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Correlation between oncogene integrator complex subunit 7 and a poor prognosis in lung adenocarcinoma.
BACKGROUND: Lung adenocarcinoma (LUAD) is one of the most common types of cancer worldwide with high incidence and mortality rates. The integrator complex subunit 7 (INTS7) encodes a subunit of the integrator complex that mediates small-nuclear ribonucleic acid (RNA) processing and has been shown to be associated with RNA polymerase II. However, the clinical significance of INTS7 in LUAD is still not clear and needs to be investigated. METHODS: The single-cell sequencing of a publicly available data set was conducted to compare the expression levels and percentages of INTS7 in lung malignant cells at different classifications and stages. Further, 33 cancer types from The Cancer Genome Atlas (TCGA) database were analyzed, protein-protein interaction (PPI) networks were constructed, and functional annotations were undertaken for the INTS7 gene. INTS7 small-interfering RNAs were transfected into LUAD cell lines, and cell biological behaviors, such as migration, invasion, apoptosis and proliferation capacity, were then examined. RESULTS: We found that the expression of INTS7 was significantly more upregulated in the LUAD tissues than the adjacent normal tissues. Increased INTS7 messenger RNA expression was correlated with TNM (tumor node metastasis classification) stage and gender in LUAD patients. Further, the Kaplan-Meier survival analysis indicated that LUAD patients with high INTS7 expression levels had a worse prognosis than those with low INTS7 expression levels. Finally, we found that silencing INTS7 inhibited LUAD cell viability and invasion in vitro. CONCLUSIONS: These results suggest that INTS7 can be used as a potential therapy target and prognostic marker for LUAD. Further, INTS7 may aggravate migration and invasion, induce the proliferation, and attenuate the apoptosis capacity of cells in LUAD.
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Identification of exosome-related genes signature based on bioinformatics and machine learning for prognostic prediction in colorectal cancer.
BACKGROUND: Colorectal cancer (CRC) is a prevalent malignant tumor with increasing incidence and mortality rates worldwide. Exosomes are secretory vesicles generated by the endosomal system within cells. Previous studies have reported that exosome-related genes (ERGs) are associated with the progression of malignancies. This study investigates the role of ERGs in CRC, evaluates their impact on CRC prognosis, and explores inter-individual differences among CRC patients in different risk groups. METHODS: Weighted Gene Co-expression Network Analysis (WGCNA) algorithm was employed to identify ERGs associated with CRC. Subsequently, various bioinformatics approaches, including enrichment analysis, consensus clustering, and survival analysis, were utilized to investigate the role of ERGs in individual CRC patients. Furthermore, exosome-related signature genes were refined using the Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms based on colon cancer samples from The Cancer Genome Atlas (TCGA) database. An ERG-related gene signature was then constructed to calculate the ERG-associated risk score for each patient, which was validated using data from the Gene Expression Omnibus (GEO) database. Based on the risk scores, we assessed the responsiveness of different CRC individuals to immunotherapy and chemotherapy. Finally, single-cell analysis provided deeper insights into the relationship between ERGs and CRC, and a nomogram was established to enhance their clinical utility. RESULTS: This study identified two ERG-related subtypes that exhibited significant differences in prognosis, enriched pathways, clinicopathological features, and immune characteristics. Moreover, CRC individuals with high ERG-related risk scores were associated with poor responsiveness to immunotherapy and increased sensitivity to various chemotherapeutic agents. Single-cell analysis revealed that ERGs were highly expressed in monocytes. The model developed in this study demonstrated strong predictive accuracy for assessing ERG-related risk in CRC patients. CONCLUSIONS: This study identified two hub genes, CUL4A and UCHL1, highlighting the diagnostic and prognostic significance of ERGs in CRC and offering new insights into CRC treatment. Additionally, the ERG signature plays a crucial role in predicting individualized prognosis and facilitating the development of novel therapeutic strategies for CRC patients. Nevertheless, further studies are necessary to bring statistically derived risk models into clinically applicable assays.
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Sex differences orchestrated by androgens at single-cell resolution.
Sex differences in mammalian complex traits are prevalent and are intimately associated with androgens1-7. However, a molecular and cellular profile of sex differences and their modulation by androgens is still lacking. Here we constructed a high-dimensional single-cell transcriptomic atlas comprising over 2.3 million cells from 17 tissues in Mus musculus and explored the effects of sex and androgens on the molecular programs and cellular populations. In particular, we found that sex-biased immune gene expression and immune cell populations, such as group 2 innate lymphoid cells, were modulated by androgens. Integration with the UK Biobank dataset revealed potential cellular targets and risk gene enrichment in antigen presentation for sex-biased diseases. This study lays the groundwork for understanding the sex differences orchestrated by androgens and provides important evidence for targeting the androgen pathway as a broad therapeutic strategy for sex-biased diseases.
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A spatially-resolved transcriptional atlas of the murine dorsal pons at single-cell resolution.
The "dorsal pons", or "dorsal pontine tegmentum" (dPnTg), is part of the brainstem. It is a complex, densely packed region whose nuclei are involved in regulating many vital functions. Notable among them are the parabrachial nucleus, the Kölliker Fuse, the Barrington nucleus, the locus coeruleus, and the dorsal, laterodorsal, and ventral tegmental nuclei. In this study, we applied single-nucleus RNA-seq (snRNA-seq) to resolve neuronal subtypes based on their unique transcriptional profiles and then used multiplexed error robust fluorescence in situ hybridization (MERFISH) to map them spatially. We sampled ~1 million cells across the dPnTg and defined the spatial distribution of over 120 neuronal subtypes. Our analysis identified an unpredicted high transcriptional diversity in this region and pinpointed the unique marker genes of many neuronal subtypes. We also demonstrated that many neuronal subtypes are transcriptionally similar between humans and mice, enhancing this study's translational value. Finally, we developed a freely accessible, GPU and CPU-powered dashboard ( http://harvard.heavy.ai:6273/ ) that combines interactive visual analytics and hardware-accelerated SQL into a data science framework to allow the scientific community to query and gain insights into the data.
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Single-cell multi-omics analysis of human testicular germ cell tumor reveals its molecular features and microenvironment.
Seminoma is the most common malignant solid tumor in 14 to 44 year-old men. However, its molecular features and tumor microenvironment (TME) is largely unexplored. Here, we perform a series of studies via genomics profiling (single cell multi-omics and spatial transcriptomics) and functional examination using seminoma samples and a seminoma cell line. We identify key gene expression programs share between seminoma and primordial germ cells, and further characterize the functions of TFAP2C in promoting tumor invasion and migration. We also identify 15 immune cell subtypes in TME, and find that subtypes with exhaustion features were located closer to the tumor region through combined spatial transcriptome analysis. Furthermore, we identify key pathways and genes that may facilitate seminoma disseminating beyond the seminiferous tubules. These findings advance our knowledge of seminoma tumorigenesis and produce a multi-omics atlas of in situ human seminoma microenvironment, which could help discover potential therapy targets for seminoma.
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A spatial transcriptomic atlas of acute neonatal lung injury across development and disease severity.
A molecular understanding of lung organogenesis requires delineation of the timing and regulation of the cellular transitions that ultimately form and support a surface capable of gas exchange. Although the advent of single-cell transcriptomics has allowed for the discovery and identification of transcriptionally distinct cell populations present during lung development, the spatiotemporal dynamics of these transcriptional shifts remain undefined. With imaging-based spatial transcriptomics, we analyzed the gene expression patterns in 17 human infant lungs at varying stages of development and injury, creating a spatial transcriptomic atlas of approximately 1.2 million cells. We applied computational clustering approaches to identify shared molecular patterns among this cohort, informing how tissue architecture and molecular spatial relationships are coordinated during development and disrupted in disease. Recognizing that all preterm birth represents an injury to the developing lung, we created a simplified classification scheme that relies upon the routinely collected objective measures of gestational age and lifespan. Within this framework, we have identified cell type patterns across gestational age and life span variables that would likely be overlooked when using the conventional "disease versus control" binary comparison. Together, these data represent an open resource for the lung research community, supporting discovery-based inquiry and identification of targetable molecular mechanisms in both normal and arrested human lung development.NEW & NOTEWORTHY Mapping the spatial and temporal transcriptional relationships during lung development is fundamental to understanding regeneration and chronic lung disease; however, the classification of samples as control or disease is especially challenging in the setting of preterm birth (itself a lung injury). Here, we report the largest neonatal lung transcriptomic atlas to date and an analysis framework based only on gestational age and lifespan, providing a new resource for hypothesis generation to the lung community.
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Multi-omics nominates VDAC2 as a candidate protective locus in sepsis-associated cholesterol dysregulation.
Sepsis, a life-threatening condition, involves dysregulated cholesterol metabolism critical for immune regulation and cellular processes. This study employed multi-omics and machine learning to explore cholesterol metabolism in sepsis, aiming to identify novel therapeutic targets. Transcriptome and single-cell RNA sequencing data for sepsis were retrieved from the Gene Expression Omnibus (GEO) database. The limma package and WGCNA co-expression network were used to screen genes, hybridized with cholesterol metabolism genes (CMGs) to identify hub genes. Machine learning algorithms screened pivotal genes to construct diagnostic model, validating performance via multi-cohort Receiver Operating Characteristic (ROC) curve. Non-negative matrix factorization (NMF) based molecular typing using CMGs, and integration of 101 machine learning algorithms built prognostic models. Single-cell analysis characterized expression patterns of pivotal genes and key subsets. Causal effects and phenotypic associations of target genes were evaluated using Summary data-based Mendelian Randomization (SMR) and PheWAS. Integrated transcriptomic analysis identified three key genes (VDAC1, VDAC2, and LDLRAP1) associated with dysregulated cholesterol metabolism in sepsis. Machine learning-based diagnostic models exhibited high predictive accuracy. NMF clustering revealed two molecular subtypes, with Cluster 1 characterized by immunosuppression and metabolic reprogramming, linked to poorer prognosis. A machine learning model integrating 101 algorithms predicted 28-day mortality. The single-cell transcriptome atlas identified CD14+CD163+ monocytes as the hub cell population in the immune microenvironment of sepsis, and the active cholesterol metabolic pathway might constitute the core for regulating the immune response. Elevated VDAC2 expression was significantly correlated with reduced sepsis risk, as determined by SMR analysis. This study underscored cholesterol metabolism's critical role in sepsis pathogenesis. Multi-omics nominates VDAC2 as a candidate protective locus in sepsis-associated cholesterol dysregulation.
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ACSS2 in pan-cancer context: multi-omics insights into metabolic reprogramming and immunotherapy response.
ACSS2, a key member of the acyl-CoA synthetase short-chain family, plays a critical role in tumor metabolic reprogramming by regulating fatty acid synthesis, histone acetylation, and lactyl-CoA production. However, its immunomodulatory functions and prognostic significance across cancers remain poorly characterized. Using multiple databases such as The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and cBioPortal, we comprehensively analyzed ACSS2's pan-cancer associations with clinical survival, genomic profiles, tumor stemness, functional enrichment, single-cell heterogeneity, immune infiltration, immune checkpoints, immunotherapy responsiveness, and drug sensitivity. Our analyses revealed pronounced ACSS2 dysregulation across tumors, with elevated expression correlated with poor prognosis in multiple cancers. Genomic profiling identified copy number amplifications as the predominant ACSS2 alteration, with missense and truncating mutations constituting key functional impact variants. ACSS2 expression was negatively correlated with DNA methylation levels. Notably, ACSS2 expression showed significant associations with tumor genomic features, stemness indices, immune cell infiltration, immune regulators. These findings underscore its potential as an immune checkpoint-predictive biomarker. Single-cell resolution analysis further delineated cancer type-specific correlations between ACSS2 and immune cell spatial distribution. Crucially, ACSS2 expression demonstrated predictive value for anti-PD-1/PD-L1 therapy responsiveness, highlighting its clinical relevance in immunotherapy stratification. This study elucidates the pivotal role of ACSS2 in carcinogenesis and tumor progression through metabolism-immune interaction within the tumor microenvironment (TME). Our findings not only provide a theoretical foundation for establishing a prognostic evaluation system based on metabolic features but also highlight the potential value of ACSS2 as a pan-cancer prognostic biomarker and combination therapy target.
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Clinical and immunological implications of lymphocyte-activation gene 3 expression in metastatic colorectal cancer.
BACKGROUND: Lymphocyte-activation gene 3 (LAG3), an inhibitory immune checkpoint receptor, has emerged as a potential therapeutic target in various cancer, including colorectal cancer (CRC). However, its prognostic role and immunologic context in metastatic CRC (mCRC) remain unclear. This study investigated the prognostic impact of LAG3 expression and its biological role within the mCRC immune microenvironment. METHODS: LAG3 expression was evaluated by immunohistochemistry in primary and/or metastatic tumors from a retrospective mCRC cohort. Associations between LAG3 expression, clinicopathological features, and overall survival were analyzed and validated using data from The Cancer Genome Atlas (TCGA) COADREAD cohort. Transcriptomic data from TCGA and single-cell RNA sequencing data from 23 Korean CRC samples were used to explore pathway enrichment and cell-cell interactions. RESULTS: A total of 144 mCRC patients were included. Primary tumors showed higher LAG3 expression than metastatic tumors, with right-sided and liver metastases exhibiting the highest level. Both univariate and multivariate analyses revealed that LAG3 expression was an independent prognostic biomarker of mCRC, validated in the TCGA COADREAD cohort. Transcriptomic analysis revealed significant correlations between LAG3 expression and angiogenesis, epithelial-mesenchymal transition, and TGF-β signaling pathways, which were linked to immunosuppressive microenvironment and poor prognosis. Cell-cell communication analysis showed CD8⁺ T cells interact with various cell subpopulations, with Galectin-9-TIM-3 signaling uniquely present in LAG3⁺CD8⁺ T cells. CONCLUSIONS: LAG3 expression is an independent prognostic biomarker of mCRC. The distinct Galectin-9-TIM-3 signaling in LAG3⁺CD8⁺ T cells may contribute to limited therapeutic efficacy of LAG3 blockade, suggesting potential combinational immunotherapy strategies.
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Predicting survival in bladder cancer with a novel apoptotic gene-related prognostic model.
BACKGROUND: Apoptosis and apoptotic genes play a critical role in the carcinogenesis and progression of bladder cancer. However, there is no prognostic model established by apoptotic genes. METHODS: Messenger RNA (mRNA), Expression data, and related clinical data were obtained from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. After extracting the apoptosis-related genes, the survival-related apoptosis genes were screened by univariate Cox regression analysis in the TCGA cohort. Following the Least Absolute Shrinkage and Selection Operator (LASSO) regression method, these genes were modeled by multivariate Cox analysis. The predictive abilities of the Apoptosis-Related Gene Model (ARGM) for overall survival (OS) rate, disease-specific survival (DSS) measures, and progression-free survival (PFS) were verified by the Kaplan-Meier(K-M)survival analysis and time-dependent Receiver Operating Characteristic (ROC) curve. Functional enrichment analyses were performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG). CIBERSORT and Single-Sample Gene Set Enrichment Analysis (ssGSEA) were used to calculate the type of immune cell infiltration and immune functions. The model's predictive ability for immunotherapy were evaluated using Tumor Immune Dysfunction and Exclusion (TIDE) and the Imvigor210 study.The single-cell sequencing was used to display the expression level of the ARGM.Finally,qRT-PCR was executed to validate the expression level of ARGM. RESULTS: Several apoptosis genes were identified through the model, including ANXA1, CASP6, CD2, F2, PDGFRB, SATB1, and TSPO. The prognostic value of the model for OS, DSS, and PFS were verified using the TCGA and GEO cohort. The model can predict patient response to immunotherapy treatment as established through the model's score which was linked to different types of immune cell infiltration and identified significant differences in the signal pathways between high-risk and low-risk groups. Nomogram variables, prompted from ARGM and clinical parameters, also generate a high predictive value for patient survival. CONCLUSION: Ourestablished apoptosis-related gene model (ARGM) has a substantial predictive value for prognosis and immunotherapy of bladder cancer. It may help with clinical consultation, clinical stratification, and treatment selection. The immune infiltration status and signal pathway of different risk groups also provide direction for further research.