B2M, JAK2 and MET in the genetic landscape of immunotolerance in lung cancer

Neutrophil estimation and prognosis analysis based on existing lung squamous cell carcinoma datasets: the development and validation of a prognosis prediction model

Background

Notwithstanding the rapid developments in precision medicine in recent years, lung cancer still has a low survival rate, especially lung squamous cell cancer (LUSC). The tumor microenvironment (TME) plays an important role in the progression of lung cancer, in which high neutrophil levels are correlated with poor prognosis, potentially due to their interactions with tumor cells via pro-inflammatory cytokines and chemokines. However, the precise mechanisms of how neutrophils influence lung cancer remain unclear. This study aims to explore these mechanisms and develop a prognosis predictive model in LUSC, addressing the knowledge gap in neutrophil-related cancer pathogenesis.

Methods

LUSC datasets from the Xena Hub and Gene Expression Omnibus (GEO) databases were used, comprising 473 tumor samples and 195 tumor samples, respectively. Neutrophil contents in these samples were estimated using CIBERSORT, xCell, and microenvironment cell populations (MCP) counter tools. Differentially expressed genes (DEGs) were identified using DEseq2, and a weighted gene co-expression network analysis (WGCNA) was performed to identify neutrophil-related genes. A least absolute shrinkage and selection operator (LASSO) Cox regression model was constructed for prognosis prediction, and the model's accuracy was validated using Kaplan-Meier survival curves and time-dependent receiver operating characteristic (ROC) curves. Additionally, genomic changes, immune correlations, drug sensitivity, and immunotherapy response were analyzed to further validate the model's predictive power.

Results

Neutrophil content was significantly higher in adjacent normal tissue compared to LUSC tissue (P<0.001). High neutrophil content was associated with worse overall survival (OS) (P=0.02), disease-free survival (DFS) (P=0.02), and progression-free survival (PFS) (P=0.03) using different software estimates. Nine gene modules were identified, with blue and yellow modules showing strong correlations with neutrophil prognosis (P<0.001). Eight genes were selected for the prognostic model, which accurately predicted 1-, 3-, and 5-year survival in both the training set [area under the curve (AUC) value =0.60, 0.63, 0.66, respectively] and validation set (AUC value =0.58, 0.58, 0.59, respectively), with significant prognosis differences between high- and low-risk groups (P<0.001). The model's independent prognostic factors included risk group, pathologic M stage, and tumor stage (P<0.05). A further molecular mechanism analysis revealed differences between risk groups were revealed in immune checkpoint and human leukocyte antigen (HLA) gene expression, hallmark pathways, drug sensitivity, and immunotherapy responses.

Conclusions

This study established a risk-score model that effectively predicts the prognosis of LUSC patients and sheds light on the molecular mechanisms involved. The findings enhance the understanding of neutrophil-tumor interactions, offering potential targets for personalized treatments. However, further experimental validation and clinical studies are required to confirm these findings and address study limitations, including reliance on public databases and focus on a specific lung cancer subtype.

Insights Into Long Non-Coding RNA and mRNA Expression in the Jejunum of Lambs Challenged With Escherichia coli F17

It has long been recognized that enterotoxigenic Escherichia coli (ETEC) is the major pathogen responsible for vomiting and diarrhea. E. coli F17, a main subtype of ETEC, is characterized by high morbidity and mortality in young livestock. However, the transcriptomic basis underlying E. coli F17 infection has not been fully understood. In the present study, RNA sequencing was conducted to explore the expression profiles of mRNAs and long non-coding RNAs (lncRNAs) in the jejunum of lambs who were identified as resistant or sensitive to E. coli F17 that was obtained in a challenge experiment. A total of 772 differentially expressed (DE) mRNAs and 190 DE lncRNAs were detected between the E. coli F17—resistance and E. coli F17-sensitive lambs (i.e., TFF2, LOC105606142, OLFM4, LYPD8, REG4, APOA4, TCONS_00223467, and TCONS_00241897). Then, a two-step machine learning approach (RX) combination Random Forest and Extreme Gradient Boosting were performed, which identified 16 mRNAs and 17 lncRNAs as potential biomarkers, within which PPP2R3A and TCONS_00182693 were prioritized as key biomarkers involved in E. coli F17 infection. Furthermore, functional enrichment analysis showed that peroxisome proliferator-activated receptor (PPAR) pathway was significantly enriched in response to E. coli F17 infection. Our finding will help to improve the knowledge of the mechanisms underlying E. coli F17 infection and may provide novel targets for future treatment of E. coli F17 infection.

Combination of Ipilimumab and Nivolumab in Cancers: From Clinical Practice to Ongoing Clinical Trials

Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and programmed cell death protein 1 (PD-1) are inhibitory checkpoints that are commonly seen on activated T cells and have been offered as promising targets for the treatment of cancers. Immune checkpoint inhibitors (ICIs)targeting PD-1, including pembrolizumab and nivolumab, and those targeting its ligand PD-L1, including avelumab, atezolizumab, and durvalumab, and two drugs targeting CTLA-4, including ipilimumab and tremelimumab have been approved for the treatment of several cancers and many others are under investigating in advanced trial phases. ICIs increased antitumor T cells’ responses and showed a key role in reducing the acquired immune system tolerance which is overexpressed by cancer and tumor microenvironment. However, 50% of patients could not benefit from ICIs monotherapy. To overcome this, a combination of ipilimumab and nivolumab is frequently investigated as an approach to improve oncological outcomes. Despite promising results for the combination of ipilimumab and nivolumab, safety concerns slowed down the development of such strategies. Herein, we review data concerning the clinical activity and the adverse events of ipilimumab and nivolumab combination therapy, assessing ongoing clinical trials to identify clinical outlines that may support combination therapy as an effective treatment. To the best of our knowledge, this paper is one of the first studies to evaluate the efficacy and safety of ipilimumab and nivolumab combination therapy in several cancers.