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Case Report: Transformation From Cold to Hot Tumor in a Case of NSCLC Neoadjuvant Immunochemotherapy Pseudoprogression.
A 56-year-old male was diagnosed with right lung upper lobe squamous cancer with right hilar and mediastinum lymph node metastasis. After four cycles of neoadjuvant immunochemotherapy, reexamination by computed tomography showed progressive disease of the primary lesion. Then, the patient underwent a right lung upper lobectomy, and hilar and mediastinum lymph node dissection. Surgical pathology showed a partial response to immunochemotherapy. Single-cell RNA sequencing was used to characterize the infiltrating immune cell atlas after neoadjuvant immunochemotherapy; the most common infiltrating immune cell types were cytotoxic CD8+ T cells, monocyte-derived dendritic cells, and macrophages. Imaging mass cytometry revealed a transformation from cold to hot tumor after neoadjuvant immunochemotherapy. In this case study, we are the first to report a case of neoadjuvant immunochemotherapy pseudoprogression, proved by surgical pathology, single-cell RNA sequencing, and imaging mass cytometry. Both single-cell RNA sequencing and imaging mass cytometry revealed an activated immune microenvironment after neoadjuvant immunochemotherapy.
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Comprehensive analysis and experimental verification of the mechanism of anoikis related genes in pancreatic cancer.
BACKGROUND: Pancreatic cancer (PC), characterized by its aggressive nature and low patient survival rate, remains a challenging malignancy. Anoikis, a process inhibiting the spread of metastatic cancer cells, is closely linked to cancer progression and metastasis through anoikis-related genes. Nonetheless, the precise mechanism of action of these genes in PC remains unclear. METHODS: Study data were acquired from the Cancer Genome Atlas (TCGA) database, with validation data accessed at the Gene Expression Omnibus (GEO) database. Differential expression analysis and univariate Cox analysis were performed to determine prognostically relevant differentially expressed genes (DEGs) associated with anoikis. Unsupervised cluster analysis was then employed to categorize cancer samples. Subsequently, a least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted on the identified DEGs to establish a clinical prognostic gene signature. Using risk scores derived from this signature, patients with cancer were stratified into high-risk and low-risk groups, with further assessment conducted via survival analysis, immune infiltration analysis, and mutation analysis. External validation data were employed to confirm the findings, and Western blot and immunohistochemistry were utilized to validate risk genes for the clinical prognostic gene signature. RESULTS: A total of 20 prognostic-related DEGs associated with anoikis were obtained. The TCGA dataset revealed two distinct subgroups: cluster 1 and cluster 2. Utilizing the 20 DEGs, a clinical prognostic gene signature comprising two risk genes (CDKN3 and LAMA3) was constructed. Patients with pancreatic adenocarcinoma (PAAD) were classified into high-risk and low-risk groups per their risk scores, with the latter exhibiting a superior survival rate. Statistically significant variation was noted across immune infiltration and mutation levels between the two groups. Validation cohort results were consistent with the initial findings. Additionally, experimental verification confirmed the high expression of CDKN3 and LAMA3 in tumor samples. CONCLUSION: Our study addresses the gap in understanding the involvement of genes linked to anoikis in PAAD. The clinical prognostic gene signature developed herein accurately stratifies patients with PAAD, contributing to the advancement of precision medicine for these patients.
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Prognostic value and immune infiltration of novel markers TNRC6C/AMPD1 in pancreatic cancer microenvironment.
BACKGROUND: Pancreatic cancer (PC) is a highly lethal malignancy with limited treatment options. Identifying novel prognostic biomarkers and therapeutic targets is crucial for improving patient outcomes. METHODS: A comprehensive bioinformatics analysis was conducted on the Gene Expression Omnibus (GEO, GSE79668, GSE183795) and The Cancer Genome Atlas- Pancreatic Adenocarcinoma (TCGA-PAAD) datasets to identify prognostic biomarkers. The prognostic value of these biomarkers was validated through survival analysis and a Cox proportional hazards model (Cox model). A clinical phenotypic prediction model was constructed using AMPD1 and TNRC6C expression levels, with logistic regression models being built for their combination. The nomogram was constructed to visually represent the model's predictive power. Additionally, immune infiltration and single-cell analyses were performed to explore the underlying mechanisms. Functional experiments were conducted to validate the effects of these biomarkers on PC cell behavior. RESULTS: Adenosine Monophosphate Deaminase 1 (AMPD1) and Trinucleotide Repeat Containing Adaptor 6C (TNRC6C) were identified as key prognostic biomarkers for PC. High expression of these genes was associated with improved patient survival. Furthermore, AMPD1 and TNRC6C were found to be positively correlated with various immune cells, suggesting their potential role in modulating the tumor immune microenvironment. Functional experiments confirmed that these genes inhibited cancer cell proliferation, migration, invasion, and promoted apoptosis. The prognostic model based on AMPD1 and TNRC6C expression showed significant predictive accuracy, suggesting its potential clinical utility. CONCLUSION: This study highlights the prognostic significance of AMPD1 and TNRC6C in PC. These findings provide potential new therapeutic targets for PC and warrant further investigation. The developed clinical prediction model further supports their potential utility as biomarkers for patient stratification and prognosis.
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The role of MRO as an M2 macrophage-associated gene in non-small cell lung cancer: insights into immune infiltration, prognostic significance, and therapeutic implications.
BACKGROUND: Non-small cell lung cancer (NSCLC), including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), is influenced by tumor-immune interactions. M2 macrophages play a significant role in the tumor microenvironment. This study explores the role of MRO, an M2 macrophage-associated gene, in NSCLC, focusing on immune infiltration, prognostic significance, and therapeutic potential. METHODS: NSCLC samples from The Cancer Genome Atlas (TCGA) were analyzed using the CIBERSORT algorithm to quantify immune cell compositions. Differential gene expression and correlation studies examined MRO's association with M2 macrophages. Univariate Cox regression and Kaplan-Meier analyses assessed prognostic significance. Single-cell RNA sequencing data from TISCH2 evaluated MRO expression in different cell types. The ESTIMATE algorithm analyzed correlations between MRO expression and immune scores, while TIDE and Submap analyses predicted immunotherapy responses. RESULTS: MRO was highly expressed in NSCLC, particularly in LUAD and LUSC, and associated with M2 macrophages. MRO correlated with key immune pathways, including TNFα signaling via NFκB, inflammatory response, and IL6 JAK STAT3 signaling. High MRO expression correlated with poorer overall survival (OS) and disease-specific survival (DSS). Single-cell analysis confirmed MRO expression in macrophages. The ESTIMATE algorithm showed positive correlations between MRO expression and immune scores. TIDE and Submap analyses suggested low MRO expression in LUSC patients might predict better immunotherapy responses. CONCLUSIONS: MRO is a critical M2 macrophage-associated gene in NSCLC, influencing immune infiltration and prognosis. It may serve as a biomarker for prognostication and a target for therapeutic intervention in NSCLC.
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Integrating innovative multiomics and machine learning strategies for prognostic biomarker discovery in hepatocellular carcinoma: guiding personalized treatment strategies with single-cell analysis.
BACKGROUND: Precise treatments for hepatocellular carcinoma (HCC), a highly invasive and heterogeneous cancer, are lacking. We aimed to analyze multiomics data with machine learning and single-cell analysis to develop biomarkers for predicting HCC prognosis and guiding clinical practice. METHODS: Obtaining data from The Cancer Genome Atlas dataset, we analyzed messenger RNA, long noncoding RNA, and microRNA expression profiles, genomic mutations, and DNA methylation data to construct cancer subtypes (CSs) and identify differentially expressed genes (DEGs). Using AddModuleScore, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA), we identified cancer subtype1-related genes (CS1RGs) at the single-cell and bulk transcriptome levels. To construct a consensus CS1-related signature (CS1RS), we developed a novel machine learning framework with 13 combinations and evaluated it in the external set. Multiomics analysis provided comprehensive insight into the prognostic signature. We then created a CS1RS-integrated nomogram for prognostic prediction, and we assessed immunotherapy responses and identified drugs for targeted personalized medicine. RESULTS: We identified two CSs of HCC through multiomics consensus clustering. CS1 showed unfavorable survival outcomes. Comprehensive analyses revealed distinct molecular features that differentiated these subtypes, including gene expression patterns, chromatin remodeling, and immune infiltration. Single-cell RNA sequencing identified 2,028 DEGs between the high- and low-CS1 groups. We constructed a CS1RS using 13 machine learning approaches that demonstrated robust predictive accuracy across the training and validation sets. When we combined the CS1RS with clinical characteristics, we observed that they together offered a reliable tool for personalized prognosis prediction in HCC. Additionally, we observed significant correlations between the CS1RS and tumor microenvironment, immune infiltration, and drug sensitivity, providing insights into HCC progression and potential therapeutic targets. CONCLUSIONS: Developing a new risk stratification and prediction model (CS1RS) can help in the prognosis assessment, early risk warning of HCC patients, and can also be used to formulate individualized treatment plans. Our innovation is poised to significantly impact clinical intelligence and management approaches.
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Prognostic Potential of Secreted Modular Calcium-Binding Protein 1 in Low-Grade Glioma.
Background: Secreted modular calcium-binding protein 1 (SMOC1) belongs to a family of matricellular proteins; it was involved in embryo development, endothelial cell proliferation, angiogenesis, integrin-matrix interactions, cell adhesion, and regulation of glucose metabolism. Previous studies showed that the expression of SMOC1 was increased in some tumors. However, the prognostic value and the biological function of SMOC1 in tumor remain unclear. Methods: In this study, we explored the expression profile and prognostic value of SMOC1 in pan-cancers, especially glioma, via multiple databases, including Oncomine, Gene Expression Profiling Interactive 2, PrognoScan, Kaplan-Meier plotter, and the Chinese Glioma Genome Atlas database. Furthermore, LinkedOmics was used to identify the genes coexpressed with SMOC1 and to perform Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology analysis in low-grade glioma (LGG). Also, the Cancer Single-Cell State Atlas database was used to evaluate the correlation between SMOC1 expression and functional state activities in glioma cells. In addition, the Tumor Immune Estimation Resource and TISIDB databases were used to evaluate the correlations between SMOC1 expression and tumor-infiltrating immune cells in the tumor microenvironment. Results: Compared with normal brain tissues, the expression of SMOC1 was increased in LGG tissues. The higher expression of SMOC1 was significantly correlated with better survival of LGG patients. Additionally, functional analyses showed that the SMOC1 coexpressed genes were inhibited in processes such as response to type I interferon and interferon-gamma, lymphocyte-mediated immunity, leukocyte migration, adaptive immune response, neutrophil-mediated immunity, T cell activation, and pathways including EMC-receptor interaction, Th17 cell differentiation, and leukocyte trans-endothelial migration in LGG. Moreover, the expression of SMOC1 was correlated with stemness, hypoxia, EMT, and metastasis of glioma cells. Additionally, the expression of SMOC1 expression was negatively correlated with levels of infiltrating B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells, and gene markers of most immune cells in LGG. Conclusion: Our results suggest that SMOC1 could be a potential biomarker to determine prognosis and might play a specific role in the tumor microenvironment of glioma, thereby influencing the development and progression of glioma. These findings provide some new insights for further investigation.
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[Omics and cell controlling technology for drug discovery].
Knowledge Palette, Inc. is a start-up company that aims to overcome incurable diseases by applying the world's most accurate single-cell level and bulk level transcriptome technology to obtain large-scale data on the state of cells treated with various types of drugs and media, and using this information to highly control cells for improving human health. We are working on new phenotypic drug discovery and higher quality cells for regenerative medicine using big data. As one of its core technologies, the company is utilizing a single-cell-level whole gene expression analysis technology, Quartz-Seq2, which was originally developed in RIKEN. This technology received first place in accuracy of genes detection as well as marker identification, and was ranked No. 1 in overall score in the benchmarking in the international Human Cell Atlas project. By applying this technology to the bulk level analysis of ultra-multiple samples, it has enabled drug screening, analysis of human clinical specimens, and evaluation of numerous culture environments in a high-throughput way. This paper presents an omics-driven drug discovery and cell regulation approach that is combined with large-scale data and artificial intelligence technology.
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Distinct immunophenotypic profiles and neutrophil heterogeneity in colorectal cancer.
Colorectal cancer (CRC) exhibits significant molecular and immunological heterogeneity. Neutrophil infiltration patterns play a crucial yet poorly understood role in tumor progression and patient outcomes. This study presents a comprehensive single-cell atlas of the CRC tumor microenvironment (TME), integrating transcriptomic data from 388,511 cells across 98 samples from 63 patients. Employing advanced computational methods, we stratified patients based on their immune cell infiltration profiles, revealing distinct immunophenotypes with potential therapeutic implications. Our analysis focused on tissue-resident neutrophils (TRNs) and uncovered previously uncharacterized subpopulations with diverse functional states. Trajectory inference analysis revealed a dynamic differentiation path from normal-associated neutrophils to tumor-associated neutrophils, highlighting the remarkable plasticity of these cells within the tumor environment. By integrating single-cell data with bulk transcriptomic and clinical information, we identified specific neutrophil-derived gene signatures associated with poor prognosis in CRC, suggesting their potential as novel prognostic biomarkers. This study not only provides unprecedented insights into neutrophil heterogeneity in CRC but also identifies potential targets for immunomodulatory therapies. Our findings lay the groundwork for developing more nuanced, personalized immunotherapeutic strategies for CRC, potentially improving treatment efficacy for patients who currently show a limited response to existing immunotherapies.
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A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.
INTRODUCTION: Colorectal Cancer (CRC) remains a leading cause of cancer-related mortality, characterized by substantial interpatient heterogeneity and limited effective prognostic biomarkers. METHODS: To address this gap, we constructed a robust prognostic model by integrating over 100 machine learning algorithms-such as LASSO, CoxBoost, and StepCox-based on transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts. RESULTS: Plexin-A3 (PLXNA3) emerged as a top risk gene within the ensemble model, which achieved strong predictive performance, surpassing conventional clinical indicators. Multi-omics validation confirmed PLXNA3's prognostic relevance. Spatial and single-cell transcriptomics demonstrated their enrichment in malignant epithelial regions and negative association with immune cell infiltration, particularly CD8+ T cells and plasma cells. Transcription factor (TF) and microRNA (miRNA) correlation analyses revealed potential upstream regulators of PLXNA3 linked to tumor stemness and immune suppression. Functional enrichment indicated its association with cell cycle, DNA damage repair, and interferon signaling pathways. Immunohistochemistry (IHC) confirmed PLXNA3 overexpression in tumor tissues and its correlation with nodal metastasis. Moreover, drug sensitivity profiling and Connectivity Map (CMap) analysis identified potential compounds, including imatinib, MS-275 and fasudil, capable of reversing PLXNA3-driven transcriptional programs. DISCUSSION: This study identifies PLXNA3 as a novel immune-related biomarker in colorectal cancer and elucidates its multifaceted role in tumor progression, immune evasion, and therapeutic resistance. These findings provide a foundation for incorporating PLXNA3 into precision oncology frameworks for gastrointestinal malignancies.
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Multi-omics analysis of kinesin family member 2C in human tumors: novel prognostic biomarker and tumor microenvironment regulator.
Kinesin family member 2C (KIF2C) is the best-characterized member of the kinesin-13 family and is involved in accurately fine-tuned dynamics of mitotic spindles. As KIF2C is involved in both spindle formation and regulation of DNA double-strand breaks, precise regulation of KIF2C is essential to prevent malignant transformation associated with gains and losses of DNA content. In the present study, we initially reviewed The Cancer Genome Atlas database and observed that KIF2C is abundantly expressed in most tumor types. We then analyzed the gene alteration profile, protein expression, prognosis, and immune reactivities of KIF2C in more than 10,000 samples from several well-established databases. In addition, we conducted a gene enrichment set analysis to investigate the potential mechanisms underlying the role of KIF2C in tumorigenesis. Multi-omics analysis of KIF2C demonstrated significant statistical correlations between KIF2C expression and clinical prognosis, oncogenic signature gene sets, myeloid-derived suppressor cell infiltration, ImmunoScore, immune checkpoints, microsatellite instability, and tumor mutational burden across multiple tumors. Single-cell data showed that KIF2C is abundantly expressed in malignant cells. The experimental validation demonstrated that KIF2C is highly expressed in gastric cancer cell lines, gastric adenocarcinoma, and hepatocelluar carcinoma. The findings of this study provide important insight for understanding the role and mechanisms of KIF2C in tumorigenesis and immunotherapy in a variety of cancers.