Deciphering Treg cell roles in esophageal squamous cell carcinoma: a comprehensive prognostic and immunotherapeutic analysis

Advancing lung adenocarcinoma prognosis and immunotherapy prediction with a multi-omics consensus machine learning approach

Lung adenocarcinoma (LUAD) is a tumour characterized by high tumour heterogeneity. Although there are numerous prognostic and immunotherapeutic options available for LUAD, there is a dearth of precise, individualized treatment plans. We integrated mRNA, lncRNA, microRNA, methylation and mutation data from the TCGA database for LUAD. Utilizing ten clustering algorithms, we identified stable multi-omics consensus clusters (MOCs). These data were then amalgamated with ten machine learning approaches to develop a robust model capable of reliably identifying patient prognosis and predicting immunotherapy outcomes. Through ten clustering algorithms, two prognostically relevant MOCs were identified, with MOC2 showing more favourable outcomes. We subsequently constructed a MOCs-associated machine learning model (MOCM) based on eight MOCs-specific hub genes. Patients characterized by a lower MOCM score exhibited better overall survival and responses to immunotherapy. These findings were consistent across multiple datasets, and compared to many previously published LUAD biomarkers, our MOCM score demonstrated superior predictive performance. Notably, the low MOCM group was more inclined towards 'hot' tumours, characterized by higher levels of immune cell infiltration. Intriguingly, a significant positive correlation between GJB3 and the MOCM score (R = 0.77, p < 0.01) was discovered. Further experiments confirmed that GJB3 significantly enhances LUAD proliferation, invasion and migration, indicating its potential as a key target for LUAD treatment. Our developed MOCM score accurately predicts the prognosis of LUAD patients and identifies potential beneficiaries of immunotherapy, offering broad clinical applicability.

Investigating cellular similarities and differences between upper tract urothelial carcinoma and bladder urothelial carcinoma using single-cell sequencing

Background

Upper tract urothelial carcinoma (UTUC) and bladder urothelial carcinoma (BLCA) both originate from uroepithelial tissue, sharing remarkably similar clinical manifestations and therapeutic modalities. However, emerging evidence suggests that identical treatment regimens may lead to less favorable outcomes in UTUC compared to BLCA. Therefore, it is imperative to explore molecular processes of UTUC and identify biological differences between UTUC and BLCA.

Methods

In this study, we performed a comprehensive analysis using single-cell RNA sequencing (scRNA-seq) on three UTUC cases and four normal ureteral tissues. These data were combined with publicly available datasets from previous BLCA studies and RNA sequencing (RNA-seq) data for both cancer types. This pooled analysis allowed us to delineate the transcriptional differences among distinct cell subsets within the microenvironment, thus identifying critical factors contributing to UTUC progression and phenotypic differences between UTUC and BLCA.

Results

scRNA-seq analysis revealed seemingly similar but transcriptionally distinct cellular identities within the UTUC and BLCA ecosystems. Notably, we observed striking differences in acquired immunological landscapes and varied cellular functional phenotypes between these two cancers. In addition, we uncovered the immunomodulatory functions of vein endothelial cells (ECs) in UTUC, and intercellular network analysis demonstrated that fibroblasts play important roles in the microenvironment. Further intersection analysis showed that MARCKS promote UTUC progression, and immunohistochemistry (IHC) staining revealed that the diverse expression patterns of MARCKS in UTUC, BLCA and normal ureter tissues.

Conclusion

This study expands our multidimensional understanding of the similarities and distinctions between UTUC and BLCA. Our findings lay the foundation for further investigations to develop diagnostic and therapeutic targets for UTUC.

Evaluating the predictive value of angiogenesis-related genes for prognosis and immunotherapy response in prostate adenocarcinoma using machine learning and experimental approaches

Background

Angiogenesis, the process of forming new blood vessels from pre-existing ones, plays a crucial role in the development and advancement of cancer. Although blocking angiogenesis has shown success in treating different types of solid tumors, its relevance in prostate adenocarcinoma (PRAD) has not been thoroughly investigated.

Method

This study utilized the WGCNA method to identify angiogenesis-related genes and assessed their diagnostic and prognostic value in patients with PRAD through cluster analysis. A diagnostic model was constructed using multiple machine learning techniques, while a prognostic model was developed employing the LASSO algorithm, underscoring the relevance of angiogenesis-related genes in PRAD. Further analysis identified MAP7D3 as the most significant prognostic gene among angiogenesis-related genes using multivariate Cox regression analysis and various machine learning algorithms. The study also investigated the correlation between MAP7D3 and immune infiltration as well as drug sensitivity in PRAD. Molecular docking analysis was conducted to assess the binding affinity of MAP7D3 to angiogenic drugs. Immunohistochemistry analysis of 60 PRAD tissue samples confirmed the expression and prognostic value of MAP7D3.

Result

Overall, the study identified 10 key angiogenesis-related genes through WGCNA and demonstrated their potential prognostic and immune-related implications in PRAD patients. MAP7D3 is found to be closely associated with the prognosis of PRAD and its response to immunotherapy. Through molecular docking studies, it was revealed that MAP7D3 exhibits a high binding affinity to angiogenic drugs. Furthermore, experimental data confirmed the upregulation of MAP7D3 in PRAD, correlating with a poorer prognosis.

Conclusion

Our study confirmed the important role of angiogenesis-related genes in PRAD and identified a new angiogenesis-related target MAP7D3.

To develop a prognostic model for neoadjuvant immunochemotherapy efficacy in esophageal squamous cell carcinoma by analyzing the immune microenvironment

Objective

The choice of neoadjuvant therapy for esophageal squamous cell carcinoma (ESCC) is controversial. This study aims to provide a basis for clinical treatment selection by establishing a predictive model for the efficacy of neoadjuvant immunochemotherapy (NICT).

Methods

A retrospective analysis of 30 patients was conducted, divided into Response and Non-response groups based on whether they achieved major pathological remission (MPR). Differences in genes and immune microenvironment between the two groups were analyzed through next-generation sequencing (NGS) and multiplex immunofluorescence (mIF). Variables most closely related to therapeutic efficacy were selected through LASSO regression and ROC curves to establish a predictive model. An additional 48 patients were prospectively collected as a validation set to verify the model's effectiveness.

Results

NGS suggested seven differential genes (ATM, ATR, BIVM-ERCC5, MAP3K1, PRG, RBM10, and TSHR) between the two groups (P < 0.05). mIF indicated significant differences in the quantity and location of CD3+, PD-L1+, CD3+PD-L1+, CD4+PD-1+, CD4+LAG-3+, CD8+LAG-3+, LAG-3+ between the two groups before treatment (P < 0.05). Dynamic mIF analysis also indicated that CD3+, CD8+, and CD20+ all increased after treatment in both groups, with a more significant increase in CD8+ and CD20+ in the Response group (P < 0.05), and a more significant decrease in PD-L1+ (P < 0.05). The three variables most closely related to therapeutic efficacy were selected through LASSO regression and ROC curves: Tumor area PD-L1+ (AUC= 0.881), CD3+PD-L1+ (AUC= 0.833), and CD3+ (AUC= 0.826), and a predictive model was established. The model showed high performance in both the training set (AUC= 0.938) and the validation set (AUC= 0.832). Compared to the traditional CPS scoring criteria, the model showed significant improvements in accuracy (83.3% vs 70.8%), sensitivity (0.625 vs 0.312), and specificity (0.937 vs 0.906).

Conclusion

NICT treatment may exert anti-tumor effects by enriching immune cells and activating exhausted T cells. Tumor area CD3+, PD-L1+, and CD3+PD-L1+ are closely related to therapeutic efficacy. The model containing these three variables can accurately predict treatment outcomes, providing a reliable basis for the selection of neoadjuvant treatment plans.

Unveiling the oncogenic role of CLDN11-secreting fibroblasts in gastric cancer peritoneal metastasis through single-cell sequencing and experimental approaches

BACKGROUND

Fibroblasts are necessary to the progression of cancer. However, the role of fibroblasts in peritoneal metastasis (PM) of gastric cancer (GC) remains elusive. In this study, we would explore the role of fibroblasts mediated cell interaction in PM of GC.

METHODS

Single-cell sequencing data from public database GSE183904 was used to explore the specific fibroblast cluster. Fibroblasts were extracted from PM and GC tissues. The expression level of CXCR7 was verified by western blot, immunohistochemistry. The role of CLDN11 was investigate through in vitro and in vivo study. Multiple immunohistochemistry was used to characterize the tumor microenvironment.

RESULTS

CXCR7-positive fibroblasts were significantly enriched in PM of GC. CXCR7 could promote the expression of CLDN11 through activation of the AKT pathway in fibroblasts. Fibroblasts promote the GC proliferation and peritoneal metastasis by secreting CLDN11 in vitro and in vivo. Furthermore, it was revealed that CXCR7-positive fibroblasts were significantly associated with M2-type macrophages infiltration in tissues.

CONCLUSION

CXCR7-positive fibroblasts play an essential role in PM of GC via CLDN11. Therapy targeting CXCR7-positive fibroblasts or CLDN11 may be helpful in the treatment of GC with PM.

Occurrence and Prognosis of Mixed Subtype Adenocarcinoma and Adeno-Squamous Carcinoma in Esophageal Cancer

Purpose: To gain a deeper understanding of the incidence and survival rates of rare esophageal mixed adenoacanthoma (EAM) and esophageal mixed adeno-squamous carcinoma (EASC) to promote a more comprehensive understanding of these two subtypes. Background: EAM and EASC are rare subtypes of esophageal cancer with limited literature available. Extensive research has been conducted on the clinical and pathological characteristics of gastric and colorectal mixed adenoacanthomas, but there is relatively little literature on esophageal mixed adenoacanthomas. Therefore, this study aims to investigate the incidence and survival rates of these two subtypes in depth. Methods: Patients diagnosed with EAM and EASC between 2000 and 2019 were selected from the SEER database for the study. Joinpoint software was used to calculate the incidence rates of esophageal AM and ASC patients, and differences in cancer overall survival (OS) and cancer-specific survival (CSS) based on Kaplan-Meier curves were compared. Multivariate Cox regression analysis was employed to identify independent prognostic factors for OS and CSS, and a prognostic model was established and validated for accuracy. Results: The study found that the incidence of EAM increased until 2014, followed by a decline, while the incidence of EASC decreased until 2017, followed by an increase. Both of these subtypes were more common in male patients and those over the age of 65. For EAM patients, preoperative chemoradiotherapy was associated with better survival rates, while for EASC patients, preoperative radiotherapy combined with adjuvant chemotherapy improved survival. Finally, we constructed nomograms for predicting the overall survival of EAM and EASC patients by incorporating identified risk factors, which demonstrated good sensitivity and specificity. Conclusion: EAM and EASC are rare subtypes of esophageal cancer, and an in-depth exploration of their incidence and survival rates provides valuable data and insights for understanding these rare esophageal cancer subtypes. This information can assist clinical decision-making for healthcare professionals.