Single cell and bulk RNA sequencing identifies tumor microenvironment subtypes and chemoresistance-related IGF1+ cancer-associated fibroblast in gastric cancer.

BACKGROUND: The tumor microenvironment (TME) significantly influences prognosis and drug resistance in various tumors, yet its heterogeneity and the mechanisms affecting therapeutic response remain unclear in gastric cancer (GC). METHODS: The heterogenous TME were explored with single-cell RNA-sequencing (scRNA-seq) data of 50 primary GC samples. We then identified four GC TME subtypes with nonnegative matrix factorization (NMF) and constructed a pearson nearest-centroid classifier based on subtype-specific upregulated genes. Genomic features and clinical significance of four subtypes were comprehensively evaluated. We reclustered fibroblasts to identify cancer-associated fibroblast (CAF) subtype associated with poor clinical outcomes. RT-qPCR and double immunofluorescence staining were applied to validate the findings. Cellchat analysis elucidated potential molecular mechanisms of the CAF subtype in GC disease progression and chemotherapy resistance. FINDINGS: The GC TME exhibited high heterogeneity, influencing chemo-sensitivity. Four TME-based subtypes predicting response to immunotherapy and chemotherapy were identified and validated in 1406 GC patients. Among which, ISG1 subtype displayed higher fibroblasts infiltration and heightened oncogenic pathways, and inferior response to chemotherapy with unfavorable prognosis. Microsatellite instability-high (MSI-H) GCs within four TME subtypes showed immunological heterogeneity. We then reported an IGF1-overexpressing CAF was associated with chemo-resistance and GC recurrence. Cell communication analysis revealed IGF1+ CAF may induce drug-resistant phenotypes in tumor cells through IGF1-α6ß4 integrin ligand-receptor binding and activation of EMT biological process. INTERPRETATION: We identified four TME-based subtypes with different clinical outcomes and IGF1+ CAFs contributing to poor clinical outcomes in GC, which might provide guidance for individualized treatment and facilitate the development of novel therapeutic targets.

Genomic and transcriptional characterization of early esophageal squamous cell carcinoma.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a highly heterogeneous cancer that lacks comprehensive understanding and effective treatment. Although multi-omics study has revealed features and underlying drivers of advanced ESCC, research on molecular characteristics of the early stage ESCC is quite limited. MATERIALS AND METHODS: We presented characteristics of genomics and transcriptomics in 10 matched pairs of tumor and normal tissues of early ESCC patients in the China region. RESULTS: We identified the specific patterns of cancer gene mutations and copy number variations. We also found a dramatic change in the transcriptome, with more than 4,000 genes upregulated in cancer. Among them, more than one-third of HOX family genes were specifically and highly expressed in early ESCC samples of China and validated by RT-qPCR. Gene regulation network analysis indicated that alteration of Hox family genes promoted the proliferation and metabolism remodeling of early ESCC. CONCLUSIONS: We characterized the genomic and transcriptomic landscape of 10 paired normal adjacent and early ESCC tissues in the China region, and provided a new perspective to understand the development of ESCC and insight into potential prevention and diagnostic targets for the management of early ESCC in China.

GepLiver: an integrative liver expression atlas spanning developmental stages and liver disease phases.

Chronic liver diseases usually developed through stepwise pathological transitions under the persistent risk factors. The molecular changes during liver transitions are pivotal to improve liver diagnostics and therapeutics yet still remain elusive. Cumulative large-scale liver transcriptomic studies have been revealing molecular landscape of various liver conditions at bulk and single-cell resolution, however, neither single experiment nor databases enabled thorough investigations of transcriptomic dynamics along the progression of liver diseases. Here we establish GepLiver, a longitudinal and multidimensional liver expression atlas integrating expression profiles of 2469 human bulk tissues, 492 mouse samples, 409,775 single cells from 347 human samples and 27 liver cell lines spanning 16 liver phenotypes with uniformed processing and annotating methods. Using GepLiver, we have demonstrated dynamic changes of gene expression, cell abundance and crosstalk harboring meaningful biological associations. GepLiver can be applied to explore the evolving expression patterns and transcriptomic features for genes and cell types respectively among liver phenotypes, assisting the investigation of liver transcriptomic dynamics and informing biomarkers and targets for liver diseases.

Identification of a Four-Gene-Based SERM Signature for Prognostic and Drug Sensitivity Prediction in Gastric Cancer.

BACKGROUND: Gastric cancer (GC) is a highly molecular heterogeneous tumor with poor prognosis. Epithelial-mesenchymal transition (EMT) process and cancer stem cells (CSCs) are reported to share common signaling pathways and cause poor prognosis in GC. Considering about the close relationship between these two processes, we aimed to establish a gene signature based on both processes to achieve better prognostic prediction in GC. METHODS: The gene signature was constructed by univariate Cox and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses by using The Cancer Genome Atlas (TCGA) GC cohort. We performed enrichment analyses to explore the potential mechanisms of the gene signature. Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) curves were implemented to assess its prognostic value in TCGA cohort. The prognostic value of gene signature on overall survival (OS), disease-free survival (DFS), and drug sensitivity was validated in different cohorts. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) validation of the prognostic value of gene signature for OS and DFS prediction was performed in the Fudan cohort. RESULTS: A prognostic signature including SERPINE1, EDIL3, RGS4, and MATN3 (SERM signature) was constructed to predict OS, DFS, and drug sensitivity in GC. Enrichment analyses illustrated that the gene signature has tight connection with the CSC and EMT processes in GC. Patients were divided into two groups based on the risk score obtained from the formula. The Kaplan-Meier analyses indicated high-risk group yielded significantly poor prognosis compared with low-risk group. Pearson's correlation analysis indicated that the risk score was positively correlated with carboplatin and 5-fluorouracil IC50 of GC cell lines. Multivariate Cox regression analyses showed that the gene signature was an independent prognostic factor for predicting GC patients' OS, DFS, and susceptibility to adjuvant chemotherapy. CONCLUSIONS: Our SERM prognostic signature is of great value for OS, DFS, and drug sensitivity prediction in GC, which may give guidance to the development of targeted therapy for CSC- and EMT-related gene in the future.