Identification Of Signatures Research Articles

Abstract PR014: Combinatorial genetic screens to map synthetic lethal interactions and identify new cancer drug targets in KRAS mutant cancers

Abstract The success of precision oncology depends on our ability to translate accumulating genomic data into actionable treatment options in a personalized manner. The first step requires the identification of a specific genomic signature, then matching this signature with the most effective therapy. Unfortunately, more often than not, we do not have a specific cancer drug that is effective for the mutation found in a specific tumor. We desperately need to identify new targets so that we can develop new drugs to deliver on the promise of precision cancer medicine. In addition, we need to develop a rational way to combine drugs that does not depend on trial and error. Combinatorial genetic screening CRISPR-Cas9 now allows us to identify synthetic lethal interactions in which simultaneous perturbation of two genes leads to cell death. However, multiplex gene editing to reveal these synergies between genes is a major challenge. For model systems like yeast, high-throughput methods and technologies have made it possible to create genetic networks with around 23 million double mutants (6000 genes x 6000 genes) and has resulted in the most detailed genetic interaction network to date consisting of ∼900,000 genetic interactions. This landscape of genetic interaction network of yeast has taken the scientific research community 15 years. In human cells, there are ∼20,000 genes and 400 million combinations, making this very complex network impossible to screen and test all these 400M combinations. To systematically interrogate such genetic interactions, we designed a dual CRISPR-Cas9 perturbation library targeting the top 1000 genes upregulated in KRAS mutant cancers (lung, pancreas and colon cancers). We performed these combinatorial double knockout screens on 100K (100x1000) unique gene pairs and identified 27 pairs whose co-disruption results in a loss of cellular fitness. We next validated those top synthetic lethal pairs of genes by performing secondary screens using CRISPR-Cas12a on more KRAS mutant and KRAS WT cell lines. Overall, our findings will provide insight into potential combinational targets in KRAS mutant tumors and will highlight the synthetic lethality effects that occurs among the novel targets, and other proliferation, metastasis, immune and metabolism modules. Citation Format: Rand Arafeh, Laura Chang, Lydia Sawyer, Helen Wang, James McFarland, Joshua Dempster, Peter DeWeirdt, John Doench, William C. Hahn. Combinatorial genetic screens to map synthetic lethal interactions and identify new cancer drug targets in KRAS mutant cancers [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr PR014.

Extracting interpretable signatures of whole-brain dynamics through systematic comparison.

The brain's complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.

Identification of tumor-antigen signatures and immune subtypes for messenger RNA vaccine selection in advanced clear cell renal cell carcinoma

BackgroundAdvanced clear cell renal cell carcinoma still lacks reliable diagnostic and prognostic biomarkers. Recently, tumor vaccines targeting specific molecules have been proposed as a promising treatment in mitigating tumor progression, which was rekindled under the background of the COVID-19 pandemic. However, the application of messenger RNA vaccine against advanced clear cell renal cell carcinoma antigens remains stagnant, and no subgroup of patients deemed suitable for vaccination has been extensively studied or validated. Our study aims to explore novel advanced clear cell renal cell carcinoma antigen signatures to select suitable patients for vaccination. MethodsGene expression profiles of advanced clear cell renal cell carcinoma samples and their corresponding clinical data were retrieved from The Cancer Genome Atlas. The least absolute shrinkage and selection operator model was applied to develop signatures to stratify patients with advanced clear cell renal cell carcinoma. Receiver operating characteristic analysis was used to compare the prognostic accuracy of each factor. Tumor Immune Estimation Resource was used to visualize the relationship between the proportion of antigen-presenting cells and the expression of filtered genes. The "CIBERSORT" and "WGCNA" R Packages were employed to ascertain disparities in immune infiltration levels between advanced clear cell renal cell carcinoma subgroups. The Search Tools for the Retrieval of Interacting Genes database and Cytoscape were used to construct the protein-protein interaction network. CCK-8 and colony formation assays were included in the invitro experiment. ResultsIn total, 244 potential tumor antigens were identified. Using the least absolute shrinkage and selection operator Cox regression, 21 tumor antigens were selected for developing a risk evaluation signature. The risk score signature can be a useful tool to predict the outcome of advanced clear cell renal cell carcinoma patients. According to the differential clinical, molecular, and immune-related genes, we divided advanced clear cell renal cell carcinoma patients into the immune “cold” subtype and immune “hot” subtype. By developing a logistic score, the immune subtype signature can better distinguish a patient more likely to be immune “cold” subtype or immune “hot” subtype.Interestingly, patients with high risk scores had a higher proportion of immune “hot” subtype than those with a low risk score. Furthermore, the prognostic value was significantly improved when combining risk score and immune subtype. Distinct immune landscapes and signal pathways were observed between different tumor subtypes. Finally, novel tumor antigens related to oxidative stress were identified. ConclusionThe tumor-antigens–based risk score and immune subtype signatures identified potentially effective neo-antigens for advanced clear cell renal cell carcinoma messenger RNA vaccine development, and patients with low risk scores and immune “cold” subtype tumors are more prone to benefit from messenger RNA vaccination. Furthermore, our study highlights the significant role of oxidative stress in determining the efficacy of the messenger RNA vaccine.

Identification of a metabolism-linked genomic signature for prognosis and immunotherapeutic efficiency in metastatic skin cutaneous melanoma

Identification of a metabolism-linked genomic signature for prognosis and immunotherapeutic efficiency in metastatic skin cutaneous melanoma

Genome-wide comparative analysis reveals selection signatures for reproduction traits in prolific Suffolk sheep.

The identification of genome-wide selection signatures can reveal the potential genetic mechanisms involved in the generation of new breeds through natural or artificial selection. In this study, we screened the genome-wide selection signatures of prolific Suffolk sheep, a new strain of multiparous mutton sheep, to identify candidate genes for reproduction traits and unravel the germplasm characteristics and population genetic evolution of this new strain of Suffolk sheep. Whole-genome resequencing was performed at an effective sequencing depth of 20× for genomic diversity and population structure analysis. Additionally, selection signatures were investigated in prolific Suffolk sheep, Suffolk sheep, and Hu sheep using fixation index (F ST) and heterozygosity H) analysis. A total of 5,236.338Gb of high-quality genomic data and 28,767,952 SNPs were obtained for prolific Suffolk sheep. Moreover, 99 selection signals spanning candidate genes were identified. Twenty-three genes were significantly associated with KEGG pathway and Gene Ontology terms related to reproduction, growth, immunity, and metabolism. Through selective signal analysis, genes such as ARHGEF4, CATIP, and CCDC115 were found to be significantly correlated with reproductive traits in prolific Suffolk sheep and were highly associated with the mTOR signaling pathway, the melanogenic pathway, and the Hippo signaling pathways, among others. These results contribute to the understanding of the evolution of artificial selection in prolific Suffolk sheep and provide candidate reproduction-related genes that may be beneficial for the establishment of new sheep breeds.

Identification of an inflammatory response-related gene prognostic signature and immune microenvironment for cervical cancer.

Background: Cervical cancer (CC) is the fourth most common cancer among women worldwide. As part of the brisk cross-talk between the host and the tumor, prognosis can be affected through inflammatory responses or the tumor microenvironment. However, further exploration of the inflammatory response-related genes that have prognostic value, microenvironment infiltration, and chemotherapeutic therapies in CC is needed. Methods: The clinical data and mRNA expression profiles of CC patients were downloaded from a public database for this study. In the TCGA cohort, a multigene prognostic signature was constructed by least absolute shrinkage and selection operator (LASSO) and Cox analyses. CC patients from the GEO cohort were used for validation. K‒M analysis was used to compare overall survival (OS) between the high- and low-risk groups. Univariate and multivariate Cox analyses were applied to determine the independent predictors of OS. The immune cell infiltration and immune-related functional score were calculated by single-sample gene set enrichment analysis (GSEA). Immunohistochemistry was utilized to validate the protein expression of prognostic genes in CC tissues. Results: A genetic signature model associated with the inflammatory response was built by LASSO Cox regression analysis. Patients in the high-risk group had a significantly lower OS rate. The predictive ability of the prognostic genes was evaluated by means of receiver operating characteristic (ROC) curve analysis. The risk score was confirmed to be an independent predictor of OS by univariate and multivariate Cox analyses. The immune status differed between the high-risk and low-risk groups, and the cancer-related pathways were enriched in the high-risk group according to functional analysis. The risk score was significantly related to tumor stage and immune infiltration type. The expression levels of five prognostic genes (LCK, GCH1, TNFRSF9, ITGA5, and SLC7A1) were positively related to sensitivity to antitumor drugs. Additionally, the expression of prognostic genes was significantly different between CC tissues and myoma patient cervix (non-tumorous) tissues in the separate sample cohort. Conclusion: A model consisting of 5 inflammation-related genes can be used to predict prognosis and influence immune status in CC patients. Furthermore, the inhibition or enhancement of these genes may become a novel alternative therapy.

Identification of potential shared gene signatures between gastric cancer and type 2 diabetes: a data-driven analysis.

Gastric cancer (GC) and type 2 diabetes (T2D) contribute to each other, but the interaction mechanisms remain undiscovered. The goal of this research was to explore shared genes as well as crosstalk mechanisms between GC and T2D. The Gene Expression Omnibus (GEO) database served as the source of the GC and T2D datasets. The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were utilized to identify representative genes. In addition, overlapping genes between the representative genes of the two diseases were used for functional enrichment analysis and protein-protein interaction (PPI) network. Next, hub genes were filtered through two machine learning algorithms. Finally, external validation was undertaken with data from the Cancer Genome Atlas (TCGA) database. A total of 292 and 541 DEGs were obtained from the GC (GSE29272) and T2D (GSE164416) datasets, respectively. In addition, 2,704 and 336 module genes were identified in GC and T2D. Following their intersection, 104 crosstalk genes were identified. Enrichment analysis indicated that "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were mutual pathways. Through the PPI network, 10 genes were identified as candidate hub genes. Machine learning further selected BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 as hub genes. "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were revealed as possible crosstalk mechanisms. BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 were identified as shared genes and potential therapeutic targets for people suffering from GC and T2D.

Identification and evaluation of a six-lncRNA prognostic signature for multiple myeloma

PurposeMultiple myeloma (MM) is the second most common hematologic malignancy, and there is no cure for this disease. This study aimed to explore the prognostic value of long noncoding RNAs (lncRNAs) in MM and to reveal related immune and chemotherapy resistance mechanisms.MethodsIn this study, lncRNA profiles from the Multiple Myeloma Research Foundation (MMRF) and Gene Expression Omnibus (GEO) databases were analyzed to identify lncRNAs linked to MM patient survival. A risk assessment model stratified patients into high- and low-risk groups, and survival was evaluated. Additionally, a triple-ceRNA (lncRNA–miRNA–mRNA) network was constructed, and functional analysis was performed. The research also involved immune function analysis and chemotherapy drug sensitivity assessment using oncoPredict and the GDSC dataset.ResultsWe identified 422 lncRNAs significantly associated with overall survival in MM patients and ultimately focused on the 6 with the highest prognostic value. These lncRNAs were used to develop a risk score formula that stratified patients into high- and low-risk groups. Kaplan–Meier analysis revealed shorter survival in high-risk patients. We integrated this lncRNA signature with clinical parameters to construct a nomogram for predicting MM prognosis. Additionally, a triple-ceRNA network was constructed to reveal potential miRNA targets, coding genes related to these lncRNAs and significantly enriched pathways. Immune checkpoint gene expression and immune cell composition were also analyzed in relation to the lncRNA risk score. Finally, using the oncoPredict tool, we observed that high-risk patients exhibited decreased sensitivity to key MM chemotherapeutics, suggesting that lncRNA profiles are linked to chemotherapy resistance.

Identification of Immune-Related Gene Signature in Schizophrenia.

Schizophrenia (SCZ) is a type of psychiatric disorder characterized by multiple symptoms. Our aim is to decipher the relevant mechanisms of immune-related gene signatures in SCZ. The SCZ dataset and its associated immunoregulatory genes were retrieved using Gene Expression Omnibus (GEO) and single-sample gene set enrichment analysis (ssGSEA). Co-expressed gene modules were determined through weighted gene correlation network analysis (WGCNA). To elucidate the functional characteristics of these clusters, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used. Additionally, gene set enrichment analysis (GSEA) and Gene Set Variation Analysis (GSVA) were conducted to identify enriched pathways for the immune subgroups. A protein-protein interaction (PPI) network analysis was performed to identify core genes relevant to SCZ. A significantly higher immune score was observed in SCZ compared to control samples. Seven distinct gene modules were identified, with genes highlighted in green selected for further analysis. Using the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) method, degrees of immune cell adhesion and accumulation related to 22 different immune cell types were calculated. Significantly enriched bioprocesses concerning the immunoregulatory genes with differential expressions included interferon-beta, IgG binding, and response to interferon-gamma, according to GO and KEGG analyses. Eleven hub genes related to immune infiltration emerged as key players among the three top-ranked GO terms. This study underscores the involvement of immunoregulatory reactions in SCZ development. Eleven immune-related genes (IFITM1 (interferon induced transmembrane protein 1), GBP1 (guanylate binding protein 1), BST2 (bone marrow stromal cell antigen 2), IFITM3 (interferon induced transmembrane protein 3), GBP2 (guanylate binding protein 2), CD44 (CD44 molecule), FCER1G (Fc epsilon receptor Ig), HLA-DRA (major histocompatibility complex, class II, DR alpha), FCGR2A (Fc gamma receptor IIa), IFI16 (interferon gamma inducible protein 16), and FCGR3B (Fc gamma receptor IIIb)) were identified as hub genes, representing potential biomarkers and therapeutic targets associated with the immune response in SCZ patients.

Identification of tumor-marker gangliosides for early cancer detection: A multi-platform approach.

e17551 Background: Tumor-marker gangliosides (TMGs) have enormous potential for early-stage cancer detection as diagnostic biomarkers despite being relatively understudied in this context. AOA Dx is focused on the analytical development of three separate platforms for the quantitation of TMGs in human serum. The development of these platforms for TMG quantitation may allow for the identification of diagnostic signatures for early-stage cancer detection. Methods: First, we are developing a pipeline of antibody-based assays to target TMGs. To aid in development of multiple immunoassays, we collected specificity and affinity data on commercial and proprietary antibodies targeting TMGs using glycoarrays. We are also currently using two immune-based platforms to target TMGs. We have developed an enzyme-linked immunosorbent assay (ELISA) and we have implemented high-performance thin-layer chromatography (HPTLC) to detect individual and total gangliosides. Additionally, we are applying both targeted and untargeted mass spectrometry to human serum from confirmed cancer diagnoses. Results: The specificity and affinity data from the glycoarrays confirm that our TMG antibodies have negligible cross-reactivity and are suitable for diagnostic platform development. Additionally, our developed ELISA allows for the quantification of TMGs in diverse and complex matrices including human serum and plasma, cell lines, and exosomes. HPTLC also detects individual and total gangliosides in each of these matrices, which allows for high-throughput analysis for TMG quantitation. Finally, the application of both targeted and untargeted mass spectrometry has identified multiple TMGs characterized by unique lipid tails. Notably, MS analysis shows that the serum of melanoma and ovarian cancer patients exhibited significantly increased levels of several ganglioside species compared to age-matched healthy serum, further highlighting the potential of TMGs as a promising avenue for cancer diagnosis. Further experiments will focus on refining all platforms. We will also expand the demographic representation of our human serum sample cohort to confirm the initial findings from our mass spectrometry experiments and continue to use this platform to identify additional TMGs of interest. Conclusions: AOA Dx is advancing the development of a multi-platform approach for the detection of TMGs. Our objective is the identification of a diagnostic disease signature for early-stage cancer detection. The validation of this type of diagnostic panel would allow for expedited diagnosis and treatment of cancers currently diagnosed at late stages, ultimately reducing healthcare costs and increasing survival rates. Future research will aim to validate these biomarkers in independent prospective studies.

Identification of a predictive phosphoproteomic signature of response to atezolizumab and bevacizumab (AB) in patients with advanced hepatocellular carcinoma (aHCC).

Identification of a predictive phosphoproteomic signature of response to atezolizumab and bevacizumab (AB) in patients with advanced hepatocellular carcinoma (aHCC).

Cancer mutational signatures identification in clinical assays using neural embedding-based representations

While mutational signatures provide a plethora of prognostic and therapeutic insights, their application in clinical-setting, targeted gene panels is extremely limited. We develop a mutational representation model (which learns and embeds specific mutation signature connections) that enables prediction of dominant signatures with only a few mutations. We predict the dominant signatures across more than 60,000 tumors with gene panels, delineating their landscape across different cancers. Dominant signature predictions in gene panels are of clinical importance. These included UV, tobacco, and apolipoprotein B mRNA editing enzyme, catalytic polypeptide (APOBEC) signatures that are associated with better survival, independently from mutational burden. Further analyses reveal gene and mutation associations with signatures, such as SBS5 with TP53 and APOBEC with FGFR3S249C. In a clinical use case, APOBEC signature is a robust and specific predictor for resistance to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs). Our model provides an easy-to-use way to detect signatures in clinical setting assays with many possible clinical implications for an unprecedented number of cancer patients.

Identification of a prognostic signature based on five ferroptosis-related genes for diffuse large B-cell lymphoma.

Therapies for diffuse large B-cell lymphoma (DLBCL) are limited due to the diverse gene expression profiles and complicated immune microenvironments, making it an aggressive lymphoma. Beyond this, researches have shown that ferroptosis contributes to tumorigenesis, progression, and metastasis. We thus are interested to dissect the connection between ferroptosis and disease status of DLBCL. We aim at generating a valuable prognosis gene signature for predicting the status of patients of DLBCL, with focus on ferroptosis-related genes (FRGs). To examine the connection between ferroptosis-related genes (FRGs) and clinical outcomes in DLBCL patients based on public datasets. An expression profile dataset for DLBCL was downloaded from GSE32918 (https://www.ncbi.nlm.nih.gov/geo/ query/acc.cgi?acc=gse32918), and a ferroptosis-related gene cluster was obtained from the FerrDb database (http://www. zhounan.org/ferrdb/). A prognostic signature was developed from this gene cluster by applying a least absolute shrinkage and selection operator (LASSO) Cox regression analysis to GSE32918, followed by external validation. Its effectiveness as a biomarker and the prognostic value was determined by a receiver operator characteristic curve mono factor analysis. Finally, functional enrichment was evaluated by the package Cluster Profiler of R. Five ferroptosis-related genes (FRGs) (GOP1, GPX2, SLC7A5, ATF4, and CXCL2) associated with DLBCL were obtained by a multivariate analysis. The prognostic power of these five FRGs was verified by TCGA (https://xenabrowser.net/datapages/?dataset=TCGA.DLBC.sampleMap%2FHiSeqV2_PANCAN&host=https%3A%2F%2Ftcga.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A44) and GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse 32918) datasets, with ROC analyses. KEGG and GO analyses revealed that upregulated genes in the high-risk group based on the gene signature were enriched in receptor interactions and other cancer-related pathways, including pathways related to abnormal metabolism and cell differentiation. The newly developed signature involving GOP1, GPX2, SLC7A5, ATF4, and CXCL2 has the potential to serve as a prognostic biomarker. Furthermore, our results provide additional support for the contribution of ferroptosis to DLBCL.

Identification and validation of a prognostic signature based on six immune-related genes for colorectal cancer

BackgroundColorectal cancer (CRC) is a prevalent malignancy with high mortality and morbidity rates. Although the significant efficacy of immunotherapy is well established, it is only beneficial for a limited number of individuals with CRC.MethodsDifferentially expressed immune-related genes (DE-IRGs) were retrieved from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and ImmPort databases. A prognostic signature comprising DE-IRGs was developed using univariate, LASSO, and multivariate Cox regression analyses. A nomogram integrating the independent prognostic factors was also developed. CIBERSORT was used to assess immune cell infiltration (ICI). Furthermore, wound-healing, colony formation, migration, and invasion assays were performed to study the involvement of ACTG1 in CRC.ResultsA signature including six DE-IRGs was developed. The overall survival (OS) rate was accurately estimated for TCGA and GSE38832 cohorts. The risk score (RS) of the signature was an independent factor for OS. Moreover, a nomogram encompassing age, RS, and pathological T stage accurately predicted the long-term OS probability of individuals with CRC. The high-risk group had an elevated proportion of patients treated with ICIs, including native B cells, relative to the low-risk group. Additionally, ACTG1 expression was upregulated, which supported the proliferation, migration, and invasion abilities of CRC cells.ConclusionsAn immune-related prognostic signature was developed for predicting OS and for determining the immune status of individuals with CRC. The present study provides new insights into accurate immunotherapy for individuals with CRC. Moreover, ACTG1 may serve as a new immune biomarker.

Machine Learning-Based Integrated Multiomics Characterization of Colorectal Cancer Reveals Distinctive Metabolic Signatures.

The metabolic signature identification of colorectal cancer is critical for its early diagnosis and therapeutic approaches that will significantly block cancer progression and improve patient survival. Here, we combined an untargeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry and the machine learning approach to analyze metabolites in 173 pairs of cancer samples and matched normal tissue samples to build robust metabolic signature models for diagnostic purposes. Screening and independent validation of metabolic signatures from colorectal cancers via machine learning methods (Logistic Regression_L1 for feature selection and eXtreme Gradient Boosting for classification) was performed to generate a panel of seven signatures with good diagnostic performance (the accuracy of 87.74%, sensitivity of 85.82%, and specificity of 89.66%). Moreover, seven signatures were evaluated according to their ability to distinguish between cancer and normal tissues, with the metabolic molecule PC (30:0) showing good diagnostic performance. In addition, genes associated with PC (30:0) were identified by multiomics analysis (combining metabolic data with transcriptomic data analysis) and our results showed that PC (30:0) could promote the proliferation of colorectal cancer cell SW480, revealing the correlation between genetic changes and metabolic dysregulation in cancer. Overall, our results reveal potential determinants affecting metabolite dysregulation, paving the way for a mechanistic understanding of altered tissue metabolites in colorectal cancer and design interventions for manipulating the levels of circulating metabolites.

Identification of a Macrophage marker gene signature to evaluate immune infiltration and therapeutic response in hepatocellular carcinoma

BackgroundOnly a minority of hepatocellular carcinoma (HCC) patients can benefit from systemic regimens. Macrophages, which abundantly infiltrate in HCC, could mediate tumour microenvironment remodelling and immune escape, proving to be powerful weapons in combating HCC. Thus, a deeper understanding of macrophages is necessary for improving existing antitumour treatments. MethodsWith a series of bioinformatic approaches, we comprehensively explored the role of macrophage-related genes in human HCCs from multiple single-cell and bulk RNA sequencing datasets. Unsupervised clustering was performed to cluster the macrophage marker genes (MMGs). GSVA and functional enrichment analysis were used to elucidate the functional differences among the MMG-associated clusters. Subsequently, a component analysis algorithm was used to construct a Macrosig score, and the prognosis, biological characteristics, mutation profile, TME cell infiltration status and drug response of patients with different Macrosig scores were further analysed. ResultsWe identified 13 MMGs in 574 HCC samples, based on which three MMG-associated clusters were defined. Overall survival time, clinicopathological features and immune infiltration scores differed among the different clusters. On this basis, 12 hub genes were identified among these clusters; subsequently, a scoring system was constructed to determine the Macrosig score. Importantly, patients with low-Macrosig scores, characterized by increased immune infiltration, increased mutation frequency and increased immune checkpoint expression, including CTLA-4, LAG3, PDCD1 and TIGIT, exhibited enhanced efficacy of immunotherapy when validated in an external database. Moreover, a low-Macrosig score indicates increased sensitivity to AZD.2281, A.443654, ABT.263, ABT.888, AG.014699 and ATRA, while a high Macrosig score indicates increased sensitivity to AZD6482, AKT inhibitor VIII, AS601245, AZ628, AZD.0530 and AZD6244. ConclusionsA novel scoring system was constructed to guide more effective prognostic evaluation and tailoring therapeutic regimens for HCC patients.

Identification of Gut Microbiome Signatures Associated with Indole Pathway in Tryptophan Metabolism in Patients Undergoing Hemodialysis.

Microbiota tryptophan metabolism and the biosynthesis of indole derivatives play an important role in homeostasis and pathogenesis in the human body and can be affected by the gut microbiota. However, studies on the interplay between gut microbiota and tryptophan metabolites in patients undergoing dialysis are lacking. This study aimed to identify the gut microbiota, the indole pathway in tryptophan metabolism, and significant functional differences in ESRD patients with regular hemodialysis. We performed the shotgun metagenome sequencing of stool samples from 85 hemodialysis patients. Using the linear discriminant analysis effect size (LEfSe), we examined the composition of the gut microbiota and metabolic features across varying concentrations of tryptophan and indole metabolites. Higher tryptophan levels promoted tyrosine degradation I and pectin degradation I metabolic modules; lower tryptophan levels were associated with glutamate degradation I, fructose degradation, and valine degradation modules. Higher 3-indoxyl sulfate concentrations were characterized by alanine degradation I, anaerobic fatty acid beta-oxidation, sulfate reduction, and acetyl-CoA to crotonyl-CoA. Contrarily, lower 3-indoxyl sulfate levels were related to propionate production III, arabinoxylan degradation, the Entner-Doudoroff pathway, and glutamate degradation II. The present study provides a better understanding of the interaction between tryptophan, indole metabolites, and the gut microbiota as well as their gut metabolic modules in ESRD patients with regular hemodialysis.

Identification and Verification of a Glycolysis-Related lncRNA Prognostic Signature for Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a primary liver cancer with a high mortality rate. The search for a new biomarker could help the prognosis of HCC patients. We identified the glycolytic gene set associated with HCC and the glycolytic lncRNA based on TCGA and MsigDB databases. According to these lncRNAs, K-means clustering, and regression analysis were performed on the patients. Two groups of HCC patients with different lncRNA expression levels were obtained based on K-means clustering results. The results of difference analysis and enrichment analysis showed that DEmRNA in the two HCC populations with significant survival differences was mainly enriched in transmembrane transporter complex, RNA polymerase II specificity, cAMP signaling pathway, and calcium signaling pathway. In addition, a prognostic model of HCC with 4 DElncRNAs was constructed based on regression analysis. ROC curve analysis showed that the model had good predictive performance. Drug predictionresults showed that the efficacy of JQ1, niraparib, and teniposide was higher in the low-risk group than in the high-risk group. In conclusion, this study preliminarily identified glycolytic-related prognostic features of lncRNAs in HCC and constructed a risk assessment model. The results of this study are expected to guide the prognosis assessment of clinical HCC patients.

Inflammation-based lung adenocarcinoma molecular subtype identification and construction of an inflammation-related signature with bulk and single-cell RNA-seq data.

The role of inflammation is increasingly understood to have a central influence on therapeutic outcomes and prognosis in lung adenocarcinoma (LUAD). However, the detailed molecular divisions involved in inflammatory responses are yet to be fully elucidated. Our study identified two main inflammation-oriented LUAD grades: the inflammation-low (INF-low) and the inflammation-high (INF-high) subtypes. Both presented with unique clinicopathological features, implications for prognosis, and distinctive tumor microenvironment profiles. Broadly, the INF-low grade, marked by its dominant immunosuppressive tumor microenvironment, was accompanied by less favorable prognostic outcomes and a heightened prevalence of oncogenic mutations. In contrast, the INF-high grade exhibited more optimistic clinical trajectories, underscored by its immune-active environment. In addition, our efforts led to the conceptualization and empirical validation of an inflammation-centric predictive model with considerable predictive potency. Our study paves the way for a refined inflammation-centric LUAD classification and fosters a deeper understanding of tumor microenvironment intricacies.

Identification of a methyltransferase-related long noncoding RNA signature as a novel prognosis biomarker for lung adenocarcinoma.

Lung adenocarcinoma (LUAD) accounts for a high proportion of tumor deaths globally, while methyltransferase-related lncRNAs in LUAD were poorly studied. In our study, we focused on two distinct cohorts, TCGA-LUAD and GSE3021, to establish a signature of methyltransferase-related long non-coding RNAs (MeRlncRNAs) in LUAD. We employed univariate Cox and LASSO regression analyses as our main analytical tools. The GSE30219 cohort served as the validation cohort for our findings. Furthermore, to explore the differential pathway enrichments between groups stratified by risk, we utilized Gene Set Enrichment Analysis (GSEA). Additionally, single-sample GSEA (ssGSEA) was conducted to assess the immune infiltration landscape within each sample. Reverse transcription quantitative PCR (RT-qPCR) was also performed to verify the expression of prognostic lncRNAs in both clinically normal and LUAD samples. In LUAD, we identified a set of 32 MeRlncRNAs. We further narrowed our focus to six prognostic lncRNAs to develop gene signatures. The TCGA-LUAD cohort and GSE30219 were utilized to validate the risk score model derived from these signatures. Our analysis showed that the risk score served as an independent prognostic factor, linked to immune-related pathways. Additionally, the analysis of immune infiltration revealed that the immune landscape in high-risk groups was suppressed, which could contribute to poorer prognoses. We also constructed a regulatory network comprising 6 prognostic lncRNAs, 19 miRNAs, and 21 mRNAs. Confirmatory RT-qPCR results aligned with public database findings, verifying the expression of these prognostic lncRNAs in the samples. The prognostic gene signature of LUAD associated with MeRlncRNAs that we provided, may offer us a comprehensive picture of the prognosis prediction for LUAD patients.