Oesophageal squamous cell carcinoma (ESCC) is a leading cause of cancer-related deaths worldwide because existing treatments are often insufficient. Therefore, new, reliable biomarkers must be identified. CTSL overexpression is closely associated with tumour progression and poor prognosis. However, the role and mechanism of CTSL as an oncogene in ESCC remain unclear. Genome-wide association study (GWAS) data were used for Mendelian randomization analysis to investigate the possible relationships between CTSL and ESCC. The correlation between CTSL expression and prognosis was analysed using GEO, TCGA, and GEPIA data. We compared CTSL expression among the cell types using single-cell sequencing. Correlations between CTSL and the tumour microenvironment, immune cell infiltration, tumour mutational load, immunological checkpoints, and treatment sensitivity in patients with ESCC were investigated. Finally, using mouse models and cellular investigations, we assessed the effects of CTSL on the growth, apoptosis, and metastasis of ESCC tumour cells. CTSL was overexpressed in ESCC and correlated with prognosis. We also discovered its close association with cell immunity, especially with tumour-associated macrophages and immune checkpoints in the tumour microenvironment. CTSL may play a key role in ESCC development by affecting M2 macrophage polarisation. CTSL and the M2 macrophage marker genes showed significant positive correlations. Mendelian randomization analysis confirmed a relationship between CTSL and ESCC. Finally, our in vitro and in vivo experiments demonstrated that CTSL promoted the proliferation and migration of ESCC cells, validating our bioinformatic analysis. CTSL emerged as a crucial gene in ESCC that influences patient prognosis and immunity, particularly in association with M2 macrophages. Therefore, targeting or modulating CTSL levels may provide new therapeutic strategies for patients with ESCC.

Background: Esophageal squamous cell carcinoma (ESCC) is an aggressive and fatal malignancy that leads to epithelial cancer. The association between epithelial cell heterogeneity, prognosis, and immune response in this cancer remains uncertain. This study aimed to investigate epithelial cell heterogeneity in ESCC and develop a predictive risk model using the identified cell types. Methods: Single-cell RNA sequencing (scRNA-seq) and differential ESCC gene data were accessed from the Gene Expression Omnibus. Functional enrichment analysis, inferCNV, cell development trajectories, and intercellular communication were analyzed following epithelial cell characterization. Differentially expressed ESCC (n = 773) and epithelial cell marker genes (n = 3407) were intersected to obtain core genes, and epithelial cell-related prognostic genes were identified. LASSO regression analysis was used to construct a prognostic model. The external dataset GSE53624 was used to further validate the stability of the model. Drug sensitivity predictions, and immune cell infiltration were analyzed. Molecular docking clarified the possible therapeutic role of β-sitosterol in ESCC. Finally, wound healing assay, cell colony, and transwell assay were constructed to detect the effects of the core gene PDLIM2 on ESCC cell proliferation, invasion, and migration. Results: Eight cell clusters were identified, and epithelial cells were categorized into tumor and paratumor groups. The tumor group possessed more chromosomal variants than the paratumor group. Epithelial cells were associated with multiple cell types and significantly correlated with the Wnt, transforming growth factor, and epidermal growth factor signaling pathways. From 231 intersected genes, five core genes were screened for use in the risk model: CTSL, LAPTM4B, MYO10, NCF2, and PDLIM2. These genes may contribute to the cancerous transformation of normal esophageal epithelial cells and thereby act as biomarkers and potential therapeutic targets in patients with ESCC. β-Sitosterol furthermore displayed excellent docking potential with these genes. Meanwhile, further experiments demonstrated that the gene PDLIM2 plays a major role in the progression of oesophageal squamous carcinoma. Conclusion: We successfully developed a risk model for the prognosis of ESCC based on epithelial cells that addresses the response of ESCC to immunotherapy and offers novel cancer treatment options.

Xiu Wang, Zhenhu Zhang, Yamin Shi, Wenjuan Zhang, Chongyi Su, and Dong Wang. J. Microbiol. Biotechnol. 2024;34:1164-77. https://doi.org/10.4014/jmb.2310.10052

The significance of inflammation in tumorigenesis and progression has become prominent. This study aimed to construct and validate the molecular subtype and a novel prognostic signature based on inflammatory response-related genes in uveal melanoma (UM). Patients from the TCGA, GSE84976, and GSE22138 UM cohorts were enrolled. According to the consensus cluster analysis, patients were divided into two molecular subtypes, namely IC1 and IC2. Survival curves showed that patients in IC1 had a better prognosis. The IC2 subgroup had higher levels of immune cell infiltration and more enriched immunological pathways. There were statistical differences in the immune-inflammation microenvironment, immune checkpoint genes expression, and drug sensitivity. The prognostic signature constructed based on inflammatory response-related genes exhibited a stable predictive power. Multivariate analysis confirmed that the signature was a prognostic factor independent of clinical characteristics. Functional analyses showed that the high-risk group was associated with immunological response, inflammatory cell activation, and tumor-related signal pathways. The riskscore had a negative relationship with tumor purity and was positively correlated with immune and stromal scores. Furthermore, the prognostic signature could sensitively predict the response to drug treatments. In conclusion, the prognostic signature might aid in stratifying patients at risk premised on the prognosis and immunotherapy sensitivity.

Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer and remains a leading cause of cancer-related mortality worldwide. Althou

Background: Increasing evidence suggests that esophageal cancer (ESCA) may be correlated with gut flora. However, their causal connection remains unclear. This study aimed to evaluate potential causal linkages and gene–gut microbiome associations between the gut microbiota and ESCA using Mendelian randomization (MR).Methods: We analyzed the data using genome-wide association studies. The exposure factor and outcome variable were the gut microbiota and ESCA, respectively. The MR-Egger method, weighted median, inverse-variance weighted method, heterogeneity test, sensitivity analysis, and multiplicity analysis were used for the MR analysis. And it was validated using an external dataset. Further meta-analysis was performed to validate the robustness of this relationship. Finally, we annotated single nucleotide polymorphisms in the gut microbiota that were causally associated with ESCA to explore possible host gene-gut microbiota correlations in patients with ESCA.Results: We identified four species with potential associations with ESCA. Three of these species had a negative causal relationship with ESCA (odds ratio (OR): 0.961; 95% confidence interval (CI): 0.923–0.971; p = 0.047 for Romboutsia; OR: 0.972; 95% CI: 0.921–0.961; p = 0.018 for Lachnospira; OR: 0.948; 95% CI: 0.912–0.970; p = 0.032 for Eubacterium). A positive causal relationship was observed between one bacterial group and ESCA (OR: 1.105; 95% CI: 1.010–1.072; p = 0.018 for Veillonella). External datasets show the same trend. This is further supported by meta-analysis. None of the data showed pleiotropy, and leave-one-out analysis indicated the reliability of these findings. The gut microbiomes of patients with ESCA may correlate with the 19 identified genes.Conclusion: Our data indicate a potential causal link between these four gut bacteria and ESCA and identify a correlation between host genes and gut microbiota in ESCA, offering novel therapeutic options.