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

Structural insights into an atypical histone binding mechanism by a PHD finger

Complex associating with SET1 (COMPASS) is a histone H3K4 tri-methyltransferase controlled by several regulatory subunits including CXXC zinc finger protein 1 (Cfp1). Prior studies established the structural underpinnings controlling H3K4me3 recognition by the PHD domain of Cfp1's yeast homolog (Spp1). However, metazoans Cfp1PHD lacks structural elements important for H3K4me3 stabilization in Spp1, suggesting that in metazoans, Cfp1PHD domain binds H3K4me3 differently. The structure of Cfp1PHD in complex with H3K4me3 shows unique features such as non-canonical coordination of the first zinc atom and a disulfide bond forcing the reorientation of Cfp1PHD N-terminus, thereby leading to an atypical H3K4me3 binding pocket. This configuration minimizes Cfp1PHD reliance on canonical residues important for histone binding functions of other PHD domains. Cancer-related mutations in Cfp1PHD impair H3K4me3 binding, implying a potential impact on epigenetic signaling. Our work highlights a potential diversification of PHD histone binding modes and the impact of cancer mutations on Cfp1 functions.

DNA methylation profile of inflammatory breast cancer and its impact on prognosis and outcome

BACKGROUND

Inflammatory breast cancer (IBC) is a rare disease characterized by rapid progression, early metastasis, and a high mortality rate.

METHODS

Genome-wide DNA methylation analysis (EPIC BeadChip platform, Illumina) and somatic gene variants (105 cancer-related genes) were performed in 24 IBCs selected from a cohort of 140 cases.

RESULTS

We identified 46,908 DMPs (differentially methylated positions) (66% hypomethylated); CpG islands were predominantly hypermethylated (39.9%). Unsupervised clustering analysis revealed three clusters of DMPs characterized by an enrichment of specific gene mutations and hormone receptor status. The comparison among DNA methylation findings and external datasets (TCGA-BRCA stages III-IV) resulted in 385 shared DMPs mapped in 333 genes (264 hypermethylated). 151 DMPs were associated with 110 genes previously detected as differentially expressed in IBC (GSE45581), and 68 DMPs were negatively correlated with gene expression. We also identified 4369 DMRs (differentially methylated regions) mapped on known genes (2392 hypomethylated). BCAT1, CXCL12, and TBX15 loci were selected and evaluated by bisulfite pyrosequencing in 31 IBC samples. BCAT1 and TBX15 had higher methylation levels in triple-negative compared to non-triple-negative, while CXCL12 had lower methylation levels in triple-negative than non-triple-negative IBC cases. TBX15 methylation level was associated with obesity.

CONCLUSIONS

Our findings revealed a heterogeneous DNA methylation profile with potentially functional DMPs and DMRs. The DNA methylation data provided valuable insights for prognostic stratification and therapy selection to improve patient outcomes.

Sensitive detection of synthetic response to cancer immunotherapy driven by gene paralog pairs

Emerging immunotherapies such as immune checkpoint blockade (ICB) and chimeric antigen receptor T-cell (CAR-T) therapy have revolutionized cancer treatment and have improved the survival of patients with multiple cancer types. Despite this success many patients are unresponsive to these treatments or relapse following treatment. CRISPR activation and knockout (KO) screens have been used to identify novel single gene targets that can enhance effector T cell function and promote immune cell targeting and eradication of tumors. However, cancer cells often employ multiple genes to promote an immunosuppressive pathway and thus modulating individual genes often has a limited effect. Paralogs are genes that originate from common ancestors and retain similar functions. They often have complex effects on a particular phenotype depending on factors like gene family similarity, each individual gene's expression and the physiological or pathological context. Some paralogs exhibit synthetic lethal interactions in cancer cell survival; however, a thorough investigation of paralog pairs that could enhance the efficacy of cancer immunotherapy is lacking. Here we introduce a sensitive computational approach that uses sgRNA sets enrichment analysis to identify cancer-intrinsic paralog pairs which have the potential to synergistically enhance T cell-mediated tumor destruction. We have further developed an ensemble learning model that uses an XGBoost classifier and incorporates features such as gene characteristics, sequence and structural similarities, protein-protein interaction (PPI) networks, and gene coevolution data to predict paralog pairs that are likely to enhance immunotherapy efficacy. We experimentally validated the functional significance of these predicted paralog pairs using double knockout (DKO) of identified paralog gene pairs as compared to single gene knockouts (SKOs). These data and analyses collectively provide a sensitive approach to identify previously undetected paralog pairs that can enhance cancer immunotherapy even when individual genes within the pair has a limited effect.

Development and evaluation of a disulfidoptosis-related lncRNA index for prognostication in clear cell renal cell carcinoma

Background

This study introduces a novel prognostic tool, the Disulfidoptosis-Related lncRNA Index (DRLI), integrating the molecular signatures of disulfidoptosis and long non-coding RNAs (lncRNAs) with the cellular heterogeneity of the tumor microenvironment, to predict clinical outcomes in patients with clear cell renal cell carcinoma (ccRCC).

Methods

We analyzed 530 tumor and 72 normal samples from The Cancer Genome Atlas (TCGA), employing k-means clustering based on disulfidoptosis-associated gene expression to stratify ccRCC samples into prognostic groups. lncRNAs correlated with disulfidoptosis were identified and used to construct the DRLI, which was validated by Kaplan-Meier and receiver operating characteristic curves. We utilized single-cell deconvolution analysis to estimate the proportion of immune cell types within the tumor microenvironment, while the ESTIMATE and TIDE algorithms were employed to assess immune infiltration and potential response to immunotherapy.

Results

The Disulfidoptosis-Related lncRNA Index (DRLI) effectively stratified ccRCC patients into high and low-risk groups, significantly impacting survival outcomes (P < 0.001). High-risk patients, marked by a unique lncRNA profile associated with disulfidoptosis, faced worse prognoses. Single-cell analysis revealed marked tumor microenvironment heterogeneity, especially in immune cell makeup, correlating with patient risk levels. In prognostic predictions, DRLI outperformed traditional clinical indicators, achieving AUC values of 0.779, 0.757, and 0.779 for 1-year, 3-year, and 5-year survival in the training set, and 0.746, 0.734, and 0.750 in the validation set. Notably, while the constructed nomogram showed exceptional predictive capability for short-term prognosis (AUC = 0.877), the DRLI displayed remarkable long-term predictive accuracy, with its AUC value reaching 0.823 for 10-year survival, closely approaching the nomogram's performance.

Conclusions

The study introduces the DRLI as a groundbreaking molecular stratification tool for ccRCC, enhancing prognostic precision and potentially guiding personalized treatment strategies. This advancement is particularly significant in the context of long-term survival predictions. Our findings also elucidate the complex interplay between disulfidoptosis, lncRNAs, and the immune microenvironment in ccRCC, offering a comprehensive perspective on its pathogenesis and progression. The DRLI and the nomogram together represent significant strides in ccRCC research, highlighting the importance of molecular-based assessments in predicting patient outcomes.

Prognostic potential of CUL3 ligase with differential roles in luminal A and basal type breast cancer tumors

Breast cancer is a prevalent and significant cause of mortality in women, and manifests as six molecular subtypes. Its further histologic classification into non-invasive ductal or lobular carcinoma (DCIS) and invasive carcinoma (ILC or IDC) underscores its heterogeneity. The ubiquitin-proteasome system plays a crucial role in breast cancer, with inhibitors targeting the 26S proteasome showing promise in clinical treatment. The Cullin-RING ubiquitin ligases, including CUL3, have direct links to breast cancer. This study focuses on CUL3 as a potential biomarker, leveraging high-throughput sequencing, gene expression profiling, experimental and data analysis tools. Through comprehensive analysis using databases like GEPIA2 and UALCAN, as well as TCGA datasets, CUL3's expression and its association with prognostic values were assessed. Additionally, the impact of CUL3 overexpression was explored in MCF-7 and MDA-MB-231 breast cancer cell lines, revealing distinct differences in molecular and phenotypic characteristics. We further profiled its expression and localization in breast cancer tissues identifying prominent differences between luminal A and TNBC tumors. Conclusively, CUL3 was found to be associated with cell cycle progression, and DNA damage response, exhibiting diverse roles depending on the tumor's molecular type. It exhibits a tendency to act as an oncogene in triple-negative tumors and as a tumor suppressor in luminal A types, suggesting a potential significance in breast cancer progression and therapeutic directions.

Homeobox and Polycomb target gene methylation in human solid tumors

DNA methylation is an epigenetic mark that plays an important role in defining cancer phenotypes, with global hypomethylation and focal hypermethylation at CpG islands observed in tumors. These methylation marks can also be used to define tumor types and provide an avenue for biomarker identification. The homeobox gene class is one that has potential for this use, as well as other genes that are Polycomb Repressive Complex 2 targets. To begin to unravel this relationship, we performed a pan-cancer DNA methylation analysis using sixteen Illumina HM450k array datasets from TCGA, delving into cancer-specific qualities and commonalities between tumor types with a focus on homeobox genes. Our comparisons of tumor to normal samples suggest that homeobox genes commonly harbor significant hypermethylated differentially methylated regions. We identified two homeobox genes, HOXA3 and HOXD10, that are hypermethylated in all 16 cancer types. Furthermore, we identified several potential homeobox gene biomarkers from our analysis that are uniquely methylated in only one tumor type and that could be used as screening tools in the future. Overall, our study demonstrates unique patterns of DNA methylation in multiple tumor types and expands on the interplay between the homeobox gene class and oncogenesis.

A microdeletion event at 19q13.43 in IDH-mutant astrocytomas is strongly correlated with MYC overexpression

MYC dysregulation is pivotal in the onset and progression of IDH-mutant gliomas, mostly driven by copy-number alterations, regulatory element alterations, or epigenetic changes. Our pilot analysis uncovered instances of relative MYC overexpression without alterations in the proximal MYC network (PMN), prompting a deeper investigation into potential novel oncogenic mechanisms. Analysing comprehensive genomics profiles of 236 "IDH-mutant 1p/19q non-co-deleted" lower-grade gliomas from The Cancer Genome Atlas, we identified somatic genomic alterations within the PMN. In tumours without PMN-alterations but with MYC-overexpression, genes correlated with MYC-overexpression were identified. Our analyses yielded that 86/236 of astrocytomas exhibited no PMN-alterations, a subset of 21/86 displaying relative MYC overexpression. Within this subset, we discovered 42 genes inversely correlated with relative MYC expression, all on 19q. Further analysis pinpointed a minimal common region at 19q13.43, encompassing 15 genes. The inverse correlations of these 15 genes with relative MYC overexpression were re-confirmed using independent scRNAseq data. Further, the micro-deleted astrocytoma subset displayed significantly higher genomic instability compared to WT cases, but lower instability compared to PMN-hit cases. This newly identified 19q micro-deletion represents a potential novel mechanism underlying MYC dysregulation in astrocytomas. Given the prominence of 19q loss in IDH-mutant gliomas, our findings bear significant implications for understanding gliomagenesis.

Identification and validation of immune-related gene signature models for predicting prognosis and immunotherapy response in hepatocellular carcinoma

Background

This study seeks to enhance the accuracy and efficiency of clinical diagnosis and therapeutic decision-making in hepatocellular carcinoma (HCC), as well as to optimize the assessment of immunotherapy response.

Methods

A training set comprising 305 HCC cases was obtained from The Cancer Genome Atlas (TCGA) database. Initially, a screening process was undertaken to identify prognostically significant immune-related genes (IRGs), followed by the application of logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods for gene modeling. Subsequently, the final model was constructed using support vector machines-recursive feature elimination (SVM-RFE). Following model evaluation, quantitative polymerase chain reaction (qPCR) was employed to examine the gene expression profiles in tissue samples obtained from our cohort of 54 patients with HCC and an independent cohort of 231 patients, and the prognostic relevance of the model was substantiated. Thereafter, the association of the model with the immune responses was examined, and its predictive value regarding the efficacy of immunotherapy was corroborated through studies involving three cohorts undergoing immunotherapy. Finally, the study uncovered the potential mechanism by which the model contributed to prognosticating HCC outcomes and assessing immunotherapy effectiveness.

Results

SVM-RFE modeling was applied to develop an OS prognostic model based on six IRGs (CMTM7, HDAC1, HRAS, PSMD1, RAET1E, and TXLNA). The performance of the model was assessed by AUC values on the ROC curves, resulting in values of 0.83, 0.73, and 0.75 for the predictions at 1, 3, and 5 years, respectively. A marked difference in OS outcomes was noted when comparing the high-risk group (HRG) with the low-risk group (LRG), as demonstrated in both the initial training set (P <0.0001) and the subsequent validation cohort (P <0.0001). Additionally, the SVMRS in the HRG demonstrated a notable positive correlation with key immune checkpoint genes (CTLA-4, PD-1, and PD-L1). The results obtained from the examination of three cohorts undergoing immunotherapy affirmed the potential capability of this model in predicting immunotherapy effectiveness.

Conclusions

The HCC predictive model developed in this study, comprising six genes, demonstrates a robust capability to predict the OS of patients with HCC and immunotherapy effectiveness in tumor management.

RPS24 alternative splicing is a marker of cancer progression and epithelial-mesenchymal transition

Although alternative splicing (AS) is a major mechanism that adds diversity to gene expression patterns, its precise role in generating variability in ribosomal proteins, known as ribosomal heterogeneity, remains unclear. The ribosomal protein S24 (RPS24) gene, encoding a ribosomal component, undergoes AS; however, in-depth studies have been challenging because of three microexons between exons 4 and 6. We conducted a detailed analysis of RPS24 AS isoforms using a direct approach to investigate the splicing junctions related to these microexons, focusing on four AS isoforms. Each of these isoforms showed tissue specificity and relative differences in expression among cancer types. Significant differences in the proportions of these RPS24 AS isoforms between cancerous and normal tissues across diverse cancer types were also observed. Our study highlighted a significant correlation between the expression levels of a specific RPS24 AS isoform and the epithelial-mesenchymal transition process in lung and breast cancers. Our research contributes to a better understanding of the intricate regulatory mechanisms governing AS of ribosomal protein genes and highlights the biological implications of RPS24 AS isoforms in tissue development and tumorigenesis.

A pan-cancer single-cell transcriptional analysis of antigen-presenting cancer-associated fibroblasts in the tumor microenvironment

Background

Cancer-associated fibroblasts (CAFs) are the primary stromal cells found in tumor microenvironment, and display high plasticity and heterogeneity. By using single-cell RNA-seq technology, researchers have identified various subpopulations of CAFs, particularly highlighting a recently identified subpopulation termed antigen-presenting CAFs (apCAFs), which are largely unknown.

Methods

We collected datasets from public databases for 9 different solid tumor types to analyze the role of apCAFs in the tumor microenvironment.

Results

Our data revealed that apCAFs, likely originating mainly from normal fibroblast, are commonly found in different solid tumor types and generally are associated with anti-tumor effects. apCAFs may be associated with the activation of CD4+ effector T cells and potentially promote the survival of CD4+ effector T cells through the expression of C1Q molecules. Moreover, apCAFs exhibited highly enrichment of transcription factors RUNX3 and IKZF1, along with increased glycolytic metabolism.

Conclusions

Taken together, these findings offer novel insights into a deeper understanding of apCAFs and the potential therapeutic implications for apCAFs targeted immunotherapy in cancer.

Integrative bioinformatics approach yields a novel gene expression risk model for prognosis and progression prediction in prostate cancer

Prostate cancer (PCa), a prevalent malignancy among elderly males, exhibits a notable rate of advancement, even when subjected to conventional androgen deprivation therapy or chemotherapy. An effective progression prediction model would prove invaluable in identifying patients with a higher progression risk. Using bioinformatics strategies, we integrated diverse data sets of PCa to construct a novel risk model predicated on gene expression and progression-free survival (PFS). The accuracy of the model was assessed through validation using an independent data set. Eight genes were discerned as independent prognostic factors and included in the prediction model. Patients assigned to the high-risk cohort demonstrated a diminished PFS, and the areas under the curve of our model in the validation set for 1-year, 3-year, and 5-year PFS were 0.9325, 0.9041 and 0.9070, respectively. Additionally, through the application of single-cell RNA sequencing to two castration-related prostate cancer (CRPC) samples and two hormone-related prostate cancer (HSPC) samples, we discovered that luminal cells within CRPC exhibited an elevated risk score. Subsequent molecular biology experiments corroborated our findings, illustrating heightened SYK expression levels within tumour tissues and its contribution to cancer cell migration. We found that the knockdown of SYK could inhibit migration in PCa cells. Our progression-related risk model demonstrated the potential prognostic value of SYK and indicated its potential as a target for future diagnosis and treatment strategies in PCa management.

Clover: An unbiased method for prioritizing differentially expressed genes using a data-driven approach

Identifying key genes from a list of differentially expressed genes (DEGs) is a critical step in transcriptome analysis. However, current methods, including Gene Ontology analysis and manual annotation, essentially rely on existing knowledge, which is highly biased depending on the extent of the literature. As a result, understudied genes, some of which may be associated with important molecular mechanisms, are often ignored or remain obscure. To address this problem, we propose Clover, a data-driven scoring method to specifically highlight understudied genes. Clover aims to prioritize genes associated with important molecular mechanisms by integrating three metrics: the likelihood of appearing in the DEG list, tissue specificity, and number of publications. We applied Clover to Alzheimer's disease data and confirmed that it successfully detected known associated genes. Moreover, Clover effectively prioritized understudied but potentially druggable genes. Overall, our method offers a novel approach to gene characterization and has the potential to expand our understanding of gene functions. Clover is an open-source software written in Python3 and available on GitHub at https://github.com/G708/Clover.

Cytosolic DNA sensor AIM2 promotes KRAS-driven lung cancer independent of inflammasomes

Constitutively active KRAS mutations are among the major drivers of lung cancer, yet the identity of molecular co-operators of oncogenic KRAS in the lung remains ill-defined. The innate immune cytosolic DNA sensor and pattern recognition receptor (PRR) Absent-in-melanoma 2 (AIM2) is best known for its assembly of multiprotein inflammasome complexes and promoting an inflammatory response. Here, we define a role for AIM2, independent of inflammasomes, in KRAS-addicted lung adenocarcinoma (LAC). In genetically defined and experimentally induced (nicotine-derived nitrosamine ketone; NNK) LAC mouse models harboring the KrasG12D driver mutation, AIM2 was highly upregulated compared with other cytosolic DNA sensors and inflammasome-associated PRRs. Genetic ablation of AIM2 in KrasG12D and NNK-induced LAC mouse models significantly reduced tumor growth, coincident with reduced cellular proliferation in the lung. Bone marrow chimeras suggest a requirement for AIM2 in KrasG12D-driven LAC in both hematopoietic (immune) and non-hematopoietic (epithelial) cellular compartments, which is supported by upregulated AIM2 expression in immune and epithelial cells of mutant KRAS lung tissues. Notably, protection against LAC in AIM2-deficient mice is associated with unaltered protein levels of mature Caspase-1 and IL-1β inflammasome effectors. Moreover, genetic ablation of the key inflammasome adapter, ASC, did not suppress KrasG12D-driven LAC. In support of these in vivo findings, AIM2, but not mature Caspase-1, was upregulated in human LAC patient tumor biopsies. Collectively, our findings reveal that endogenous AIM2 plays a tumor-promoting role, independent of inflammasomes, in mutant KRAS-addicted LAC, and suggest innate immune DNA sensing may provide an avenue to explore new therapeutic strategies in lung cancer.

ACTL6A: unraveling its prognostic impact and paving the way for targeted therapeutics in carcinogenesis

Introduction: Increased Actin-like 6A (ACTL6A) expression is associated with various cancers, but its comprehensive investigation across different malignancies is lacking. We aimed to analyze ACTL6A as a potential oncogene and therapeutic target using bioinformatics tools. Methods: We comprehensively analyzed ACTL6A expression profiles across human malignancies, focusing on correlations with tumor grade, stage, metastasis, and patient survival. Genetic alterations were examined, and the epigenetic landscape of ACTL6A was assessed using rigorous methods. The impact of ACTL6A on immune cell infiltration in the tumor microenvironment was evaluated, along with molecular docking studies and machine learning models. Results: Our analysis revealed elevated ACTL6A expression in various tumors, correlating with poor prognostic indicators such as tumor grade, stage, metastasis, and patient survival. Genetic mutations and epigenetic modifications were identified, along with associations with immune cell infiltration and key cellular pathways. Machine learning models demonstrated ACTL6A's potential for cancer detection. Discussion: ACTL6A emerges as a promising diagnostic and therapeutic target in cancer, with implications for prognosis and therapy. Our study provides comprehensive insights into its carcinogenic actions, highlighting its potential as both a prognostic indicator and a target for anti-cancer therapy. This integrative approach enhances our understanding of ACTL6A's role in cancer pathogenesis and treatment.

Metabolic pathway-based subtypes associate glycan biosynthesis and treatment response in head and neck cancer

Head and Neck Squamous Cell Carcinoma (HNSCC) is a heterogeneous malignancy that remains a significant challenge in clinical management due to frequent treatment failures and pronounced therapy resistance. While metabolic dysregulation appears to be a critical factor in this scenario, comprehensive analyses of the metabolic HNSCC landscape and its impact on clinical outcomes are lacking. This study utilized transcriptomic data from four independent clinical cohorts to investigate metabolic heterogeneity in HNSCC and define metabolic pathway-based subtypes (MPS). In HPV-negative HNSCCs, MPS1 and MPS2 were identified, while MPS3 was enriched in HPV-positive cases. MPS classification was associated with clinical outcome post adjuvant radio(chemo)therapy, with MPS1 consistently exhibiting the highest risk of therapeutic failure. MPS1 was uniquely characterized by upregulation of glycan (particularly chondroitin/dermatan sulfate) metabolism genes. Immunohistochemistry and pilot mass spectrometry imaging analyses confirmed this at metabolite level. The histological context and single-cell RNA sequencing data identified the malignant cells as key contributors. Globally, MPS1 was distinguished by a unique transcriptomic landscape associated with increased disease aggressiveness, featuring motifs related to epithelial-mesenchymal transition, immune signaling, cancer stemness, tumor microenvironment assembly, and oncogenic signaling. This translated into a distinct histological appearance marked by extensive extracellular matrix remodeling, abundant spindle-shaped cancer-associated fibroblasts, and intimately intertwined populations of malignant and stromal cells. Proof-of-concept data from orthotopic xenotransplants replicated the MPS phenotypes on the histological and transcriptome levels. In summary, this study introduces a metabolic pathway-based classification of HNSCC, pinpointing glycan metabolism-enriched MPS1 as the most challenging subgroup that necessitates alternative therapeutic strategies.

NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders

Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. We developed NetActivity, a machine learning framework that generates GSAS based on a sparsely-connected autoencoder, where each neuron in the inner layer represents a gene set. We proposed a three-tier training that yielded representative, robust, and interpretable GSAS. NetActivity model was trained with 1518 GO biological processes terms and KEGG pathways and all GTEx samples. NetActivity generates GSAS robust to the initialization parameters and representative of the original transcriptome, and assigned higher importance to more biologically relevant genes. Moreover, NetActivity returns GSAS with a more consistent definition and higher interpretability than GSVA and hipathia, state-of-the-art gene set projection methods. Finally, NetActivity enables combining bulk RNA-seq and microarray datasets in a meta-analysis of prostate cancer progression, highlighting gene sets related to cell division, key for disease progression. When applied to metastatic prostate cancer, gene sets associated with cancer progression were also altered due to drug resistance, while a classical enrichment analysis identified gene sets irrelevant to the phenotype. NetActivity is publicly available in Bioconductor and GitHub.

Androgen receptor activity inversely correlates with immune cell infiltration and immunotherapy response across multiple cancer lineages

There is now increasing recognition of the important role of androgen receptor (AR) in modulating immune function. To gain a comprehensive understanding of the effects of AR activity on cancer immunity, we employed a computational approach to profile AR activity in 33 human tumor types using RNA-Seq datasets from The Cancer Genome Atlas. Our pan-cancer analysis revealed that the genes most negatively correlated with AR activity across cancers are involved in active immune system processes. Importantly, we observed a significant negative correlation between AR activity and IFNγ pathway activity at the pan-cancer level. Indeed, using a matched biopsy dataset from subjects with prostate cancer before and after AR-targeted treatment, we verified that inhibiting AR enriches immune cell abundances and is associated with higher IFNγ pathway activity. Furthermore, by analyzing immunotherapy datasets in multiple cancers, our results demonstrate that low AR activity was significantly associated with a favorable response to immunotherapy. Together, our data provide a comprehensive assessment of the relationship between AR signaling and tumor immunity.

RNA expression of 6 genes from metastatic mucosal gastric cancer serves as the global prognostic marker for gastric cancer with functional validation

BACKGROUND

Molecular analysis of advanced tumors can increase tumor heterogeneity and selection bias. We developed a robust prognostic signature for gastric cancer by comparing RNA expression between very rare early gastric cancers invading only mucosal layer (mEGCs) with lymph node metastasis (Npos) and those without metastasis (Nneg).

METHODS

Out of 1003 mEGCs, all Npos were matched to Nneg using propensity scores. Machine learning approach comparing Npos and Nneg was used to develop prognostic signature. The function and robustness of prognostic signature was validated using cell lines and external datasets.

RESULTS

Extensive machine learning with cross-validation identified the prognostic classifier consisting of four overexpressed genes (HDAC5, NPM1, DTX3, and PPP3R1) and two downregulated genes (MED12 and TP53), and enabled us to develop the risk score predicting poor prognosis. Cell lines engineered to high-risk score showed increased invasion, migration, and resistance to 5-FU and Oxaliplatin but maintained sensitivity to an HDAC inhibitor. Mouse models after tail vein injection of cell lines with high-risk score revealed increased metastasis. In three external cohorts, our risk score was identified as the independent prognostic factor for overall and recurrence-free survival.

CONCLUSION

The risk score from the 6-gene classifier can successfully predict the prognosis of gastric cancer.

Statistical plasmode simulations-Potentials, challenges and recommendations

Statistical data simulation is essential in the development of statistical models and methods as well as in their performance evaluation. To capture complex data structures, in particular for high-dimensional data, a variety of simulation approaches have been introduced including parametric and the so-called plasmode simulations. While there are concerns about the realism of parametrically simulated data, it is widely claimed that plasmodes come very close to reality with some aspects of the "truth" known. However, there are no explicit guidelines or state-of-the-art on how to perform plasmode data simulations. In the present paper, we first review existing literature and introduce the concept of statistical plasmode simulation. We then discuss advantages and challenges of statistical plasmodes and provide a step-wise procedure for their generation, including key steps to their implementation and reporting. Finally, we illustrate the concept of statistical plasmodes as well as the proposed plasmode generation procedure by means of a public real RNA data set on breast carcinoma patients.

Decoding dynamic miRNA:ceRNA interactions unveils therapeutic insights and targets across predominant cancer landscapes

Competing endogenous RNAs play key roles in cellular molecular mechanisms through cross-talk in post-transcriptional interactions. Studies on ceRNA cross-talk, which is particularly dependent on the abundance of free transcripts, generally involve large- and small-scale studies involving the integration of transcriptomic data from tissues and correlation analyses. This abundance-dependent nature of ceRNA interactions suggests that tissue- and condition-specific ceRNA dynamics may fluctuate. However, there are no comprehensive studies investigating the ceRNA interactions in normal tissue, ceRNAs that are lost and/or appear in cancerous tissues or their interactions. In this study, we comprehensively analyzed the tumor-specific ceRNA fluctuations observed in the three highest-incidence cancers, LUAD, PRAD, and BRCA, compared to healthy lung, prostate, and breast tissues, respectively. Our observations pertaining to tumor-specific competing endogenous RNA (ceRNA) interactions revealed that, in the cases of lung adenocarcinoma (LUAD), prostate adenocarcinoma (PRAD), and breast invasive carcinoma (BRCA), 3,204, 1,233, and 406 ceRNAs, respectively, engage in post-transcriptional intercommunication within tumor tissues, in contrast to their absence in corresponding healthy samples. We also found that 90 ceRNAs are shared by the three cancer types and that these ceRNAs participate in ceRNA interactions in tumor tissues compared to those in normal tissues. Among the 90 ceRNAs that directly interact with miRNAs, we uncovered a core network of 165 miRNAs and 63 ceRNAs that should be considered in RNA-targeted and RNA-mediated approaches in future studies and could be used in these three aggressive cancer types. More specifically, in this core interaction network, ceRNAs such as GALNT7, KLF9, and DAB2 and miRNAs like miR-106a/b-5p, miR-20a-5p, and miR-519d-3p may have potential as common targets in the three critical cancers. In contrast to conventional methods that construct ceRNA networks using differentially expressed genes compared to normal tissues, our proposed approach identifies ceRNA players by considering their context within the ceRNA:miRNA interactions. Our results have the potential to reveal distinct and common ceRNA interactions in cancer types and to pinpoint critical RNAs, thereby paving the way for RNA-based strategies in the battle against cancer.

TMBcalc: a computational pipeline for identifying pan-cancer Tumor Mutational Burden gene signatures

Background

In the precision medicine era, identifying predictive factors to select patients most likely to benefit from treatment with immunological agents is a crucial and open challenge in oncology.

Methods

This paper presents a pan-cancer analysis of Tumor Mutational Burden (TMB). We developed a novel computational pipeline, TMBcalc, to calculate the TMB. Our methodology can identify small and reliable gene signatures to estimate TMB from custom targeted-sequencing panels. For this purpose, our pipeline has been trained on top of 17 cancer types data obtained from TCGA.

Results

Our results show that TMB, computed through the identified signature, strongly correlates with TMB obtained from whole-exome sequencing (WES).

Conclusion

We have rigorously analyzed the effectiveness of our methodology on top of several independent datasets. In particular we conducted a comprehensive testing on: (i) 126 samples sourced from the TCGA database; few independent whole-exome sequencing (WES) datasets linked to colon, breast, and liver cancers, all acquired from the EGA and the ICGC Data Portal. This rigorous evaluation clearly highlights the robustness and practicality of our approach, positioning it as a promising avenue for driving substantial progress within the realm of clinical practice.

Dichotomous Effects of Glypican-4 on Cancer Progression and Its Crosstalk with Oncogenes

Glypicans are linked to various aspects of neoplastic behavior, and their therapeutic value has been proposed in different cancers. Here, we have systematically assessed the impact of GPC4 on cancer progression through functional genomics and transcriptomic analyses across a broad range of cancers. Survival analysis using TCGA cancer patient data reveals divergent effects of GPC4 expression across various cancer types, revealing elevated GPC4 expression levels to be associated with both poor and favorable prognoses in a cancer-dependent manner. Detailed investigation of the role of GPC4 in glioblastoma and non-small cell lung adenocarcinoma by genetic perturbation studies displays opposing effects on these cancers, where the knockout of GPC4 with CRISPR/Cas9 attenuated proliferation of glioblastoma and augmented proliferation of lung adenocarcinoma cells and the overexpression of GPC4 exhibited a significant and opposite effect. Further, the overexpression of GPC4 in GPC4-knocked-down glioblastoma cells restored the proliferation, indicating its mitogenic effect in this cancer type. Additionally, a survival analysis of TCGA patient data substantiated these findings, revealing an association between elevated levels of GPC4 and a poor prognosis in glioblastoma, while indicating a favorable outcome in lung carcinoma patients. Finally, through transcriptomic analysis, we attempted to assign mechanisms of action to GPC4, as we find it implicated in cell cycle control and survival core pathways. The analysis revealed upregulation of oncogenes, including FGF5, TGF-β superfamily members, and ITGA-5 in glioblastoma, which were downregulated in lung adenocarcinoma patients. Our findings illuminate the pleiotropic effect of GPC4 in cancer, underscoring its potential as a putative prognostic biomarker and indicating its therapeutic implications in a cancer type dependent manner.

Identification of PPAR-related differentially expressed genes liver hepatocellular carcinoma and construction of a prognostic model based on data analysis and molecular docking

Liver hepatocellular carcinoma (LIHC) is a significant global health issue with limited treatment options. In this study, single-cell RNA sequencing (scRNA-seq) data were used to explore the molecular mechanisms of LIHC development and identify potential targets for therapy. The expression of peroxisome proliferator-activated receptors (PPAR)-related genes was analysed in LIHC samples, and primary cell populations, including natural killer cells, T cells, B cells, myeloid cells, endothelial cells, fibroblasts and hepatocytes, were identified. Analysis of the differentially expressed genes (DEGs) between normal and tumour tissues revealed significant changes in gene expression in various cell populations. PPAR activity was evaluated using the 'AUCell' R software, which indicated higher scores in the normal versus the malignant hepatocytes. Furthermore, the DEGs showed significant enrichment of pathways related to lipid and glucose metabolism, cell development, differentiation and inflammation. A prognostic model was then constructed using 8 PPARs-related genes, including FABP5, LPL, ACAA1, PPARD, FABP4, PLIN1, HMGCS2 and CYP7A1, identified using least absolute shrinkage and selection operator-Cox regression analysis, and validated in the TCGA-LIHC, ICGI-LIRI and GSE14520 datasets. Patients with low-risk scores had better prognosis in all cohorts. Based on the expression of the eight model genes, two clusters of patients were identified by ConsensusCluster analysis. We also predicted small-molecule drugs targeting the model genes, and identified perfluorohexanesulfonic acid, triflumizole and perfluorononanoic acid as potential candidates. Finally, wound healing assay confirmed that PPARD can promote the migration of liver cancer cells. Overall, our study offers novel perspectives on the molecular mechanisms of LIHC and potential areas for therapeutic intervention, which may facilitate the development of more effective treatment regimens.

Identification and validation of a novel six-gene signature based on mucinous adenocarcinoma-related gene molecular typing in colorectal cancer

BACKGROUND AND OBJECTIVES

Colorectal mucinous adenocarcinoma (MAC) is a particular pathological type that has yet to be thoroughly studied. This study aims to investigate the characteristics of colorectal MAC-related genes in colorectal cancer (CRC), explore the role of MAC-related genes in accurately classifying CRC, and further construct a prognostic signature.

METHODS

CRC samples were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). MAC-related differentially expressed genes (DEGs) were analyzed in TCGA samples. Based on colorectal MAC-related genes, TCGA CRC samples were molecularly typed by the non-negative matrix factorization (NMF). According to the molecular subtype characteristics, the RiskScore signature was constructed through univariate Cox, the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Clinical significance in CRC of the RiskScore signature was analyzed. A nomogram was further built based on the RiskScore signature.

RESULTS

From the colorectal MAC-related genes, three distinct molecular subtypes were identified. A RiskScore signature composed of six CRC subtype-related genes (CALB1, MMP1, HOXC6, ZIC2, SFTA2, and HYAL1) was constructed. Patients with high-RiskScores had the worse prognoses. RiskScores led to differences in gene mutation characteristics, antitumor drug sensitivity, and tumor microenvironment of CRC. A nomogram based on the signature was developed to predict the one-, three-, and five-year survival of CRC patients.

CONCLUSION

MAC-related genes were able to classify CRC. A RiskScore signature based on the colorectal MAC-related molecular subtype was constructed, which had important clinical significance for guiding the accurate stratification of CRC patients.

Multiomic profiling of breast cancer cells uncovers stress MAPK-associated sensitivity to AKT degradation

More than 50% of human tumors display hyperactivation of the serine/threonine kinase AKT. Despite evidence of clinical efficacy, the therapeutic window of the current generation of AKT inhibitors could be improved. Here, we report the development of a second-generation AKT degrader, INY-05-040, which outperformed catalytic AKT inhibition with respect to cellular suppression of AKT-dependent phenotypes in breast cancer cell lines. A growth inhibition screen with 288 cancer cell lines confirmed that INY-05-040 had a substantially higher potency than our first-generation AKT degrader (INY-03-041), with both compounds outperforming catalytic AKT inhibition by GDC-0068. Using multiomic profiling and causal network integration in breast cancer cells, we demonstrated that the enhanced efficacy of INY-05-040 was associated with sustained suppression of AKT signaling, which was followed by induction of the stress mitogen-activated protein kinase (MAPK) c-Jun N-terminal kinase (JNK). Further integration of growth inhibition assays with publicly available transcriptomic, proteomic, and reverse phase protein array (RPPA) measurements established low basal JNK signaling as a biomarker for breast cancer sensitivity to AKT degradation. Together, our study presents a framework for mapping the network-wide signaling effects of therapeutically relevant compounds and identifies INY-05-040 as a potent pharmacological suppressor of AKT signaling.

Stromal Signals Dominate Gene Expression Signature Scores That Aim to Describe Cancer Cell-intrinsic Stemness or Mesenchymality Characteristics

Epithelial-to-mesenchymal transition (EMT) in cancer cells confers migratory abilities, a crucial aspect in the metastasis of tumors that frequently leads to death. In multiple studies, authors proposed gene expression signatures for EMT, stemness, or mesenchymality of tumors based on bulk tumor expression profiling. However, recent studies suggested that noncancerous cells from the microenvironment or macroenvironment heavily influence such signature profiles. Here, we strengthen these findings by investigating 11 published and frequently referenced gene expression signatures that were proposed to describe EMT-related (EMT, mesenchymal, or stemness) characteristics in various cancer types. By analyses of bulk, single-cell, and pseudobulk expression data, we show that the cell type composition of a tumor sample frequently dominates scores of these EMT-related signatures. A comprehensive, integrated analysis of bulk RNA sequencing (RNA-seq) and single-cell RNA-seq data shows that stromal cells, most often fibroblasts, are the main drivers of EMT-related signature scores. We call attention to the risk of false conclusions about tumor properties when interpreting EMT-related signatures, especially in a clinical setting: high patient scores of EMT-related signatures or calls of "stemness subtypes" often result from low cancer cell content in tumor biopsies rather than cancer cell-specific stemness or mesenchymal/EMT characteristics.

SIGNIFICANCE

Cancer self-renewal and migratory abilities are often characterized via gene module expression profiles, also called EMT or stemness gene expression signatures. Using published clinical tumor samples, cancer cell lines, and single cancer cells, we highlight the dominating influence of noncancer cells in low cancer cell content biopsies on their scores. We caution on their application for low cancer cell content clinical cancer samples with the intent to assign such characteristics or subtypes.

ARID1A loss is associated with increased NRF2 signaling in human head and neck squamous cell carcinomas

Prior to the next generation sequencing and characterization of the tumor genome landscape, mutations in the SWI/SNF chromatin remodeling complex and the KEAP1-NRF2 signaling pathway were underappreciated. While these two classes of mutations appeared to independently contribute to tumor development, recent reports have demonstrated a mechanistic link between these two regulatory mechanisms in specific cancer types and cell models. In this work, we expand upon these data by exploring the relationship between mutations in BAF and PBAF subunits of the SWI/SNF complex and activation of NRF2 signal transduction across many cancer types. ARID1A/B mutations were strongly associated with NRF2 transcriptional activity in head and neck squamous carcinomas (HNSC). Many additional tumor types showed significant association between NRF2 signaling and mutation of specific components of the SWI/SNF complex. Different effects of BAF and PBAF mutations on the polarity of NRF2 signaling were observed. Overall, our results support a context-dependent functional link between SWI/SNF and NRF2 mutations across human cancers and implicate ARID1A inactivation in HPV-negative HNSC in promoting tumor progression and survival through activation of the KEAP1-NRF2 signaling pathway. The tumor-specific effects of these mutations open a new area of study for how mutations in the KEAP1-NRF2 pathway and the SWI/SNF complex contribute to cancer.

Functional impact of multi-omic interactions in lung cancer

Lung tumors are a leading cause of cancer-related death worldwide. Lung cancers are highly heterogeneous on their phenotypes, both at the cellular and molecular levels. Efforts to better understand the biological origins and outcomes of lung cancer in terms of this enormous variability often require of high-throughput experimental techniques paired with advanced data analytics. Anticipated advancements in multi-omic methodologies hold potential to reveal a broader molecular perspective of these tumors. This study introduces a theoretical and computational framework for generating network models depicting regulatory constraints on biological functions in a semi-automated way. The approach successfully identifies enriched functions in analyzed omics data, focusing on Adenocarcinoma (LUAD) and Squamous cell carcinoma (LUSC, a type of NSCLC) in the lung. Valuable information about novel regulatory characteristics, supported by robust biological reasoning, is illustrated, for instance by considering the role of genes, miRNAs and CpG sites associated with NSCLC, both novel and previously reported. Utilizing multi-omic regulatory networks, we constructed robust models elucidating omics data interconnectedness, enabling systematic generation of mechanistic hypotheses. These findings offer insights into complex regulatory mechanisms underlying these cancer types, paving the way for further exploring their molecular complexity.

The HSP90-MYC-CDK9 network drives therapeutic resistance in mantle cell lymphoma

Brexucabtagene autoleucel CAR-T therapy is highly efficacious in overcoming resistance to Bruton's tyrosine kinase inhibitors (BTKi) in mantle cell lymphoma. However, many patients relapse post CAR-T therapy with dismal outcomes. To dissect the underlying mechanisms of sequential resistance to BTKi and CAR-T therapy, we performed single-cell RNA sequencing analysis for 66 samples from 25 patients treated with BTKi and/or CAR-T therapy and conducted in-depth bioinformatics™ analysis. Our analysis revealed that MYC activity progressively increased with sequential resistance. HSP90AB1 (Heat shock protein 90 alpha family class B member 1), a MYC target, was identified as early driver of CAR-T resistance. CDK9 (Cyclin-dependent kinase 9), another MYC target, was significantly upregulated in Dual-R samples. Both HSP90AB1 and CDK9 expression were correlated with MYC activity levels. Pharmaceutical co-targeting of HSP90 and CDK9 synergistically diminished MYC activity, leading to potent anti-MCL activity. Collectively, our study revealed that HSP90-MYC-CDK9 network is the primary driving force of therapeutic resistance.

Clinical and immunological relevance of SLAMF6 expression in the tumor microenvironment of breast cancer and melanoma

Compelling evidence shows that the frequency of T cells in the tumor microenvironment correlates with prognosis as well as response to immunotherapy. However, considerable heterogeneity exists within tumor-infiltrating T cells, and significance of their genomic and transcriptomic landscape on clinical outcomes remains to be elucidated. Signaling lymphocyte activation molecule 6 (SLAMF6) is expressed on intra-tumoral progenitor-exhausted T cells, which exhibit the capacity to proliferate, self-renew and produce terminally-exhausted T cells in pre-clinical models and patients. Here, we investigated the impact of SLAMF6 expression on prognosis in two immunologically different tumor types using publicly available databases. Our findings demonstrate that high SLAMF6 expression is associated with better prognosis, expression of TCF7 (encoding T-cell factor 1), and increased gene signatures associated with conventional type 1 dendritic cells and effector function of T cells in melanoma and breast cancer. Single-cell profiling of breast cancer tumor microenvironment reveals SLAMF6 expression overlaps CD8 T cells with a T-effector signature, which includes subsets expressing TCF7, memory and effector-related genes, analogous to progenitor-exhausted T cells. These findings illustrate the significance of SLAMF6 in the tumor as a marker for better effector responses, and provide insights into the predictive and prognostic determinants for cancer patients.

Single-cell N6-methyladenosine-related genes function within the tumor microenvironment to affect the prognosis and treatment sensitivity in patients with gastric cancer

BACKGROUND

Gastric cancer (GC) ranks fifth for morbidity and third for mortality worldwide. The N6-methyladenosine (m6A) mRNA methylation is crucial in cancer biology and progression. However, the relationship between m6A methylation and gastric tumor microenvironment (TME) remains to be elucidated.

METHODS

We combined single-cell and bulk transcriptome analyses to explore the roles of m6A-related genes (MRG) in gastric TME.

RESULTS

Nine TME cell subtypes were identified from 23 samples. Fibroblasts were further grouped into four subclusters according to different cell markers. M6A-mediated fibroblasts may guide extensive intracellular communications in the gastric TME. The m6A-related genes score (MRGs) was output based on six differentially expressed single-cell m6A-related genes (SCMRDEGs), including GHRL, COL4A1, CAV1, GJA1, TIMP1, and IGFBP3. The protein expression level was assessed by immunohistochemistry. We identified the prognostic value of MRGs and constructed a nomogram model to predict GC patients' overall survival. MRGs may affect treatment sensitivity in GC patients.

CONCLUSION

Our study visualized the cellular heterogeneity of TME at the single-cell level, revealed the association between m6A mRNA modification and intracellular communication, clarified MRGs as an independent risk factor of prognosis, and provided a reference for follow-up treatment.

PRKCSH serves as a potential immunological and prognostic biomarker in pan-cancer

Protein kinase C substrate 80K-H (PRKCSH) plays a crucial role in the protein N-terminal glycosylation process, with emerging evidence implicating its involvement in tumorigenesis. To comprehensively assess PRKCSH's significance across cancers, we conducted a pan-cancer analysis using data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE). We assessed aberrant PRKCSH mRNA and protein expression, examined its prognostic implications, and identified correlations with clinical features, tumor mutational burden (TMB), microsatellite instability (MSI), and tumor immunity across cancer types. We explored PRKCSH gene alterations, DNA methylation, and their impact on patient prognosis. Gene Set Enrichment Analysis (GSEA) and single-cell analysis revealed potential biological roles. Additionally, we investigated drug susceptibility and conducted Connectivity Map (Cmap) analysis. Key findings revealed that PRKCSH exhibited overexpression in most tumors, with a significant association with poor overall survival (OS) in six cancer types. Notably, PRKCSH expression demonstrated variations across disease stages, primarily increasing in advanced stages among eleven tumor types. Moreover, PRKCSH exhibited significant correlations with TMB in five cancer categories, MSI in eight, and displayed associations with immune cell populations in pan-cancer analysis. Genetic variations in PRKCSH were identified across 26 tumor types, suggesting favorable disease-free survival. Furthermore, PRKCSH methylation displayed a significant negative correlation with its expression in 27 tumor types, with a marked decrease compared to normal tissues in ten tumors. Cmap predicted 24 potential therapeutic small molecules in over four cancer types. This study highlights that PRKCSH, as a potential oncogene, may be a promising prognostic marker and therapeutic target of immunotherapy for a range of malignancies.

Proteogenomic characterization of small cell lung cancer identifies biological insights and subtype-specific therapeutic strategies

We performed comprehensive proteogenomic characterization of small cell lung cancer (SCLC) using paired tumors and adjacent lung tissues from 112 treatment-naive patients who underwent surgical resection. Integrated multi-omics analysis illustrated cancer biology downstream of genetic aberrations and highlighted oncogenic roles of FAT1 mutation, RB1 deletion, and chromosome 5q loss. Two prognostic biomarkers, HMGB3 and CASP10, were identified. Overexpression of HMGB3 promoted SCLC cell migration via transcriptional regulation of cell junction-related genes. Immune landscape characterization revealed an association between ZFHX3 mutation and high immune infiltration and underscored a potential immunosuppressive role of elevated DNA damage response activity via inhibition of the cGAS-STING pathway. Multi-omics clustering identified four subtypes with subtype-specific therapeutic vulnerabilities. Cell line and patient-derived xenograft-based drug tests validated the specific therapeutic responses predicted by multi-omics subtyping. This study provides a valuable resource as well as insights to better understand SCLC biology and improve clinical practice.

CLEC19A overexpression inhibits tumor cell proliferation/migration and promotes apoptosis concomitant suppression of PI3K/AKT/NF-κB signaling pathway in glioblastoma multiforme

BACKGROUND

GBM is the most frequent malignant primary brain tumor in humans. The CLEC19A is a member of the C-type lectin family, which has a high expression in brain tissue. Herein, we sought to carry out an in-depth analysis to pinpoint the role of CLEC19A expression in GBM.

METHODS

To determine the localization of CLEC19A, this protein was detected using Western blot, Immunocytochemistry/Immunofluorescence, and confocal microscopy imaging. CLEC19A expression in glioma cells and tissues was evaluated by qRT-PCR. Cell viability, proliferation, migration, and apoptosis were examined through MTT assay, CFSE assay, colony formation, wound healing assay, transwell test, and flow cytometry respectively after CLEC19A overexpression. The effect of CLEC19A overexpression on the PI3K/AKT/NF-κB signaling pathway was investigated using Western blot. An in vivo experiment substantiated the in vitro results using the glioblastoma rat models.

RESULTS

Our in-silico analysis using TCGA data and measuring CLEC19A expression level by qRT-PCR determined significantly lower expression of CLEC19A in human glioma tissues compared to healthy brain tissues. By employment of ICC/IF, confocal microscopy imaging, and Western blot we could show that CLEC19A is plausibly a secreted protein. Results obtained from several in vitro readouts showed that CLEC19A overexpression in U87 and C6 glioma cell lines is associated with the inhibition of cell proliferation, viability, and migration. Further, qRT-PCR and Western blot analysis showed CLEC19A overexpression could reduce the expression levels of PI3K, VEGFα, MMP2, and NF-κB and increase PTEN, TIMP3, RECK, and PDCD4 expression levels in glioma cell lines. Furthermore, flow cytometry results revealed that CLEC19A overexpression was associated with significant cell cycle arrest and promotion of apoptosis in glioma cell lines. Interestingly, using a glioma rat model we could substantiate that CLEC19A overexpression suppresses glioma tumor growth.

CONCLUSIONS

To our knowledge, this is the first report providing in-silico, molecular, cellular, and in vivo evidences on the role of CLEC19A as a putative tumor suppressor gene in GBM. These results enhance our understanding of the role of CLEC19A in glioma and warrant further exploration of CLEC19A as a potential therapeutic target for GBM.

A heavy-tailed model for analyzing miRNA-seq raw read counts

This article addresses the limitations of existing statistical models in analyzing and interpreting highly skewed miRNA-seq raw read count data that can range from zero to millions. A heavy-tailed model using discrete stable distributions is proposed as a novel approach to better capture the heterogeneity and extreme values commonly observed in miRNA-seq data. Additionally, the parameters of the discrete stable distribution are proposed as an alternative target for differential expression analysis. An R package for computing and estimating the discrete stable distribution is provided. The proposed model is applied to miRNA-seq raw counts from the Norwegian Women and Cancer Study (NOWAC) and the Cancer Genome Atlas (TCGA) databases. The goodness-of-fit is compared with the popular Poisson and negative binomial distributions, and the discrete stable distributions are found to give a better fit for both datasets. In conclusion, the use of discrete stable distributions is shown to potentially lead to more accurate modeling of the underlying biological processes.

Stroma AReactive Invasion Front Areas (SARIFA) proves prognostic relevance in gastric carcinoma and is based on a tumor-adipocyte interaction indicating an altered immune response

BACKGROUND

Recently, we presented Stroma AReactive Invasion Front Areas (SARIFA) as a new histomorphologic negative prognostic biomarker in gastric cancer. It is defined as direct contact between tumor cells and fat cells. The aim of this study was to further elucidate the underlying genomic, transcriptional, and immunological mechanisms of the SARIFA phenomenon.

METHODS

To address these questions, SARIFA was classified on H&E-stained tissue sections of three cohorts: an external cohort (n = 489, prognostic validation), the TCGA-STAD cohort (n = 194, genomic and transcriptomic analysis), and a local cohort (n = 60, digital spatial profiling (whole transcriptome) and double RNA in situ hybridization/immunostaining of cytokines).

RESULTS

SARIFA status proved to be an independent negative prognostic factor for overall survival in an external cohort of gastric carcinomas. In TCGA-STAD cohort, SARIFA is not driven by distinct genomic alterations, whereas the gene expression analyses showed an upregulation of FABP4 in SARIFA-positive tumors. In addition, the transcriptional regulations of white adipocyte differentiation, triglyceride metabolism, and catabolism were upregulated in pathway analyses. In the DSP analysis of SARIFA-positive tumors, FABP4 and the transcriptional regulation of white adipocyte differentiation were upregulated in macrophages. Additionally, a significantly lower expression of the cytokines IL6 and TNFα was observed at the invasion front.

CONCLUSIONS

SARIFA proves to be a strong negative prognostic biomarker in advanced gastric cancer, implicating an interaction of tumor cells with tumor-promoting adipocytes with crucial changes in tumor cell metabolism. SARIFA is not driven by tumor genetics but is very likely driven by an altered immune response as a causative mechanism.

FAK suppresses antigen processing and presentation to promote immune evasion in pancreatic cancer

OBJECTIVE

Immunotherapy for the treatment of pancreatic ductal adenocarcinoma (PDAC) has shown limited efficacy. Poor CD8 T-cell infiltration, low neoantigen load and a highly immunosuppressive tumour microenvironment contribute to this lack of response. Here, we aimed to further investigate the immunoregulatory function of focal adhesion kinase (FAK) in PDAC, with specific emphasis on regulation of the type-II interferon response that is critical in promoting T-cell tumour recognition and effective immunosurveillance.

DESIGN

We combined CRISPR, proteogenomics and transcriptomics with mechanistic experiments using a KrasG12Dp53R172H mouse model of pancreatic cancer and validated findings using proteomic analysis of human patient-derived PDAC cell lines and analysis of publicly available human PDAC transcriptomics datasets.

RESULTS

Loss of PDAC cell-intrinsic FAK signalling promotes expression of the immunoproteasome and Major Histocompatibility Complex class-I (MHC-I), resulting in increased antigen diversity and antigen presentation by FAK-/- PDAC cells. Regulation of the immunoproteasome by FAK is a critical determinant of this response, optimising the physicochemical properties of the peptide repertoire for high affinity binding to MHC-I. Expression of these pathways can be further amplified in a STAT1-dependent manner via co-depletion of FAK and STAT3, resulting in extensive infiltration of tumour-reactive CD8 T-cells and further restraint of tumour growth. FAK-dependent regulation of antigen processing and presentation is conserved between mouse and human PDAC, but is lost in cells/tumours with an extreme squamous phenotype.

CONCLUSION

Therapies aimed at FAK degradation may unlock additional therapeutic benefit for the treatment of PDAC through increasing antigen diversity and promoting antigen presentation.

Methylation-directed regulatory networks determine enhancing and silencing of mutation disease driver genes and explain inter-patient expression variation

BACKGROUND

Common diseases manifest differentially between patients, but the genetic origin of this variation remains unclear. To explore possible involvement of gene transcriptional-variation, we produce a DNA methylation-oriented, driver-gene-wide dataset of regulatory elements in human glioblastomas and study their effect on inter-patient gene expression variation.

RESULTS

In 175 of 177 analyzed gene regulatory domains, transcriptional enhancers and silencers are intermixed. Under experimental conditions, DNA methylation induces enhancers to alter their enhancing effects or convert into silencers, while silencers are affected inversely. High-resolution mapping of the association between DNA methylation and gene expression in intact genomes reveals methylation-related regulatory units (average size = 915.1 base-pairs). Upon increased methylation of these units, their target-genes either increased or decreased in expression. Gene-enhancing and silencing units constitute cis-regulatory networks of genes. Mathematical modeling of the networks highlights indicative methylation sites, which signified the effect of key regulatory units, and add up to make the overall transcriptional effect of the network. Methylation variation in these sites effectively describe inter-patient expression variation and, compared with DNA sequence-alterations, appears as a major contributor of gene-expression variation among glioblastoma patients.

CONCLUSIONS

We describe complex cis-regulatory networks, which determine gene expression by summing the effects of positive and negative transcriptional inputs. In these networks, DNA methylation induces both enhancing and silencing effects, depending on the context. The revealed mechanism sheds light on the regulatory role of DNA methylation, explains inter-individual gene-expression variation, and opens the way for monitoring the driving forces behind deferential courses of cancer and other diseases.

Analysis of the Tumor Immune Microenvironment (TIME) in Clear Cell Renal Cell Carcinoma (ccRCC) Reveals an M0 Macrophage-Enriched Subtype: An Exploration of Prognostic and Biological Characteristics of This Immune Phenotype

There is a need to optimize the treatment of clear cell renal cell carcinoma (ccRCC) patients at high recurrence risk after nephrectomy. We sought to elucidate the tumor immune microenvironment (TIME) of localized ccRCC and understand the prognostic and predictive characteristics of certain features. The discovery cohort was clinically localized patients in the TCGA-Kidney Renal Clear Cell Carcinoma (KIRC) project (n = 382). We identified an M0 macrophage-enriched cluster (n = 25) in the TCGA-KIRC cohort. This cluster's median progression-free survival (PFS) and overall survival (OS) were 40.4 and 45.3 months, respectively, but this was not reached in the others (p = 0.0003 and <0.0001, respectively). Gene set enrichment (GSEA) analysis revealed an enrichment of epithelial to mesenchymal transition and cell cycle progression genes within this cluster, and these patients also had a lower predicted response to immune checkpoint blockade (ICB) (4% vs. 20-34%). An M0-enriched cluster (n = 9) with shorter PFS (p = 0.0006) was also identified in the Clinical Proteomics Tumor Analysis Consortium (CPTAC) cohort (n = 94). Through this characterization of the TIME in ccRCC, a cluster of patients defined by enrichment in M0 macrophages was identified that demonstrated poor prognosis and lower predicted ICB response. Pending further validation, this signature can identify localized ccRCC patients at high risk of recurrence after nephrectomy and who may require therapeutic approaches beyond ICB monotherapy.

Effect of activating cancer-associated fibroblasts biomarker TNC on immune cell infiltration and prognosis in breast cancer

BACKGROUND

Cancer-associated fibroblasts (CAFs) are the most important components of the tumor microenvironment (TME). CAFs are heterogeneous and involved in tumor tumorigenesis and drug resistance, contributing to TME remodeling and predicting clinical outcomes as prognostic factors. However, the effect of CAFs the TME and the prognosis of patients with breast cancer (BC) is not fully understood. This study investigated the correlation between CAFs-activating biomarkers immune cell infiltration and survival in patients with breast cancer.

METHODS

RNA sequencing data and survival information for patients with breast cancer were downloaded from The Cancer Genome Atlas (TCGA) using R software. We then analyzed the correlation between CAFs-expressing biomarkers and immune cells using the clusterProfiler package, and evaluated the prognostic role of appealing genes using the Survminer package. Immunohistochemical (IHC) staining was used to determine the expression levels of TNC in 160 breast cancer samples pathologically diagnosed as invasive ductal carcinoma that were not otherwise specified (IDC-NOS).

RESULTS

Data analysis showed that CAFs-expressing genes was higher than in normal tissues (p < 0.05). Pathway enrichment revealed that the overexpression of CAFs-related genes was mainly enriched in the focal adhesion and phosphoinositol-3 kinase-serine/threonine kinase (PI3K-AKT) signaling pathways. Immune infiltration analysis suggested that high expression of CAFs-related genes was significantly positively correlated with the infiltration of naive B cells and resting dendritic cells and inversely correlated with macrophages cell infiltration. In addition, high TNC expression in tumor cells was associated with the most adverse clinicopathological features and reduced metastasis-free survival (MFS) (hazard ratio (HR) 0.574, 95% confidence interval (CI) 0.404-0.815, p = 0.035).

CONCLUSIONS

This study found that CAFs may participate in immunosuppression and regulate tumor cell proliferation and invasion. High TNC expression is associated with several adverse clinicopathological features, and high TNC expression in tumor cells has been identified as an independent prognostic factor for IDC-NOS.

Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects

Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional observational studies report associations of multiple circulating proteins with PCa risk. However, the existing findings may be subject to incoherent biases of conventional epidemiologic studies. To better characterize their associations, herein, we evaluated associations of genetically predicted concentrations of plasma proteins with PCa risk. We developed comprehensive genetic prediction models for protein levels in plasma. After testing 1308 proteins in 79 194 cases and 61 112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL, 24 proteins showed significant associations with PCa risk, including 16 previously reported proteins and eight novel proteins. Of them, 14 proteins showed negative associations and 10 showed positive associations with PCa risk. For 18 of the identified proteins, potential functional somatic changes of encoding genes were detected in PCa patients in The Cancer Genome Atlas (TCGA). Genes encoding these proteins were significantly involved in cancer-related pathways. We further identified drugs targeting the identified proteins, which may serve as candidates for drug repurposing for treating PCa. In conclusion, this study identifies novel protein biomarker candidates for PCa risk, which may provide new perspectives on the etiology of PCa and improve its therapeutic strategies.

Single-cell RNA sequencing revealed potential targets for immunotherapy studies in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a solid tumor prone to chemotherapy resistance, and combined immunotherapy is expected to bring a breakthrough in HCC treatment. However, the tumor and tumor microenvironment (TME) of HCC is highly complex and heterogeneous, and there are still many unknowns regarding tumor cell stemness and metabolic reprogramming in HCC. In this study, we combined single-cell RNA sequencing data from 27 HCC tumor tissues and 4 adjacent non-tumor tissues, and bulk RNA sequencing data from 374 of the Cancer Genome Atlas (TCGA)-liver hepatocellular carcinoma (LIHC) samples to construct a global single-cell landscape atlas of HCC. We analyzed the enrichment of signaling pathways of different cells in HCC, and identified the developmental trajectories of cell subpopulations in the TME using pseudotime analysis. Subsequently, we performed transcription factors regulating different subpopulations and gene regulatory network analysis, respectively. In addition, we estimated the stemness index of tumor cells and analyzed the intercellular communication between tumors and key TME cell clusters. We identified novel HCC cell clusters that specifically express HP (HCC_HP), which may lead to higher tumor differentiation and tumor heterogeneity. In addition, we found that the HP gene expression-positive neutrophil cluster (Neu_AIF1) had extensive and strong intercellular communication with HCC cells, tumor endothelial cells (TEC) and cancer-associated fibroblasts (CAF), suggesting that clearance of this new cluster may inhibit HCC progression. Furthermore, ErbB signaling pathway and GnRH signaling pathway were found to be upregulated in almost all HCC tumor-associated stromal cells and immune cells, except NKT cells. Moreover, the high intercellular communication between HCC and HSPA1-positive TME cells suggests that the immune microenvironment may be reprogrammed. In summary, our present study depicted the single-cell landscape heterogeneity of human HCC, identified new cell clusters in tumor cells and neutrophils with potential implications for immunotherapy research, discovered complex intercellular communication between tumor cells and TME cells.

DNA methylation-based depiction of the immune microenvironment and immune-associated long non-coding RNAs in oral cavity squamous cell carcinomas

Oral cavity squamous cell carcinoma (OSCC) is a complex and dynamic disease characterized by clinicopathological and molecular heterogeneity. Spatial and temporal heterogeneity of cell subpopulations has been associated with cancer progression and implicated in the prognosis and therapy response. Emerging evidence indicates that aberrant epigenetic profiles in OSCC may foster an immunosuppressive tumor microenvironment by modulating the expression of immune-related long non-coding RNAs (lncRNAs). DNA methylation analysis was performed in 46 matched OSCC and normal adjacent tissue samples using a genome-wide platform (Infinium HumanMethylation450 BeadChip). Reference-based computational deconvolution (MethylCIBERSORT) was applied to infer the immune cell composition of the bulk samples. The expression levels of genes encoding immune markers and differentially methylated lncRNAs were investigated using The Cancer Genome Atlas dataset. OSCC specimens presented distinct immune cell composition, including the enrichment of monocyte lineage cells, natural killer cells, cytotoxic T-lymphocytes, regulatory T-lymphocytes, and neutrophils. In contrast, B-lymphocytes, effector T-lymphocytes, and fibroblasts were diminished in tumor samples. The hypomethylation of three immune-associated lncRNAs (MEG3, MIR155HG, and WFDC21P) at individual CpG sites was confirmed by bisulfite-pyrosequencing. Also, the upregulation of a set of immune markers (FOXP3, GZMB, IL10, IL2RA, TGFB, IFNG, TDO2, IDO1, and HIF1A) was detected. The immune cell composition, immune markers alteration, and dysregulation of immune-associated lncRNAs reinforce the impact of the immune microenvironment in OSCC. These concurrent factors contribute to tumor heterogeneity, suggesting that epi-immunotherapy could be an efficient alternative to treat OSCC.

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.