Multiple Independent Datasets Research Articles

Differential whole-genome doubling based signatures for improvement on clinical outcomes and drug response in patients with breast cancer

Whole genome doublings (WGD), a hallmark of human cancer, is pervasive in breast cancer patients. However, the molecular mechanism of the complete impact of WGD on survival and treatment response in breast cancer remains unclear. To address this, we performed a comprehensive and systematic analysis of WGD, aiming to identify distinct genetic alterations linked to WGD and highlight its improvement on clinical outcomes and treatment response for breast cancer. A linear regression model along with weighted gene co-expression network analysis (WGCNA) was applied on The Cancer Genome Atlas (TCGA) dataset to identify critical genes related to WGD. Further Cox regression models with random selection were used to optimize the most useful prognostic markers in the TCGA dataset. The clinical implication of the risk model was further assessed through prognostic impact evaluation, tumor stratification, functional analysis, genomic feature difference analysis, drug response analysis, and multiple independent datasets for validation. Our findings revealed a high aneuploidy burden, chromosomal instability (CIN), copy number variation (CNV), and mutation burden in breast tumors exhibiting WGD events. Moreover, 247 key genes associated with WGD were identified from the distinct genomic patterns in the TCGA dataset. A risk model consisting of 22 genes was optimized from the key genes. High-risk breast cancer patients were more prone to WGD and exhibited greater genomic diversity compared to low-risk patients. Some oncogenic signaling pathways were enriched in the high-risk group, while primary immune deficiency pathways were enriched in the low-risk group. We also identified a risk gene, ANLN (anillin), which displayed a strong positive correlation with two crucial WGD genes, KIF18A and CCNE2. Tumors with high expression of ANLN were more prone to WGD events and displayed worse clinical survival outcomes. Furthermore, the expression levels of these risk genes were significantly associated with the sensitivities of BRCA cell lines to multiple drugs, providing valuable insights for targeted therapies. These findings will be helpful for further improvement on clinical outcomes and contribution to drug development in breast cancer.

Abstract 7323: Tissue resident memory T cell abundances impact non-small-cell lung cancer immune microenvironment and patient prognosis

Abstract Tissue resident memory T cells (TRM) are a specialized subset of long-lived memory T cells that reside in peripheral tissues. However, whether TRM exerts any immunosurveillance role in the tumor immune microenvironment (TIME) and progression of non-small-cell lung cancer (NSCLC), which accounts for 85% of all lung cancers, remains unclear. Our comprehensive analysis of multiple independent single-cell and bulk RNA-seq datasets of patient NSCLC samples generated reliable, unique TRM signatures, through which we could infer the abundance of TRM in NSCLC. We discovered that TRM abundance is consistently positively correlated with CD4+ T helper 1 cells, M1 macrophages, and resting dendritic cells in TIME and significantly impacts the prognosis of NSCLC patients. In addition, TRM signatures are strongly associated with immune checkpoint genes and the prognosis of NSCLC patients, suggesting that TRM signatures are promising prognostic markers for immunotherapy in NSCLC. We then built a machine learning model to predict patient survival based on the TRM signatures and immune related genes. The accuracy of the model was validated by Kaplan-Meyer survival analysis, receiver operating characteristic curves, principal component analysis, and t-distributed random neighbor embedding. We developed a 4-gene risk score that effectively stratified patients into low-risk and high-risk categories. The patients with high-risk scores had significantly lower overall survival than patients with low-risk. The prognostic value of the risk score was independently validated by the Cancer Genome Atlas Program (TCGA) dataset and multiple independent NSCLC patient datasets. Notably, low-risk NSCLC patients with higher TRM infiltration exhibited enhanced T-cell activation, macrophage regulation, and other TIME immune responses related pathways, indicating a more active immune profile benefitting from immunotherapy. Altogether, this study provides valuable insights into the complex interactions between NSCLC TRM and TIME and their impact on patient prognosis, highlighting the importance of TRM in shaping the NSCLC microenvironment. The development of a simplified 4-gene risk score provides a practical prognostic marker for risk stratification. Keywords: Tissue resident memory T cell, non-small-cell lung cancer, prognosis, tumor immune microenvironment, machine learning Citation Format: Aidan Shen, Aliesha Garrett, Junhua Mai, Yangzhi Zhu, Chongming Jiang. Tissue resident memory T cell abundances impact non-small-cell lung cancer immune microenvironment and patient prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7323.

Abstract 4947: Uncovering the molecular basis for clinically relevant sphingolipidomic subtypes in acute myeloid leukemia

Abstract Acute myeloid leukemia (AML) has been extensively studied at the genomic level, resulting in various genomic classifications. Despite the progress, three key challenges remain: 1) a significant proportion of patients lack identified genomic features, 2) a discrepancy exists between genomic risk classification and clinical outcomes, and 3) many mutated targets remain non-druggable. There is an urgent need for an enhanced AML risk classification that goes beyond genomic aberrations to guide effective therapeutics. Recent research, including our own, has increasingly linked AML pathogenesis and therapeutic resistance with dysfunctional sphingolipid metabolism, a family of bioactive molecules crucial for cellular functions. In a recent manuscript, we identified two robust sphingolipidomic clusters in AML, characterized by reciprocal abundances of hexosylceramide (Hex) and sphingomyelin (SM) species (HexloSMhi, HexhiSMlo). These clusters correlate with latent transcriptional states and stratify patient groups in multiple independent datasets such as TCGA, and BeatAML, with the HexloSMhi cluster representing a high-risk subgroup associated with poor clinical outcomes. These findings underscore the role of sphingolipids in refining AML risk assessment and creating avenues for discovery and therapeutic intervention. However, the signaling states driving these sphingolipidomic clusters and the mechanisms elevating risk in the HexloSMhi cluster require further investigation. To identify regulatory genes for the AML subtypes, we constructed gene regulatory networks using differentially expressed genes. Focusing on the high-risk subtype, we identified highly connected genes ranked by connectivity, including KDM1A, GSK3β, LCK, and STAT5A. We found that higher STAT5A abundances in the HexloSMhi subtype, particularly correlated with pro-survival Bcl-xL proteins. Furthermore, we identified STAT5 protein abundance as a key variable associated with drug sensitivity from pharmacological screening with sphingolipid pathway inhibitors and AML therapeutics 30 AML cell lines and patient samples. This is encouraging as STAT5 is druggable as evidenced in the NIH-Pharos druggable genome database, and several small-molecule inhibitors are currently in clinical trials. Analysis of gene expression data suggest that STAT5 inhibition, through genetic or pharmacological means, downregulates cell cycle-related genes and significantly alters sphingolipid gene expression. Recent work by others indicates that sphingolipid-catalyzing enzymes regulate STAT5 activity in leukemias, with ceramides acting as a second messenger in cells. Taken together, this suggests a possibility of a positive feedback loop between sphingolipid homeostasis and activity of STAT5; the details of this interaction require further investigation. Citation Format: B Bishal Paudel, Su-Fern Tan, Johnson Ung, David Claxton, David J. Feith, Thomas P. Loughran, Kevin A. Janes. Uncovering the molecular basis for clinically relevant sphingolipidomic subtypes in acute myeloid leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4947.

Abstract 6935: Non-genetic routes of lung cancer evolution

Abstract Cancer cell plasticity underlies the progression of several solid tumors. In lung adenocarcinoma, morphological changes characterizing the transition from indolent to aggressive disease exhibit profound epigenetic and transcriptional reprogramming1. However, the extent, features, and drivers of cancer cell plasticity in the progression of human lung adenocarcinoma is still largely unexplored. Here, we assembled and analyzed a meta-cohort of 119 human lung tumors analyzed by single-cell RNA-sequencing, for a total of ~730,000 cells. Cancer cell heterogeneity was the result of differential activation of 17 transcriptional programs associated with common cancer phenotypes, such as proliferation, cell migration, and response to hypoxia, as well as alveolar lineage identity (AT2). In particular, AT2-specific expression of MHC-II was down-regulated with the progression of the disease and markedly reduced in KEAP1 and/or STK11 mutated samples, providing a rationale for the association between these mutations and immunotherapy resistance. Co-activation of transcriptional programs clustered tumor cells in 4 major cell states, which emerged with the progression of the disease. To explore spatial distribution of different cancer cell states, we generated and analyzed multiple spatial transcriptomics and proteomics lung adenocarcinoma cohorts. To this purpose we developed a new algorithm called CellCharter2. CellCharter identified distinct cancer-enriched niches within individual samples, corresponding to the cancer cell states that we discovered in the scRNA-seq cohort and exhibited specific interactions with the tumor microenvironment (TME). In particular, we identified a cancer cell state characterized by response-to-hypoxia and migratory phenotypes, which spatially segregated from highly proliferative cancer cell niches and formed specific interactions with tumor associated neutrophils. This cell state was enriched in advanced primary samples, it uniquely characterized metastatic samples, and was an independent marker of poor prognosis across multiple independent datasets. Overall, our work uncovered cell plasticity trajectories associated with the progression of lung adenocarcinoma and shed new light into the tumor-TME interactions at their origin. 1 Tavernari et al., Cancer Discovery, 20212 Varrone et al, Nat. Genetics, (in press) Citation Format: Giovanni Ciriello, Daniele Tavernari, Marco Varrone. Non-genetic routes of lung cancer evolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6935.

Multiomics profiling of buffy coat and plasma unveils etiology-specific signatures in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality worldwide. Despite identification of several biomarkers for HCC diagnosis, challenges such as low sensitivity and intratumoral heterogeneity have impeded early detection, highlighting the need for etiology-specific blood biomarkers. We generated whole-transcriptome sequencing (WTS) and targeted proteome data from buffy coat and plasma samples from HCC patients. By integrating etiological information on viral infection, we investigated the etiology-specific gene expression landscape at the blood level. Validation of differentially expressed genes (DEGs) was performed using publicly available RNA-seq datasets and qRT‒PCR with AUC analyses. Differential expression analyses with multiomics data revealed distinct gene expression profiles between HBV-associated HCC and nonviral HCC, indicating the presence of etiology-specific blood biomarkers. The identified DEGs were validated across multiple independent datasets, underscoring their utility as biomarkers. Additionally, single-cell RNA-seq analysis of HCC confirmed differences in DEG expression across distinct immune cell types. Our buffy coat WTS data and plasma proteome data may serve as reliable sources for identifying etiology-specific blood biomarkers of HCC and might contribute to discovery of therapeutic targets for HCC across different etiologies.

Single-cell integrative analysis reveals consensus cancer cell states and clinical relevance in breast cancer

High heterogeneity and complex interactions of malignant cells in breast cancer has been recognized as a driver of cancer progression and therapeutic failure. However, complete understanding of common cancer cell states and their underlying driver factors remain scarce and challenging. Here, we revealed seven consensus cancer cell states recurring cross patients by integrative analysis of single-cell RNA sequencing data of breast cancer. The distinct biological functions, the subtype-specific distribution, the potential cells of origin and the interrelation of consensus cancer cell states were systematically elucidated and validated in multiple independent datasets. We further uncovered the internal regulons and external cell components in tumor microenvironments, which contribute to the consensus cancer cell states. Using the state-specific signature, we also inferred the abundance of cells with each consensus cancer cell state by deconvolution of large breast cancer RNA-seq cohorts, revealing the association of immune-related state with better survival. Our study provides new insights for the cancer cell state composition and potential therapeutic strategies of breast cancer.

A multi-demand operating system underlying diverse cognitive tasks

The existence of a multiple-demand cortical system with an adaptive, domain-general, role in cognition has been proposed, but the underlying dynamic mechanisms and their links to cognitive control abilities are poorly understood. Here we use a probabilistic generative Bayesian model of brain circuit dynamics to determine dynamic brain states across multiple cognitive domains, independent datasets, and participant groups, including task fMRI data from Human Connectome Project, Dual Mechanisms of Cognitive Control study and a neurodevelopment study. We discover a shared brain state across seven distinct cognitive tasks and found that the dynamics of this shared brain state predicted cognitive control abilities in each task. Our findings reveal the flexible engagement of dynamic brain processes across multiple cognitive domains and participant groups, and uncover the generative mechanisms underlying the functioning of a domain-general cognitive operating system. Our computational framework opens promising avenues for probing neurocognitive function and dysfunction.

An empirical experimental observations and MD simulation data-based model for the material properties of confined fluids in nano/Angstrom size tubes

Abstract The transport of fluids in nanometer and Angstrom-sized pores has gotten much attention because of its potential uses in nanotechnology, energy storage, and healthcare sectors. Understanding the distinct material properties of fluids in such close confinement is critical for enhancing their performance in various applications. These properties dictate the fluid’s behavior and play a crucial role in determining flow dynamics, transport processes, and, ultimately, the performance of nanoscale devices. Remarkably, many researchers observed that the size of the geometry, such as the diameter of the confining nanotube, exerts a profound and intriguing influence on the material properties of nanoconfined fluids, including on the critical parameters such as density, viscosity, and slip length. Many researchers tried to model these material properties: viscosity η, density ρ, and slip λ using various models with many dependencies on the tube diameter. It is somewhat confusing and tough to decide which model is appropriate and needs to be incorporated in the numerical simulation. In this paper, we tried to propose a simple single equation for each nano confined material property such as for density ρ ( D ) / ρ o = a + b / ( D − c ) n , viscosity η ( D ) / η o = a + b / ( D − c ) n , and the slip length λ(D) = λ 1 D e −n D + λ o (where a, b, c, n, λ 1, λ o are the free fitting parameters). We model a wealth of previous experimental and MD simulation data from the literature using our proposed model for each material property of nanoconfined fluids at the nanometer and Angstrom scales. Our single proposed equation effectively captures and models all the data, even though many different models have been employed in the existing literature to describe the same material property. Our proposed model exhibits exceptional agreement with multiple independent datasets from the experimental observations and molecular dynamics simulations. Additionally, the model possesses the advantageous properties of continuity and a continuous derivative, so the proposed model is well-suited for integration into numerical simulations. Further, the proposed models also obey the far boundary conditions, i.e., when tube diameter D ⟹ ∞, the material properties tend to the bulk properties of the fluid. Due to the models’ simplicity, smooth, and generic nature, this heuristic model holds promise to apply in simulations to design and optimize nanoscale devices.

Multi-omics analysis of disulfidptosis regulators and therapeutic potential reveals glycogen synthase 1 as a disulfidptosis triggering target for triple-negative breast cancer.

Disruption of disulfide homeostasis during biological processes can have fatal consequences. Excess disulfides induce cell death in a novel manner, termed as "disulfidptosis." However, the specific mechanism of disulfidptosis has not yet been elucidated. To determine the cancer types sensitive to disulfidptosis and outline the corresponding treatment strategies, we firstly investigated the crucial functions of disulfidptosis regulators pan-cancer at multi-omics levels. We found that different tumor types expressed dysregulated levels of disulfidptosis regulators, most of which had an impact on tumor prognosis. Moreover, we calculated the disulfidptosis activity score in tumors and validated it using multiple independent datasets. Additionally, we found that disulfidptosis activity was correlated with classic biological processes and pathways in various cancers. Disulfidptosis activity was also associated with tumor immune characteristics and could predict immunotherapy outcomes. Notably, the disulfidptosis regulator, glycogen synthase 1 (GYS1), was identified as a promising target for triple-negative breast cancer and validated via in vitro and in vivo experiments. In conclusion, our study elucidated the complex molecular phenotypes and clinicopathological correlations of disulfidptosis regulators in tumors, laying a solid foundation for the development of disulfidptosis-targeting strategies for cancer treatment.

DiscoTope-3.0: improved B-cell epitope prediction using inverse folding latent representations.

Accurate computational identification of B-cell epitopes is crucial for the development of vaccines, therapies, and diagnostic tools. However, current structure-based prediction methods face limitations due to the dependency on experimentally solved structures. Here, we introduce DiscoTope-3.0, a markedly improved B-cell epitope prediction tool that innovatively employs inverse folding structure representations and a positive-unlabelled learning strategy, and is adapted for both solved and predicted structures. Our tool demonstrates a considerable improvement in performance over existing methods, accurately predicting linear and conformational epitopes across multiple independent datasets. Most notably, DiscoTope-3.0 maintains high predictive performance across solved, relaxed and predicted structures, alleviating the need for experimental structures and extending the general applicability of accurate B-cell epitope prediction by 3 orders of magnitude. DiscoTope-3.0 is made widely accessible on two web servers, processing over 100 structures per submission, and as a downloadable package. In addition, the servers interface with RCSB and AlphaFoldDB, facilitating large-scale prediction across over 200 million cataloged proteins. DiscoTope-3.0 is available at: https://services.healthtech.dtu.dk/service.php?DiscoTope-3.0.

GCclassifier: An R package for the prediction of molecular subtypes of gastric cancer

Gastric cancer (GC) is one of the most commonly diagnosed malignancies, threatening millions of lives worldwide each year. Importantly, GC is a heterogeneous disease, posing a significant challenge to the selection of patients for more optimized therapy. Over the last decades, extensive community effort has been spent on dissecting the heterogeneity of GC, leading to the identification of distinct molecular subtypes that are clinically relevant. However, so far, no tool is publicly available for GC subtype prediction, hindering the research into GC subtype-specific biological mechanisms, the design of novel targeted agents, and potential clinical applications. To address the unmet need, we developed an R package GCclassifier for predicting GC molecular subtypes based on gene expression profiles. To facilitate the use by non-bioinformaticians, we also provide an interactive, user-friendly web server implementing the major functionalities of GCclassifier. The predictive performance of GCclassifier was demonstrated using case studies on multiple independent datasets.

Autosurv: interpretable deep learning framework for cancer survival analysis incorporating clinical and multi-omics data

Accurate prognosis for cancer patients can provide critical information for optimizing treatment plans and improving life quality. Combining omics data and demographic/clinical information can offer a more comprehensive view of cancer prognosis than using omics or clinical data alone and can also reveal the underlying disease mechanisms at the molecular level. In this study, we developed and validated a deep learning framework to extract information from high-dimensional gene expression and miRNA expression data and conduct prognosis prediction for breast cancer and ovarian-cancer patients using multiple independent multi-omics datasets. Our model achieved significantly better prognosis prediction than the current machine learning and deep learning approaches in various settings. Moreover, an interpretation method was applied to tackle the “black-box” nature of deep neural networks and we identified features (i.e., genes, miRNA, demographic/clinical variables) that were important to distinguish predicted high- and low-risk patients. The significance of the identified features was partially supported by previous studies.

EPRIM: An approach of identifying cancer immune-related epigenetic regulators

Epigenetic regulation contributes to the dysregulation of gene expression involved in cancer biology. Nevertheless, the roles of epigenetic regulators (ERs) in tumor immunity and immune response remain basically unclear. Here, we developed the epigenetic regulator in immunology (EPRIM) approach to identify immune-related ERs and comprehensively dissected the ER regulation in tumor immune response across 33 cancers. The identified immune-related ERs were related to immune infiltration and could stratify cancer patients into two risk groups in multiple independent datasets. These patient groups were characterized by distinct immune functions, immune infiltrates, driver gene mutations, and prognoses. Furthermore, we constructed an immune ER-based signature and highlighted its potential utility in predicting clinical benefit from immunotherapy and selecting therapeutic agents. Taken together, our identification and evaluation of immune-related ERs highlight the usefulness of EPRIM for the understanding of ERs in immune regulation and the clinical relevance in evaluation of cancer patient prognosis and response to immune checkpoint blockade therapy.

Identification and Analysis of Immune Microenvironment-Related Genes for Keloid Risk Prediction and Their Effects on Keloid Proliferation and Migration.

Keloid is a kind of proliferative scar with continuous growth, no restriction and easy recurrence, which cannot be cured and bring serious physical injury and psychological burden to patients. The main reason is that the pathological mechanism is not clear. Therefore, this project is expected to reveal the immune microenvironment-related genes and their functions in keloid progression, and provide effective targets for the treatment of keloid. Firstly, 8 kinds of immune infiltrating cells and 19 potential characteristic genes were identified by immune infiltration analysis, ssGSEA, LASSO regression (glmnet algorithm and lars algorithm) and WGCNA, indicating that keloid was closely related to the changes of immune microenvironment. Then, 4 pathological biomarkers of keloid (MAPK1, PTPRC, STAT3 and IL1R1) were identified by differentially analysis, univariate analysis, LASSO regression (lars algorithm), support vector machine recursive feature elimination (SVM-REF) algorithm, multivariate logical regression analysis and six machine learning algorithms. Based on the 4 feature genes, the risk prediction model and nomogram were constructed. Calibration curve and ROC analysis (AUC = 0.930) showed that the model had reliable clinical value. Subsequently, consistent cluster analysis was used to find that there were 2 immune microenvironment subsets in keloid patients, of which subgroup II was immune subgroup. Multiple independent datasets and RT-qPCR showed that the expression trend of the 4 genes was consistent with the analysis. Cell gain-loss experiment confirmed that 4 genes regulated the proliferation and migration of keloid cells. The above data shows that MAPK1, PTPRC, STAT3 and IL1R1 may be personalized therapeutic targets for keloid patients.

CD5 Gene Signature Identifies Diffuse Large B-Cell Lymphomas Sensitive to Bruton's Tyrosine Kinase Inhibition.

A genetic classifier termed LymphGen accurately identifies diffuse large B-cell lymphoma (DLBCL) subtypes vulnerable to Bruton's tyrosine kinase inhibitors (BTKis), but is challenging to implement in the clinic and fails to capture all DLBCLs that benefit from BTKi-based therapy. Here, we developed a novel CD5 gene expression signature as a biomarker of response to BTKi-based therapy in DLBCL. CD5 immunohistochemistry (IHC) was performed on 404 DLBCLs to identify CD5 IHC+ and CD5 IHC- cases, which were subsequently characterized at the molecular level through mutational and transcriptional analyses. A 60-gene CD5 gene expression signature (CD5sig) was constructed using genes differentially expressed between CD5 IHC+ and CD5 IHC- non-germinal center B-cell-like (non-GCB DLBCL) DLBCLs. This CD5sig was applied to external DLBCL data sets, including pretreatment biopsies from patients enrolled in the PHOENIX study (n = 584) to define the extent to which the CD5sig could identify non-GCB DLBCLs that benefited from the addition of ibrutinib to rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). CD5 expression was observed in 12% of non-GCB DLBCLs. CD5+ DLBCLs displayed transcriptional features of B-cell receptor (BCR) activation and were enriched for BCR-activating mutations known to correlate with BTKi sensitivity. However, most CD5+ DLBCLs lacked canonical BCR-activating mutations or were LymphGen-unclassifiable (LymphGen-Other). The CD5sig recapitulated these findings in multiple independent data sets, indicating its utility in identifying DLBCLs with genetic and nongenetic bases for BCR dependence. Supporting this notion, CD5sig+ DLBCLs derived a selective survival advantage from the addition of ibrutinib to R-CHOP in the PHOENIX study, independent of LymphGen classification. CD5sig is a useful biomarker to identify DLBCLs vulnerable to BTKi-based therapies and complements current biomarker approaches by identifying DLBCLs with genetic and nongenetic bases for BTKi sensitivity.

Transcriptome-level discovery of survival-associated biomarkers and therapy targets in non-small-cell lung cancer.

Survival rate of patients with lung cancer has increased by over 60% in the recent two decades. With longer survival, the identification of genes associated with survival has emerged as an issue of utmost importance to uncover the most promising biomarkers and therapeutic targets. An integrated database was set up by combining multiple independent datasets with clinical data and transcriptome-level gene expression measurements. Univariate and multivariate survival analyses were performed to identify genes with higher expression levels linked to shorter survival. The strongest genes were filtered to include only those with known druggability. The entire database includes 2852 tumour specimens from 17 independent cohorts. Of these, 2227 have overall survival data and 1256 samples have progression-free survival time. The most significant genes associated with survival were MIF, UBC and B2M in lung adenocarcinoma and ANXA2, CSNK2A2 and KRT18 in squamous cell carcinoma. We also aimed to reveal the best druggable targets in non-smokers lung cancer. The three most promising hits in this cohort were MDK, THY1 and PADI2. The established lung cancer cohort was added to the Kaplan-Meier plotter (https://www.kmplot.com) enabling the validation of future gene expression-based biomarkers in both the present and yet unexamined subgroups of patients. In this study, we established a comprehensive database of transcriptome-level data for lung cancer. The database can be utilized to identify and rank the most promising biomarkers and therapeutic targets for different subtypes of lung cancer.

Identifying individualized prognostic signature and unraveling the molecular mechanism of recurrence in early-onset colorectal cancer

BackgroundThe incidence and mortality of early-onset colorectal cancer (EOCRC; < 50 years old) is increasing worldwide, with a high recurrence rate. The inherent heterogeneity of EOCRC makes its treatment challenging. Hence, to further understand the biology and reveal the molecular mechanisms of EOCRC, a recurrence risk signature is needed to guide clinical management.MethodsBased on the relative expression orderings (REOs) of genes in each sample, a prognostic signature was developed and validated utilizing multiple independent datasets. The underlying molecular mechanisms between distinct prognostic groups were explored via integrative analysis of multi-omics data.ResultsThe prognostic signature consisting of 6 gene pairs (6-GPS) could predict the recurrence risk for EOCRC at the individual level. High-risk EOCRC classified by 6-GPS showed a poor prognosis but a good response to adjuvant chemotherapy. Moreover, high-risk EOCRC was characterized by epithelial-mesenchymal transition (EMT) and enriched angiogenesis, and had higher mutation burden, immune cell infiltration, and PD-1/PD-L1 expression. Furthermore, we identified four genes associated with relapse-free survival in EOCRC, including SERPINE1, PECAM1, CDH1, and ANXA1. They were consistently differentially expressed at the transcriptome and proteome levels between high-risk and low-risk EOCRCs. They were also involved in regulating cancer progression and immune microenvironment in EOCRC. Notably, the expression of SERPINE1 and ANXA1 positively correlated with M2-like macrophage infiltration.ConclusionOur results indicate that 6-GPS can robustly predict the recurrence risk of EOCRC, and that SERPINE1, PECAM1, CDH1, and ANXA1 may serve as potential therapeutic targets. This study provides valuable information for the precision treatment of EOCRC.

Further evidence for the bidimensionality of the components model of addiction: a reply to Amendola (2023)

In our original research article entitled “Deconstructing the components model of addiction: an illustration through “addictive” use of social media” (Fournier et al., 2023), we showed that the Bergen Social Media Addiction Scale, a six-item psychometric instrument derived from the components model of addiction to assess social media “addiction”, did not form a unitary, but a bidimensional construct in which some components (i.e., salience, tolerance) were not associated with psychopathological symptoms, thus conflating central and peripheral features of addiction. Subsequently, in a recent commentary, Amendola (2023) sought to determine whether our findings were driven by the use of data aggregated from multiple independent datasets, i.e., a decision we transparently acknowledged as a limitation in our original research article. Following their re-analysis, Amendola (2023) claimed to have demonstrated that a unidimensional model best fitted the data. However, they only reported results for a partial set of models relevant to this investigation. In the present reply, through a transparent assessment and reporting of all unidimensional and bidimensional models relevant to this investigation, we show that the bidimensionality of the Bergen Social Media Addiction Scale is, in fact, tenable, robust, and consistent across multiple independent datasets. In line with the growing evidence demonstrating that many sets of criteria involved in operationalizing behavioral addictions pathologize involvement in appetitive behaviors, these results highlight the necessity to renew the conceptualization and assessment of behavioral addictions.

Natural killer cell-related prognosis signature predicts immune response in colon cancer patients.

Background: Natural killer (NK) cells are crucial components of the innate immune system that fight tumors and viral infections. Patients with colorectal cancer (CRC) have a poor prognosis, and immunotherapeutic tools play a key role in the treatment of CRC. Methods: Public data on CRC patients was collected from the TCGA and the GEO databases. Tissue data of CRC patients were collected from Guangxi Medical University Affiliated Cancer Hospital. An NK-related prognostic model was developed by the least absolute shrinkage and selection operator (LASSO) and Cox regression method. Validation data were collected from different clinical subgroups and an external independent validation cohort to verify the model's accuracy. In addition, multiple external independent immunotherapy datasets were collected to further examine the value of NK-related risk scores (NKRS) in the prediction of immunotherapy response. Potential biological functions of key genes were examined by methods of cell proliferation, apoptosis and Western blotting. Results: A novel prognostic model for CRC patients based on NK-related genes was developed and NKRS was generated. There was a significantly poorer prognosis among the high-NKRS group. Based on immune response prediction, patients with low NKRS may be more suitable for immunotherapy and they are more sensitive to immunotherapy. The proliferation rate of CRC cells was significantly reduced and apoptosis of CRC cells was increased after SLC2A3 was knocked down. SLC2A3 was also found to be associated with the TGF-β signaling pathway. Conclusion: NKRS has potential applications for predicting prognostic status and response to immunotherapy in CRC patients. SLC2A3 has potential as a therapeutic target for CRC.

BEST: a web application for comprehensive biomarker exploration on large-scale data in solid tumors

Data mining from RNA-seq or microarray data has become an essential part of cancer biomarker exploration. Certain existing web servers are valuable and broadly utilized, but the meta-analysis of multiple datasets is absent. Most web servers only contain tumor samples from the TCGA database with only one cohort for each cancer type, which also means that the analysis results mainly derived from a single cohort are thin and unstable. Indeed, consistent performance across multiple independent cohorts is the foundation for an excellent biomarker. Moreover, the deeper exploration of specific biomarkers on underlying mechanisms, tumor microenvironment, and drug indications are missing in existing tools. Thus, we introduce BEST (Biomarker Exploration for Solid Tumors), a web application for comprehensive biomarker exploration on large-scale data in solid tumors. To ensure the comparability of genes between different sequencing technologies and the legibility of clinical traits, we re-annotated transcriptome data and unified the nomenclature of clinical traits. BEST delivers fast and customizable functions, including clinical association, survival analysis, enrichment analysis, cell infiltration, immunomodulator, immunotherapy, candidate agents, and genomic alteration. Together, our web server provides multiple cleaned-up independent datasets and diverse analysis functionalities, helping unleash the value of current data resources. It is freely available at https://rookieutopia.com/.