Understanding the stepwise progression of esophageal squamous cell carcinoma (ESCC) is crucial for developing customized strategies for early detection and optimal clinical management. Herein, we aimed to unravel the transcriptional and immunologic alterations occurring during malignant transformation and identify clinically significant biomarkers of ESCC. Digital spatial profiling (DSP) was performed on 11 patients with early-stage ESCC (pT1) to explore the transcriptional alterations in epithelial, immune cell, and non-immune cell stromal compartments across regions of distinct histology, including normal tissues, low- and high-grade dysplasia, and cancerous tissues. Furthermore, single-cell spatial transcriptomics was performed using the CosMx Spatial Molecular Imaging (SMI) system on 4 additional patients with pT1 ESCC. Immunohistochemical (IHC) analysis was performed on consecutive histological sections of 20 pT1 ESCCs. Additionally, public bulk and single-cell RNA-sequencing (scRNA-seq) datasets were analyzed, and in vitro and in vivo functional studies were conducted. Spatial transcriptional reprogramming and dynamic cell signaling pathways that determined ESCC progression were delineated. Increased infiltration of macrophages from normal tissues through dysplasia to cancerous tissues occurred. Macrophage subtypes were characterized using the scRNA-seq dataset. Cell–cell communication analysis of scRNA-seq and SMI data indicated that the migration inhibitory factor (MIF)-CD74 axis may exhibit pro-tumor interactions between macrophages and epithelial cells. DSP, SMI, and IHC data demonstrated that DTX3L expression in epithelial cells and BST2 expression in stromal cells increased gradually with ESCC progression. Functional studies demonstrated that DTX3L or BST2 knockdown inhibited ESCC proliferation and migration and decreased M2 polarization of tumor-associated macrophages. Spatial profiling comprehensively characterized the molecular and immunological hallmarks from normal tissue to ESCC, guiding the way to a deeper understanding of the tumorigenesis and progression of this disease and contributing to the prevention of ESCC. Within this exploration, we uncovered biomarkers that exhibit a robust correlation with ESCC progression, offering potential new avenues for insightful therapeutic approaches.

Background: To undertake a bioinformatics analysis to identify abnormally expressed genes [also referred to as differentially expressed genes (DEGs)] and their functions in esophageal carcinoma (ESCA). Methods: DEGs (i.e., GSE100942, GSE17351, GSE26886, and GSE77861) were obtained from a gene expression omnibus database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using online tools from the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction network was then constructed based on the Search Tool for the Retrieval of Interacting Genes website. Cytoscape software was used to identify the top 20 DEGs located in the central region of the network. For the overall survival analysis, a Kaplan–Meier analysis was conducted of the Gene Expression Profiling Interactive Analysis website, and collagen (COL) 1A2 was selected to detect the molecular mechanism of COL1A2-small interfering ribonucleic acid (siRNA) in the following ESCA cell lines: Eca109 and TE-1. Next, the expression of COL1A2-messanger ribonucleic acid was determined using real-time quantitative polymerase chain reaction. The expression of COL1A2 was also verified by Western blot. Cell proliferation was measured by colony-forming and MTT assays, and migration and invasion by the transwell assay. Results: Based on the GEO database and screening out the hub gene, we identified that COL1A2 was abnormally expressed in ESCA. With a series of in vitro experiments, the expression of COL1A2 was defined as higher in Eca109 and TE-1. Conclusions: COL1A2 was highly expressed in ESCA tissue samples. Additionally, the proliferation and metastasis of Eca109 and TE-1 cell lines were significantly attenuated by siRNA-COL1A2-mediated small interference. Notably, the expression level of COL1A2 was obviously related to the Akt and epithelial-mesenchymal transition (EMT) pathways.

Background: Biopsies obtained from primary oesophageal squamous cell carcinoma (ESCC) guide diagnosis and treatment. However, spatial intra‐tumoral heterogeneity (ITH) influences biopsy‐derived information and patient responsiveness to therapy. Here, we aimed to elucidate the spatial ITH of ESCC and matched lymph node metastasis (LN_met).Methods: Primary tumour superficial (PT_sup), deep (PT_deep) and LN_met subregions of patients with locally advanced resectable ESCC were evaluated using whole‐exome sequencing (WES), whole‐transcriptome sequencing and spatially resolved digital spatial profiling (DSP). To validate the findings, immunohistochemistry was conducted and a single‐cell transcriptomic dataset was analysed.Results: WES revealed 15.72%, 5.02% and 32.00% unique mutations in PT_sup, PT_deep and LN_met, respectively. Copy number alterations and phylogenetic trees showed spatial ITH among subregions both within and among patients. Driver mutations had a mixed intra‐tumoral clonal status among subregions. Transcriptome data showed distinct differentially expressed genes among subregions. LN_met exhibited elevated expression of immunomodulatory genes and enriched immune cells, particularly when compared with PT_sup (all P < .05). DSP revealed orthogonal support of bulk transcriptome results, with differences in protein and immune cell abundance between subregions in a spatial context. The integrative analysis of multi‐omics data revealed complex heterogeneity in mRNA/protein levels and immune cell abundance within each subregion.Conclusions: This study comprehensively characterised spatial ITH in ESCC, and the findings highlight the clinical significance of unbiased molecular classification based on multi‐omics data and their potential to improve the understanding and management of ESCC. The current practices for tissue sampling are insufficient for guiding precision medicine for ESCC, and routine profiling of PT_deep and/or LN_met should be systematically performed to obtain a more comprehensive understanding of ESCC and better inform treatment decisions.

Objective: To screen lung adenocarcinoma (LUAC)-specific cell-cycle-related genes (CCRGs) and develop a prognostic signature for patients with LUAC.Methods: The GSE68465, GSE42127, and GSE30219 data sets were downloaded from the GEO database. Single-sample gene set enrichment analysis was used to calculate the cell cycle enrichment of each sample in GSE68465 to identify CCRGs in LUAC. The differential CCRGs compared with LUAC data from The Cancer Genome Atlas were determined. The genetic data from GSE68465 were divided into an internal training group and a test group at a ratio of 1:1, and GSE42127 and GSE30219 were defined as external test groups. In addition, we combined LASSO (least absolute shrinkage and selection operator) and Cox regression analysis with the clinical information of the internal training group to construct a CCRG risk scoring model. Samples were divided into high- and low-risk groups according to the resulting risk values, and internal and external test sets were used to prove the validity of the signature. A nomogram evaluation model was used to predict prognosis. The CPTAC and HPA databases were chosen to verify the protein expression of CCRGs.Results: We identified 10 LUAC-specific CCRGs (PKMYT1, ETF1, ECT2, BUB1B, RECQL4, TFRC, COCH, TUBB2B, PITX1, and CDC6) and constructed a model using the internal training group. Based on this model, LUAC patients were divided into high- and low-risk groups for further validation. Time-dependent receiver operating characteristic and Cox regression analyses suggested that the signature could precisely predict the prognosis of LUAC patients. Results obtained with CPTAC, HPA, and IHC supported significant dysregulation of these CCRGs in LUAC tissues.Conclusion: This prognostic prediction signature based on CCRGs could help to evaluate the prognosis of LUAC patients. The 10 LUAC-specific CCRGs could be used as prognostic markers of LUAC.

Mammalian mitochondrial ribosomal proteins are a group of protein factors encoded by nuclear genes, responsible for the synthesis of proteins in mitochondria. As a member of mitochondrial ribosomal proteins, MRPL42 (mitochondrial ribosomal protein L42) belongs to 28S and 39S subunits. The current literature showed that its role in lung adenocarcinoma (LUAD) was not clear. We found that MRPL42 was highly expressed in early-stage LUAD tissues and cell lines, and remarkably related to the prognosis of patients. Knockdown of MRPL42 could reduce the proliferation and colonization, promote cell cycle arrest in G1/S phase, and weaken the migration and invasion ability of LUAD cells in vitro. Moreover, depletion of MRPL42 also inhibited tumor growth in vivo. Bioinformatics analysis found that YY1 may bind to the promoter region upstream of the MRPL42 gene to promote the transcription of MRPL42, which was verified by the ChIP and Dual luciferase reporter assay. QRT-PCR confirmed that knocking down YY1 could attenuate the expression of MRPL42. In summary, MRPL42 acts as an oncogene in LUAD, and its expression level is regulated by YY1.

To investigate the possible involvement of germline mutations in pectus excavatum (PE). We investigated a four-generation pedigree with PE. Whole-exome sequencing (WES)was performed to identify potential mutations for PE formation. Sanger sequencing was used to validate these mutations. hFOB1.19 cell proliferation was measured with a Celigo imaging cytometry system. There were four PE patients in this four-generation pedigree. In the four patients, we identified a novel heterozygous stop-gain variant in Tubulointerstitial Nephritis Antigen (TINAG) through exome sequencing: c.G2A, p.W2*. This mutation was validated by Sanger sequencing. Knockdown of TINAG inhibited the proliferation of hFOB1.19 cells. Based on these results, we hypothesize that the TINAG c.G2A mutation is a loss-of-functionmutationthat reduces TINAG expression. Increasing TINAG warrants further investigation as a potential novel anabolic mechanism of PE treatment.