO grading, tumor budding (TB), tumor-stroma ratio (as a quantitative description of stromal desmoplasia), tumor-infiltrating lymphocytes (TIL), "microcystic, elongated, fragmented" (MELF) pattern) were analyzed in resection specimens of the TCGA-UCEC cohort (n = 228). For each quantitative parameter, a two-tiered system was developed utilizing systematically determined cutoffs. Associations with survival outcomes were calculated in univariate and multivariate analysis and validated in two independent cohorts. In MSI tumors, only TB remained an independent prognostic factor. TB (≥3 buds/high-power field) was associated with inferior outcomes and with lymph node metastases. The prognostic significance of TB was confirmed in two validation cohorts. For CN-low tumors, established grading defined by the WHO was independently prognostic with inferior outcomes for high-grade tumors. The evaluation of TB might help in identifying MSI-patients with unfavorable prognosis who, e.g., could benefit from lymphadenectomy. WHO-based grading facilitates independent prognostic stratification of CN-low endometrioid adenocarcinomas. Therefore, we propose the utilization of TB and WHO-based grading, two tissue-based and easy-to-assess biomarkers, in MSI/CN-low endometrial carcinomas for improved clinical management.

PyInteraph2 and PyInKnife2 to Analyze Networks in Protein Structural Ensembles

Due to the complex nature of noncovalent interactions and their long-range effects, analyzing protein conformations using network theory can be enlightening. Protein Structure Networks (PSNs) provide a convenient formalism to study protein structures in relation to essential properties such as key residues for structural stability, allosteric communication, and the effects of modifications of the protein. PSNs can be defined according to very different principles, and the available tools have limitations in input formats, supported models, and version control. Other outstanding problems are related to the definition of network cutoffs and the assessment of the stability of the network properties. The protein science community could benefit from a common framework to carry out these analyses and make them easier to reproduce, reuse, and evaluate. We here provide two open-source software packages, PyInteraph2 and PyInKnife2, to implement and analyze PSNs in a reproducible and documented manner. PyInteraph2 interfaces with multiple formats for protein ensembles and incorporates different network models with the possibility of integrating them into a macronetwork and performing various downstream analyses, including hubs, connected components, and several other centrality measures, and visualizes the networks or further analyzes them thanks to compatibility with Cytoscape.PyInKnife2 that supports the network models implemented in PyInteraph2. It employs a jackknife resampling approach to estimate the convergence of network properties and streamline the selection of distance cutoffs. We foresee that the modular structure of the code and the supported version control system will promote the transition to a community-driven effort, boost reproducibility, and establish common protocols in the PSN field. As developers, we will guarantee the introduction of new functionalities and maintenance, assistance, and training of new contributors.

Comprehensive Immune Profiling Unveils a Subset of Leiomyosarcoma with "Hot" Tumor Immune Microenvironment

Purpose: To investigate the immune biomarker in Leiomyosarcoma (LMS), which is rare and recognized as an immune cold cancer showing a poor response rate (<10%) to immune checkpoint inhibitors (ICIs). However, durable response and clinical benefit to ICIs has been observed in a few cases of LMS, including, but not only, LMS with tertiary lymphoid structure (TLS) structures. Patients and methods: We used comprehensive transcriptomic profiling and a deconvolution method extracted from RNA-sequencing gene expression data in two independent LMS cohorts, the International Cancer Genome Consortium (ICGC, N = 146) and The Cancer Genome Atlas (TCGA, N = 75), to explore tumor immune microenvironment (TIME) in LMS. Results: Unsupervised clustering analysis using the previously validated two methods, 90-gene signature and Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), identified immune hot (I-H) and immune high (I-Hi) LMS, respectively, in the ICGC cohort. Similarly, immune active groups (T-H, T-Hi) were identified in the TCGA cohort using these two methods. These immune active ("hot") clusters were significantly associated, but not completely overlapping, with several validated immune signatures such as sarcoma immune class (SIC) classification and TLS score, T cell inflamed signature (TIS) score, immune infiltration score (IIS), and macrophage score (M1/M2), with more patients identified by our clustering as potentially immune hot. Conclusions: Comprehensive immune profiling revealed a subset of LMS with a distinct active ("hot") TIME, consistently associated with several validated immune signatures in other cancers. This suggests that the methodologies that we used in this study warrant further validation and development, which can potentially help refine our current immune biomarkers to select the right LMS patients for ICIs in clinical trials.

Prognostic risk factors of serous ovarian carcinoma based on mesenchymal stem cell phenotype and guidance for therapeutic efficacy

BACKGROUND

Epithelial ovarian cancer is the leading cause of death from gynecologic cancer, in which serous ovarian carcinoma (SOC) is the most common histological subtype. Although PARP inhibitors (PARPi) and antiangiogenics have been accepted as maintenance treatment in SOC, response to immunotherapy of SOC patients is limited.

METHODS

The source of transcriptomic data of SOC was from the Cancer Genome Atlas database and Gene Expression Omnibus. The abundance scores of mesenchymal stem cells (MSC scores) were estimated for each sample by xCell. Weighted correlation network analysis is correlated the significant genes with MSC scores. Based on prognostic risk model construction with Cox regression analysis, patients with SOC were divided into low- and high-risk groups. And distribution of immune cells, immunosuppressors and pro-angiogenic factors in different risk groups was achieved by single-sample gene set enrichment analysis. The risk model of MSC scores was further validated in datasets of immune checkpoint blockade and antiangiogenic therapy. In the experiment, the mRNA expression of prognostic genes related to MSC scores was detected by real-time polymerase chain reaction, while the protein level was evaluated by immunohistochemistry.

RESULTS

Three prognostic genes (PER1, AKAP12 and MMP17) were the constituents of risk model. Patients classified as high-risk exhibited worse prognosis, presented with an immunosuppressive phenotype, and demonstrated high micro-vessel density. Additionally, these patients were insensitive to immunotherapy and would achieve a longer overall survival with antiangiogenesis treatment. The validation experiments showed that the mRNA of PER1, AKAP12, and MMP17 was highly expressed in normal ovarian epithelial cells compared to SOC cell lines and there was a positive correlation between protein levels of PER1, AKAP12 and MMP17 and metastasis in human ovarian serous tumors.

CONCLUSION

This prognostic model established on MSC scores can predict prognosis of patients and provide the guidance for patients receiving immunotherapy and molecular targeted therapy. Because the number of prognostic genes was fewer than other signatures of SOC, it will be easily accessible on clinic.

Network neighborhood operates as a drug repositioning method for cancer treatment

Computational drug repositioning approaches are important, as they cost less compared to the traditional drug development processes. This study proposes a novel network-based drug repositioning approach, which computes similarities between disease-causing genes and drug-affected genes in a network topology to suggest candidate drugs with highest similarity scores. This new method aims to identify better treatment options by integrating systems biology approaches. It uses a protein-protein interaction network that is the main topology to compute a similarity score between candidate drugs and disease-causing genes. The disease-causing genes were mapped on this network structure. Transcriptome profiles of drug candidates were taken from the LINCS project and mapped individually on the network structure. The similarity of these two networks was calculated by different network neighborhood metrics, including Adamic-Adar, PageRank and neighborhood scoring. The proposed approach identifies the best candidates by choosing the drugs with significant similarity scores. The method was experimented on melanoma, colorectal, and prostate cancers. Several candidate drugs were predicted by applying AUC values of 0.6 or higher. Some of the predictions were approved by clinical phase trials or other in-vivo studies found in literature. The proposed drug repositioning approach would suggest better treatment options with integration of functional information between genes and transcriptome level effects of drug perturbations and diseases.

Differential expression of the circadian clock network correlates with tumour progression in gliomas

BACKGROUND

Gliomas are tumours arising mostly from astrocytic or oligodendrocytic precursor cells. These tumours are classified according to the updated WHO classification from 2021 in 4 grades depending on molecular and histopathological criteria. Despite novel multimodal therapeutic approaches, the vast majority of gliomas (WHO grade III and IV) are not curable. The circadian clock is an important regulator of numerous cellular processes and its dysregulation had been found during the progression of many cancers, including gliomas.

RESULTS

In this study, we explore expression patterns of clock-controlled genes in low-grade glioma (LGG) and glioblastoma multiforme (GBM) and show that a set of 45 clock-controlled genes can be used to distinguish GBM from normal tissue. Subsequent analysis identified 17 clock-controlled genes with a significant association with survival. The results point to a loss of correlation strength within elements of the circadian clock network in GBM compared to LGG. We further explored the progression patterns of mutations in LGG and GBM, and showed that tumour suppressor APC is lost late both in LGG and GBM. Moreover, HIF1A, involved in cellular response to hypoxia, exhibits subclonal losses in LGG, and TERT, involved in the formation of telomerase, is lost late in the GBM progression. By examining multi-sample LGG data, we find that the clock-controlled driver genes APC, HIF1A, TERT and TP53 experience frequent subclonal gains and losses.

CONCLUSIONS

Our results show a higher level of disrgulation at the gene expression level in GBM compared to LGG, and indicate an association between the differentially expressed clock-regulated genes and patient survival in both LGG and GBM. By reconstructing the patterns of progression in LGG and GBM, our data reveals the relatively late gains and losses of clock-regulated glioma drivers. Our analysis emphasizes the role of clock-regulated genes in glioma development and progression. Yet, further research is needed to asses their value in the development of new treatments.

CITED1 as a marker of favourable outcome in anti-endocrine treated, estrogen-receptor positive, lymph-node negative breast cancer

OBJECTIVE

To investigate CITED1 as a potential biomarker of anti-endocrine response and breast cancer recurrence, given its previously determined role in mediating estrogen-dependant transcription. The study is a continuation of earlier work establishing the role of CITED1 in mammary gland development.

RESULTS

CITED1 mRNA is associated with estrogen-receptor positivity and selectively expressed in the GOBO dataset of cell lines and tumours representing the luminal-molecular subtype. In patients treated with tamoxifen, higher CITED1 correlated with better outcome, suggesting a role in anti-estrogen response. The effect was particularly evident in the subset of estrogen-receptor positive, lymph-node negative (ER+/LN-) patients although noticeable divergence of the groups was apparent only after five years. Tissue microarray (TMA) analysis further validated the association of CITED1 protein, by immunohistochemistry, with favourable outcome in ER+, tamoxifen-treated patients. Although we also found a favourable response to anti-endocrine treatment in a larger TCGA dataset, the tamoxifen-specific effect was not replicated. Finally, MCF7 cells overexpressing CITED1 showed selective amplification of AREG but not TGFα suggesting that maintenance of specific ERα-CITED1 mediated transcription is important for the long-term response to anti-endocrine therapy. These findings together confirm the proposed mechanism of action of CITED1 and support its potential use as a prognostic biomarker.

Iron-mediated oxidative stress induces PD-L1 expression via activation of c-Myc in lung adenocarcinoma

Introduction: The PD-1/PD-L1 axis is hijacked by lung adenocarcinoma (LUAD) cells to escape immune surveillance. PD-L1 expression in LUAD is affected, among others, by the metabolic trafficking between tumor cells and the tumor microenvironment (TME). Methods: Correlation between PD-L1 expression and iron content within the TME was established on FFPE LUAD tissue samples. The effects of an iron rich microenvironment on PD-L1 mRNA and protein levels were assessed in vitro in H460 and A549 LUAD by using qPCR, western blot and flow citometry. c-Myc knockdown was performed to validate the role of this transcription factor on PD-L1 expression. The effects of iron-induced PD-L1 on T cell immune function was assessed by quantifying IFN-γ release in a co-colture system. TCGA dataset was used to analyse the correlation between PD-L1 and CD71 mRNA expression in LUAD patients. Results: In this study, we highlight a significant correlation between iron density within the TME and PD-L1 expression in 16 LUAD tissue specimens. In agreement, we show that a more pronounced innate iron-addicted phenotype, indicated by a higher transferrin receptor CD71 levels, significantly correlates with higher PD-L1 mRNA expression levels in LUAD dataset obtained from TCGA database. In vitro, we demonstrate that the addition of Fe³⁺ within the culture media promotes the significant overexpression of PD-L1 in A549 and H460 LUAD cells, through the modulation of its gene transcription mediated by c-Myc. The effects of iron lean on its redox activity since PD-L1 up-regulation is counteracted by treatment with the antioxidant compound trolox. When LUAD cells are co-cultured with CD3/CD28-stimulated T cells in an iron-rich culture condition, PD-L1 up-regulation causes the inhibition of T-lymphocytes activity, as demonstrated by the significant reduction of IFN-γ release. Discussion: Overall, in this study we demonstrate that iron abundance within the TME may enhance PD-L1 expression in LUAD and, thus, open the way for the identification of possible combinatorial strategies that take into account the iron levels within the TME to improve the outcomes of LUAD patients treated with anti-PD-1/PD-L1-based therapies.

Identification and validation of a novel cuproptosis-related stemness signature to predict prognosis and immune landscape in lung adenocarcinoma by integrating single-cell and bulk RNA-sequencing

Background

Cancer stem cells (CSCs) play vital roles in lung adenocarcinoma (LUAD) recurrence, metastasis, and drug resistance. Cuproptosis has provided a novel insight into the treatment of lung CSCs. However, there is a lack of knowledge regarding the cuproptosis-related genes combined with the stemness signature and their roles in the prognosis and immune landscape of LUAD.

Methods

Cuproptosis-related stemness genes (CRSGs) were identified by integrating single-cell and bulk RNA-sequencing data in LUAD patients. Subsequently, cuproptosis-related stemness subtypes were classified using consensus clustering analysis, and a prognostic signature was constructed by univariate and least absolute shrinkage operator (LASSO) Cox regression. The association between signature with immune infiltration, immunotherapy, and stemness features was also investigated. Finally, the expression of CRSGs and the functional roles of target gene were validated in vitro.

Results

We identified six CRSGs that were mainly expressed in epithelial and myeloid cells. Three distinct cuproptosis-related stemness subtypes were identified and associated with the immune infiltration and immunotherapy response. Furthermore, a prognostic signature was constructed to predict the overall survival (OS) of LUAD patients based on eight differently expressed genes (DEGs) with cuproptosis-related stemness signature (KLF4, SCGB3A1, COL1A1, SPP1, C4BPA, TSPAN7, CAV2, and CTHRC1) and confirmed in validation cohorts. We also developed an accurate nomogram to improve clinical applicability. Patients in the high-risk group showed worse OS with lower levels of immune cell infiltration and higher stemness features. Ultimately, further cellular experiments were performed to verify the expression of CRSGs and prognostic DEGs and demonstrate that SPP1 could affect the proliferation, migration, and stemness of LUAD cells.

Conclusion

This study developed a novel cuproptosis-related stemness signature that can be used to predict the prognosis and immune landscape of LUAD patients, and provided potential therapeutic targets for lung CSCs in the future.

COL7A1 Expression Improves Prognosis Prediction for Patients with Clear Cell Renal Cell Carcinoma Atop of Stage

Clear-cell renal cell carcinoma (ccRCC) accounts for 75% of kidney cancers. Due to the high recurrence rate and treatment options that come with high costs and potential side effects, a correct prognosis of patient survival is essential for the successful and effective treatment of patients. Novel biomarkers could play an important role in the assessment of the overall survival of patients. COL7A1 encodes for collagen type VII, a constituent of the basal membrane. COL7A1 is associated with survival in many cancers; however, the prognostic value of COL7A1 expression as a standalone biomarker in ccRCC has not been investigated. With five publicly available independent cohorts, we used Kaplan-Meier curves and the Cox proportional hazards model to investigate the prognostic value of COL7A1, as well as gene set enrichment analysis to investigate genes co-expressed with COL7A1. COL7A1 expression stratifies patients in terms of aggressiveness, where the 5-year survival probability of each of the four groups was 72.4%, 59.1%, 34.15%, and 8.6% in order of increasing expression. Additionally, COL7A1 expression was successfully used to further divide patients of each stage and histological grade into groups of high and low risk. Similar results were obtained in independent cohorts. In vitro knockdown of COL7A1 expression significantly affected ccRCC cells' ability to migrate, leading to the hypothesis that COL7A1 may have a role in cancer aggressiveness. To conclude, we identified COL7A1 as a new prognosis marker that can stratify ccRCC patients.

A Tumor Microenvironment-Driven Network Regulated by STAT3 and p65 Negatively Controls the Enrichment of Cancer Stem Cells in Human HR+/HER2- Breast Cancer

Hormone receptor-positive and HER2-negative (HR+/HER2-; luminal A) tumors are prevalent in breast cancer. Our past studies demonstrated that "TME Stimulation" (estrogen + TNFα + EGF, representing three arms of the tumor microenvironment, TME) has enriched metastasis-forming cancer stem cells (CSCs) in HR+/HER2- human breast cancer cells. Here, following information obtained by RNAseq analyses of TME-stimulated CSCs and Non-CSCs, we found that TME Stimulation has induced the activation of S727-STAT3, Y705-STAT3, STAT1 and p65. Upon TME Stimulation, stattic (STAT3 inhibitor) usage demonstrated that Y705-STAT3 activation negatively controlled CSC enrichment and epithelial-to-mesenchymal transition (EMT) traits, while inducing CXCL8 (IL-8) and PD-L1 expression. However, STAT3 knock-down (siSTAT3) had no effect on these functions; in terms of CSC enrichment, p65 had down-regulatory roles that compensated for the loss of an entire STAT3 protein. Y705-STAT3 and p65 acted additively in reducing CSC enrichment, and Y705A-STAT3 variant + sip65 has enriched chemo-resistant CSCs. Clinical data analyses revealed an inverse correlation between Y705-STAT3 + p65 phosphorylation and CSC signature in luminal A patients, and connection to improved disease course. Overall, we find regulatory roles for Y705-STAT3 and p65 in TME-stimulated HR+/HER2- tumors, with the ability to limit CSC enrichment. These findings raise concerns about using inhibitors of STAT3 and p65 as therapeutic strategies in the clinic.

OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features

Late-stage drug development failures are usually a consequence of ineffective targets. Thus, proper target identification is needed, which may be possible using computational approaches. The reason being, effective targets have disease-relevant biological functions, and omics data unveil the proteins involved in these functions. Also, properties that favor the existence of binding between drug and target are deducible from the protein’s amino acid sequence. In this work, we developed OncoRTT, a deep learning (DL)-based method for predicting novel therapeutic targets. OncoRTT is designed to reduce suboptimal target selection by identifying novel targets based on features of known effective targets using DL approaches. First, we created the “OncologyTT” datasets, which include genes/proteins associated with ten prevalent cancer types. Then, we generated three sets of features for all genes: omics features, the proteins’ amino-acid sequence BERT embeddings, and the integrated features to train and test the DL classifiers separately. The models achieved high prediction performances in terms of area under the curve (AUC), i.e., AUC greater than 0.88 for all cancer types, with a maximum of 0.95 for leukemia. Also, OncoRTT outperformed the state-of-the-art method using their data in five out of seven cancer types commonly assessed by both methods. Furthermore, OncoRTT predicts novel therapeutic targets using new test data related to the seven cancer types. We further corroborated these results with other validation evidence using the Open Targets Platform and a case study focused on the top-10 predicted therapeutic targets for lung cancer.

Tumor MK2 transcript levels are associated with improved response to chemotherapy and patient survival in non-small cell lung cancer

Non-small cell lung cancers (NSCLCs) demonstrate intrinsic resistance to cell death, even after chemotherapy. Previous work suggested defective nuclear translocation of active caspase-3 in observed resistance to cell death. We have identified mitogen-activated protein kinase-activated protein kinase 2 (MK2; encoded by the gene MAPKAPK2) is required for caspase-3 nuclear translocation in the execution of apoptosis in endothelial cells. The objective was to determine MK2 expression in NSCLCs and the association between MK2 and clinical outcomes in patients with NSCLC. Clinical and MK2 mRNA data were extracted from two demographically distinct NSCLC clinical cohorts, North American (The Cancer Genome Atlas, TCGA) and East Asian (EA). Tumor responses following first round of chemotherapy were dichotomized as clinical response (complete response, partial response, and stable disease) or progression of disease. Multivariable survival analyses were performed using Cox proportional hazard ratios and Kaplan-Meier curves. NSCLC exhibited lower MK2 expression than SCLC cell lines. In patients, lower tumor MK2 transcript levels were observed in those presenting with late-stage NSCLC. Higher MK2 expression was associated with clinical response following initial chemotherapy and independently associated with improved 2-yr survival in two distinct cohorts, 0.52 (0.28-0.98) and 0.1 (0.01-0.81), TCGA and EA, respectively, even after adjusting for common oncogenic driver mutations. Survival benefit of higher MK2 expression was unique to lung adenocarcinoma when comparing across various cancers. This study implicates MK2 in apoptosis resistance in NSCLC and suggests prognostic value of MK2 transcript levels in patients with lung adenocarcinoma.

CRISPR-induced exon skipping of β-catenin reveals tumorigenic mutants driving distinct subtypes of liver cancer

CRISPR/Cas9-driven cancer modeling studies are based on the disruption of tumor suppressor genes by small insertions or deletions (indels) that lead to frame-shift mutations. In addition, CRISPR/Cas9 is widely used to define the significance of cancer oncogenes and genetic dependencies in loss-of-function studies. However, how CRISPR/Cas9 influences gain-of-function oncogenic mutations is elusive. Here, we demonstrate that single guide RNA targeting exon 3 of Ctnnb1 (encoding β-catenin) results in exon skipping and generates gain-of-function isoforms in vivo. CRISPR/Cas9-mediated exon skipping of Ctnnb1 induces liver tumor formation in synergy with YAPS127A in mice. We define two distinct exon skipping-induced tumor subtypes with different histological and transcriptional features. Notably, ectopic expression of two exon-skipped β-catenin transcript isoforms together with YAPS127A phenocopies the two distinct subtypes of liver cancer. Moreover, we identify similar CTNNB1 exon-skipping events in patients with hepatocellular carcinoma. Collectively, our findings advance our understanding of β-catenin-related tumorigenesis and reveal that CRISPR/Cas9 can be repurposed, in vivo, to study gain-of-function mutations of oncogenes in cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Th2 cells infiltrating high-grade serous ovarian cancer: a feature that may account for the poor prognosis

OBJECTIVE

We aimed to investigate the differences of transcriptome profile between 2 groups of high-grade serous ovarian cancer (HGSOC) patients with distinct outcomes and identify potential biomarkers for recurrence.

METHODS

RNA sequencing was performed in 2 groups of HGSOC patients with similar demographic characteristics but exhibiting distinct progression-free survival (PFS). Transcriptome data of poor response (PR; PFS ≤6 months) and good response (GR; PFS ≥12 months) group were compared. We employed xCell to evaluate the abundance of 63 cells in tumor microenvironment. The predictive value of recurrence-related tumor infiltration cells was validated in cohort data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) dataset. The weighted correlation network analysis was performed to identify the genes related to cell infiltration.

RESULTS

PR patients exhibited a distinct tumor infiltration immune cells-related transcriptional profile compared to GR patients, such as lower signatures of leukocyte differentiation, activation and chemotaxis. The fraction of T-helper 2 (Th2) cells infiltration was significantly higher in PR group than in GR group. High infiltration of Th2 was significantly associated with unfavorable prognosis in the GEO cohort (area under the curve=0.84 at 6 months recurrence) and TCGA cohort (p=0.008). Genes enriched to extracellular matrix organization and integrin binding were relevant to Th2 infiltration.

CONCLUSION

Patients with HGSOC having shorter PFS exhibited a distinct gene signature that related to tumor-infiltrating immune cells. The level of Th2 infiltration could facilitate patient recurrence risk stratification and may be a promising biomarker for prognosis prediction and immune-related treatment.

Attenuation of cancer proliferation by suppression of glypican-1 and its pleiotropic effects in neoplastic behavior

Glypicans (GPC1-6) are associated with tumorigenic processes and their involvement in neoplastic behavior has been discussed in different cancer types. Here, a cancer-wide GPC expression study, using clinical cancer patient data in The Cancer Genome Atlas, reveals net upregulation of GPC1 and GPC2 in primary solid tumors, whereas GPC3, GPC5 and GPC6 display lowered expression pattern compared to normal tissues. Focusing on GPC1, survival analyses of the clinical cancer patient data reveal statistically significant correlation between high expression of GPC1 and poor prognosis in 10 particular cancer types i.e., bladder urothelial carcinoma, brain lower grade glioma, liver hepatocellular carcinoma, colon adenocarcinoma, kidney renal clear cell carcinoma, lung adenocarcinoma, mesothelioma, ovarian serous cystadenocarcinoma, uterine corpus endometrial carcinoma and uveal melanoma. In vitro studies targeting GPC1 expression by CRISPR/Cas9 or siRNA or treatment with an anti-GPC1 antibody resulted in attenuation of proliferation of cancer cells from bladder carcinoma, glioma and hepatocellular carcinoma patients (T24, U87 and HepG2 cells). Further, overexpression of GPC1 exhibited a significant and negative correlation between GPC1 expression and proliferation of T24 cells. Attempt to reveal the mechanism through which downregulation of GPC1 leads to attenuation of tumor growth using systematic Ingenuity Pathway Analysis indicate that suppression of GPC1 results in ECM-mediated inhibition of specific pro-cancer signaling pathways involving TGF-β and p38 MAPK. Identified differential expression and pleiotropic effects of GPCs in specific cancer types emphasize their potential of as novel diagnostic tools and prognostic factors and open doors for future GPC targeted therapy.

Single-cell profiling of the copy-number heterogeneity in colorectal cancer

BACKGROUND

With functionally heterogeneous cells, tumors comprise a complex ecosystem to promote tumor adaptability and evolution under strong selective pressure from the given microenvironment. Diversifying tumor cells or intra-tumor heterogeneity is essential for tumor growth, invasion, and immune evasion. However, no reliable method to classify tumor cell subtypes is yet available. In this study, we introduced the single-cell sequencing combined with copy number characteristics to identify the types of tumor cells in microsatellite stable (MSS) colorectal cancer (CRC).

METHODS

To characterize the somatic copy number alteration (SCNA) of MSS CRC in a single cell profile, we analyzed 26 tissue samples from 19 Korean patients (GSE132465, the Samsung Medical Center [SMC] dataset) and then verified our findings with 15 tissue samples from five Belgian patients (GSE144735, the Katholieke Universiteit Leuven 3 [KUL3] dataset). The Cancer Genome Atlas (TCGA) cohort, GSE39582 cohort, and National Cancer Center (NCC) cohort (24 MSS CRC patients were enrolled in this study between March 2017 and October 2017) were used to validate the clinical features of prognostic signatures.

RESULTS

We employed single cell RNA-sequencing data to identify three types of tumor cells in MSS CRC by their SCNA characteristics. Among these three types of tumor cells, C1 and C3 had a higher SCNA burden; C1 had significant chromosome 13 and 20 amplification, whereas C3 was the polar opposite of C1, which exhibited deletion in chromosome 13 and 20. The three types of tumor cells exhibited various functions in the tumor microenvironment and harbored different mutations. C1 and C2 were linked to the immune response and hypoxia, respectively, while C3 was critical for cell adhesion activity and tumor angiogenesis. Additionally, one gene ( OLFM4 ) was identified as epithelium-specific biomarker of better prognosis of CRC (TCGA cohort: P  = 0.0110; GSE39582 cohort: P  = 0.0098; NCC cohort: P  = 0.0360).

CONCLUSIONS

On the basis of copy number characteristics, we illustrated tumor heterogeneity in MSS CRC and identified three types of tumor cells with distinct roles in tumor microenvironment. By understanding heterogeneity in the intricate tumor microenvironment, we gained an insight into the mechanisms of tumor evolution, which may support the development of therapeutic strategies.

The Roles of Secreted Wnt Ligands in Cancer

Wnt ligands are secreted signaling proteins that display a wide range of biological effects. They play key roles in stimulating Wnt signaling pathways to facilitate processes such as tissue homeostasis and regeneration. Dysregulation of Wnt signaling is a hallmark of many cancers and genetic alterations in various Wnt signaling components, which result in ligand-independent or ligand-dependent hyperactivation of the pathway that have been identified. Recently, research is focusing on the impact of Wnt signaling on the interaction between tumor cells and their micro-environment. This Wnt-mediated crosstalk can act either in a tumor promoting or suppressing fashion. In this review, we comprehensively outline the function of Wnt ligands in different tumor entities and their impact on key phenotypes, including cancer stemness, drug resistance, metastasis, and immune evasion. Lastly, we elaborate approaches to target Wnt ligands in cancer therapy.

A compilation of bioinformatic approaches to identify novel downstream targets for the detection and prophylaxis of cancer

The paradigm of cancer genomics has been radically changed by the development in next-generation sequencing (NGS) technologies making it possible to envisage individualized treatment based on tumor and stromal cells genome in a clinical setting within a short timeframe. The abundance of data has led to new avenues for studying coordinated alterations that impair biological processes, which in turn has increased the demand for bioinformatic tools for pathway analysis. While most of this work has been concentrated on optimizing certain algorithms to obtain quicker and more accurate results. Large volumes of these existing algorithm-based data are difficult for the biologists and clinicians to access, download and reanalyze them. In the present study, we have listed the bioinformatics algorithms and user-friendly graphical user interface (GUI) tools that enable code-independent analysis of big data without compromising the quality and time. We have also described the advantages and drawbacks of each of these platforms. Additionally, we emphasize the importance of creating new, more user-friendly solutions to provide better access to open data and talk about relevant problems like data sharing and patient privacy.

NMSDR: Drug repurposing approach based on transcriptome data and network module similarity

Computational drug repurposing aims to discover new treatment regimens by analyzing approved drugs on the market. This study proposes previously approved compounds that can change the expression profile of disease-causing proteins by developing a network theory-based drug repurposing approach. The novelty of the proposed approach is an exploration of module similarity between a disease-causing network and a compound-specific interaction network; thus, such an association leads to more realistic modeling of molecular cell responses at a system biology level. The overlap of the disease network and each compound-specific network is calculated based on a shortest-path similarity of networks by accounting for all protein pairs between networks. A higher similarity score indicates a significant potential of a compound. The approach was validated for breast and lung cancers. When all compounds are sorted by their normalized-similarity scores, 36 and 16 drugs are proposed as new candidates for breast and lung cancer treatment, respectively. A literature survey on candidate compounds revealed that some of our predictions have been clinically investigated in phase II/III trials for the treatment of two cancer types. As a summary, the proposed approach has provided promising initial results by modeling biochemical cell responses in a network-level data representation.

Prognostic Significance of Amino Acid Metabolism-Related Genes in Prostate Cancer Retrieved by Machine Learning

Prostate cancer is among the leading cancers according to both incidence and mortality. Due to the high molecular, morphological and clinical heterogeneity, the course of prostate cancer ranges from slow growth that usually does not require immediate therapeutic intervention to aggressive and fatal disease that spreads quickly. However, currently available biomarkers cannot precisely predict the course of a disease, and novel strategies are needed to guide prostate cancer management. Amino acids serve numerous roles in cancers, among which are energy production, building block reservoirs, maintenance of redox homeostasis, epigenetic regulation, immune system modulation and resistance to therapy. In this article, by using The Cancer Genome Atlas (TCGA) data, we found that the expression of amino acid metabolism-related genes is highly aberrant in prostate cancer, which holds potential to be exploited in biomarker design or in treatment strategies. This change in expression is especially evident for catabolism genes and transporters from the solute carrier family. Furthermore, by using recursive partitioning, we confirmed that the Gleason score is strongly prognostic for progression-free survival. However, the expression of the genes SERINC3 (phosphatidylserine and sphingolipids generation) and CSAD (hypotaurine generation) can refine prognosis for high and low Gleason scores, respectively. Therefore, our results hold potential for novel prostate cancer progression biomarkers.

Open-source curation of a pancreatic ductal adenocarcinoma gene expression analysis platform (pdacR) supports a two-subtype model

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease for which potent therapies have limited efficacy. Several studies have described the transcriptomic landscape of PDAC tumors to provide insight into potentially actionable gene expression signatures to improve patient outcomes. Despite centralization efforts from multiple organizations and increased transparency requirements from funding agencies and publishers, analysis of public PDAC data remains difficult. Bioinformatic pitfalls litter public transcriptomic data, such as subtle inclusion of low-purity and non-adenocarcinoma cases. These pitfalls can introduce non-specificity to gene signatures without appropriate data curation, which can negatively impact findings. To reduce barriers to analysis, we have created pdacR ( http://pdacR.bmi.stonybrook.edu , github.com/rmoffitt/pdacR), an open-source software package and web-tool with annotated datasets from landmark studies and an interface for user-friendly analysis in clustering, differential expression, survival, and dimensionality reduction. Using this tool, we present a multi-dataset analysis of PDAC transcriptomics that confirms the basal-like/classical model over alternatives.

Na, K-ATPase α1 cooperates with its endogenous ligand to reprogram immune microenvironment of lung carcinoma and promotes immune escape

Dysregulated endocrine hormones (EHs) contribute to tumorigenesis, but how EHs affect the tumor immune microenvironment (TIM) and the immunotherapy of non-small cell lung cancer (NSCLC) is still unclear. Here, endogenous ouabain (EO), an adrenergic hormone, is elevated in patients with NSCLC and closely related to tumor pathological stage, metastasis, and survival. EO promotes the suppression of TIM in vivo by modulating the expression of immune checkpoint proteins, in which programmed cell death protein ligand 1 (PD-L1) plays a major role. EO increases PD-L1 transcription; however, the EO receptor Na- and K-dependent adenosine triphosphatase (Na, K-ATPase) α1 interacts with PD-L1 to trigger the endocytic degradation of PD-L1. This seemingly contradictory result led us to discover the mechanism whereby EO cooperates with Na, K-ATPase α1 to finely control PD-L1 expression and dampen tumoral immunity. In conclusion, the Na, K-ATPase α1/EO signaling facilitates immune escape in lung cancer, and manipulation of this signaling shows great promise in improving immunotherapy for lung adenocarcinoma.

Hypoxia promotes EV secretion by impairing lysosomal homeostasis in HNSCC through negative regulation of ATP6V1A by HIF-1α

Tumour cells under hypoxia tend to modulate the number and contents of extracellular vesicles (EVs) to regulate the tumour microenvironment (TME) and thus promote tumour progression. However, the mechanism of how hypoxia influences the secretion of EVs remains to be elucidated. Here, we confirm the increased production of EVs in head and neck squamous cell carcinoma (HNSCC) cells under hypoxia, where endosome-derived EVs are the main subtype affected by insufficient O2 . The accumulation of hypoxia-inducible factor-1α (HIF-1α) under hypoxia directly downregulates the expression of ATP6V1A, which is pivotal to maintain the homeostasis of lysosomes. Subsequently, impaired lysosomal degradation contributes to the reduced fusion of multivesicular bodies (MVBs) with lysosomes and enables the secretion of intraluminal vesicles (ILVs) as EVs. These findings establish a HIF-1α-regulated lysosomal dysfunction-EV release axis and provide an exquisite framework to better understand EV biogenesis.

Dissecting the role of cancer-associated fibroblast-derived biglycan as a potential therapeutic target in immunotherapy resistance: A tumor bulk and single-cell transcriptomic study

INTRODUCTION

Cancer-associated fibroblasts (CAFs) are correlated with the immunotherapy response. However, the culprits that link CAFs to immunotherapy resistance are still rarely investigated in real-world studies.

OBJECTIVES

This study aims to systematically assess the landscape of fibroblasts in cancer patients by combining single-cell and bulk profiling data from pan-cancer cohorts. We further sought to decipher the expression, survival predictive value and association with immunotherapy response of biglycan (BGN), a proteoglycan in the extracellular matrix, in multiple cohorts.

METHODS

Pan-cancer tumor bulks and 27 single-cell RNA sequencing cohorts were enrolled to investigate the correlations and crosstalk between CAFs and tumor or immune cells. Specific secreting factors of CAFs were then identified by expression profiling at tissue microdissection, isolated primary fibroblasts and single-cell level. The role of BGN was further dissected in additional three bulk and five single-cell profiling datasets from immunotherapy cohorts and validated in real-world patients who have received PD-1 blockade using immunohistochemistry and immunofluorescence.

RESULTS

CAFs were closely correlated with immune components. Frequent crosstalk between CAFs and other cells was revealed by the CellChat analysis. Single-cell regulatory network inference and clustering identified common and distinct regulators for CAFs across cancers. The BGN was determined to be a specific secreting factor of CAFs. The BGN served as an unfavourable indicator for overall survival and immunotherapy response. In the real-world immunotherapy cohort, patients with high BGN levels presented a higher proportion of poor response compared with those with low BGN (46.7% vs. 11.8%) and a lower level of infiltrating CD8+ T cells was also observed.

CONCLUSIONS

We highlighted the importance of CAFs in the tumor microenvironment and revealed that the BGN, which is mainly derived from CAFs, may be applicable in clinical practice and serve as a therapeutic target in immunotherapy resistance.

Increased levels of microRNA-320 in blood serum and plasma is associated with imminent and advanced lung cancer

Lung cancer (LC) incidence is increasing globally and altered levels of microRNAs (miRNAs) in blood may contribute to identification of individuals with LC. We identified miRNAs differentially expressed in peripheral blood at LC diagnosis and evaluated, in pre-diagnostic blood specimens, how long before diagnosis expression changes in such candidate miRNAs could be detected. We identified upregulated candidate miRNAs in plasma specimens from a hospital-based study sample of 128 patients with confirmed LC and 62 individuals with suspected but confirmed negative LC (FalsePos). We then evaluated the expression of candidate miRNAs in pre-diagnostic plasma or serum specimens of 360 future LC cases and 375 matched controls. There were 1663 miRNAs detected in diagnostic specimens, nine of which met our criteria for candidate miRNAs. Higher expression of three candidates, miR-320b, 320c, and 320d, was associated with poor survival, independent of LC stage and subtype. Moreover, miR-320c and miR-320d expression was higher in pre-diagnostic specimens collected within 2 years of LC diagnosis. Our results indicated that elevated levels of miR-320c and miR-320d may be early indications of imminent and advanced LC.

GSNOR deficiency promotes tumor growth via FAK1 S-nitrosylation

Nitric oxide (NO) production in the tumor microenvironment is a common element in cancer. S-nitrosylation, the post-translational modification of cysteines by NO, is emerging as a key transduction mechanism sustaining tumorigenesis. However, most oncoproteins that are regulated by S-nitrosylation are still unknown. Here we show that S-nitrosoglutathione reductase (GSNOR), the enzyme that deactivates S-nitrosylation, is hypo-expressed in several human malignancies. Using multiple tumor models, we demonstrate that GSNOR deficiency induces S-nitrosylation of focal adhesion kinase 1 (FAK1) at C658. This event enhances FAK1 autophosphorylation and sustains tumorigenicity by providing cancer cells with the ability to survive in suspension (evade anoikis). In line with these results, GSNOR-deficient tumor models are highly susceptible to treatment with FAK1 inhibitors. Altogether, our findings advance our understanding of the oncogenic role of S-nitrosylation, define GSNOR as a tumor suppressor, and point to GSNOR hypo-expression as a therapeutically exploitable vulnerability in cancer.

The effects of altered DNA damage repair genes on mutational processes and immune cell infiltration in esophageal squamous cell carcinoma

BACKGROUND

Defects in DNA damage repair (DDR) pathways lead to genomic instability and oncogenesis. DDR deficiency is prevalent in esophageal squamous cell carcinoma (ESCC), but the effects of DDR alterations on mutational processes and tumor immune microenvironment in ECSS remain unclear.

METHODS

Whole-exome and transcriptome sequencing data of 45 ESCC samples from Taizhou, China, were used to identify genomic variations, gene expression modulation in DDR pathways, and the abundance of tumor-infiltrating immune cells. Ninety-six ESCC cases from The Cancer Genome Atlas (TCGA) project were used for validation.

RESULTS

A total of 57.8% (26/45) of the cases in the Taizhou data and 70.8% (68/96) of the cases in the TCGA data carried at least one functional impact DDR mutation. Mutations in the DDR pathways were associated with a high tumor mutation burden. Several DDR deficiency-related mutational signatures were discovered and were associated with immune cell infiltration, including T cells, monocytes, dendritic cells, and mast cells. The expression levels of two DDR genes, HFM1 and NEIL1, were downregulated in ESCC tumor tissues and had an independent effect on the infiltration of mast cells. In the Taizhou data, increased expression of HFM1 was associated with a poor prognosis, and the increased expression of NEIL1 was associated with a good outcome, but no reproducible correlation was observed in the TCGA data.

CONCLUSION

This research demonstrated that DDR alterations could impact mutational processes and immune cell infiltration in ESCC. The suppression of HFM1 and NEIL1 could play a crucial role in ESCC progression and may also serve as prognostic markers.

Tumor Microenvironment and Genes Affecting the Prognosis of Temozolomide-Treated Glioblastoma

Glioblastoma (GBM) is the most frequent primary brain tumor in adults and has a poor prognosis due to its resistance to Temozolomide (TMZ). However, there is limited research regarding the tumor microenvironment and genes related to the prognosis of TMZ-treated GBM patients. This study aimed to identify putative transcriptomic biomarkers with predictive value in patients with GBM who were treated with TMZ. Publicly available datasets from The Cancer Genome Atlas and Gene Expression Omnibus were analyzed using CIBERSORTx and Weighted Gene Co-expression Network Analysis (WGCNA) to obtain types of highly expressed cell types and gene clusters. Differentially Expressed Genes analysis was performed and was intersected with the WGCNA results to obtain a candidate gene list. Cox proportional-hazard survival analysis was performed to acquire genes related to the prognosis of TMZ-treated GBM patients. Inflammatory microglial cells, dendritic cells, myeloid cells, and glioma stem cells were highly expressed in GBM tissue, and ACP7, EPPK1, PCDHA8, RHOD, DRC1, ZIC3, and PRLR were significantly associated with survival. While the listed genes have been previously reported to be related to glioblastoma or other types of cancer, ACP7 was identified as a novel gene related to the prognosis of GBM. These findings may have potential implications for developing a diagnostic tool to predict GBM resistance and optimize treatment decisions.

Arginine limitation drives a directed codon-dependent DNA sequence evolution response in colorectal cancer cells

Utilization of specific codons varies between organisms. Cancer represents a model for understanding DNA sequence evolution and could reveal causal factors underlying codon evolution. We found that across human cancer, arginine codons are frequently mutated to other codons. Moreover, arginine limitation-a feature of tumor microenvironments-is sufficient to induce arginine codon-switching mutations in human colon cancer cells. Such DNA codon switching events encode mutant proteins with arginine residue substitutions. Mechanistically, arginine limitation caused rapid reduction of arginine transfer RNAs and the stalling of ribosomes over arginine codons. Such selective pressure against arginine codon translation induced an adaptive proteomic shift toward low-arginine codon-containing genes, including specific amino acid transporters, and caused mutational evolution away from arginine codons-reducing translational bottlenecks that occurred during arginine starvation. Thus, environmental availability of a specific amino acid can influence DNA sequence evolution away from its cognate codons and generate altered proteins.

Development of a novel pyroptosis-related LncRNA signature with multiple significance in acute myeloid leukemia

Background: Pyroptosis, a programmed cell death (PCD) with highly inflammatory form, has been recently found to be associated with the origin of hematopoietic malignancies. Long noncoding RNA (lncRNA) had emerged as an essential mediator to regulate gene expression and been involved in oncogenesis. However, the roles of pyroptosis-related lncRNA (PRlncRNA) in acute myeloid leukemia (AML) have not yet been completely clarified. Methods: We collected AML datasets from public databases to obtain PRlncRNA associated with survival and constructed a PRlncRNA signature using Lasso-Cox regression analysis. Subsequently, we employed RT-PCR to confirm its expression difference and internal training to further verify its reliability. Next, AML patients were classified into two subgroups by the median risk score. Finally, the differences between two groups in immune infiltration, enrichment analysis and drug sensitivity were further explored. Results: A PRlncRNA signature and an effective nomogram combined with clinicopathological variables to predict the prognosis of AML were constructed. The internal validations showed that the PRlncRNA risk score model was an accurate and productive indicator to predict the outcome of AML. Furthermore, this study indicated that higher inflammatory cell and immunosuppressive cells, and less sensitive to conventional chemotherapy drugs were highlighted in the high-risk group. Conclusion: Through comprehensive analysis of PRlncRNA model, our study may offer a valuable basis for future researches in targeting pyroptosis and tumor microenvironment (TME) and provide new measures for prevention and treatment in AML.

Arginine limitation causes a directed DNA sequence evolution response in colorectal cancer cells

Utilization of specific codons varies significantly across organisms. Cancer represents a model for understanding DNA sequence evolution and could reveal causal factors underlying codon evolution. We found that across human cancer, arginine codons are frequently mutated to other codons. Moreover, arginine restriction-a feature of tumor microenvironments-is sufficient to induce arginine codon-switching mutations in human colon cancer cells. Such DNA codon switching events encode mutant proteins with arginine residue substitutions. Mechanistically, arginine limitation caused rapid reduction of arginine transfer RNAs and the stalling of ribosomes over arginine codons. Such selective pressure against arginine codon translation induced a proteomic shift towards low arginine codon containing genes, including specific amino acid transporters, and caused mutational evolution away from arginine codons-reducing translational bottlenecks that occurred during arginine starvation. Thus, environmental availability of a specific amino acid can influence DNA sequence evolution away from its cognate codons and generate altered proteins.

Regularized survival learning and cross-database analysis enabled identification of colorectal cancer prognosis-related immune genes

Colon adenocarcinoma is the most common type of colorectal cancer. The prognosis of advanced colorectal cancer patients who received treatment is still very poor. Therefore, identifying new biomarkers for prognosis prediction has important significance for improving treatment strategies. However, the power of biomarker analyses was limited by the used sample size of individual database. In this study, we combined Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases to expand the number of healthy tissue samples. We screened differentially expressed genes between the GTEx healthy samples and TCGA tumor samples. Subsequently, we applied least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis to identify nine prognosis-related immune genes: ANGPTL4, IDO1, NOX1, CXCL3, LTB4R, IL1RL2, CD72, NOS2, and NUDT6. We computed the risk scores of samples based on the expression levels of these genes and divided patients into high- and low-risk groups according to this risk score. Survival analysis results showed a significant difference in survival rate between the two risk groups. The high-risk group had a significantly lower overall survival rate and poorer prognosis. We found the receiver operating characteristic based on the risk score was showed to accurately predict patients' prognosis. These prognosis-related immune genes may be potential biomarkers for colorectal cancer diagnosis and treatment. Our open-source code is freely available from GitHub at https://github.com/gutmicrobes/Prognosis-model.git.