TNF-Alpha Pathway Alternation Predicts Survival of Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer

From chaos to order: optimizing fecal microbiota transplantation for enhanced immune checkpoint inhibitors efficacy

The integration of fecal microbiota transplantation (FMT) with immune checkpoint inhibitors (ICIs) presents a promising approach for enhancing cancer treatment efficacy and overcoming therapeutic resistance. This review critically examines the controversial effects of FMT on ICIs outcomes and elucidates the underlying mechanisms. We investigate how FMT modulates gut microbiota composition, microbial metabolite profiles, and the tumor microenvironment, thereby influencing ICIs effectiveness. Key factors influencing FMT efficacy, including donor selection criteria, recipient characteristics, and administration protocols, are comprehensively discussed. The review delineates strategies for optimizing FMT formulations and systematically monitoring post-transplant microbiome dynamics. Through a comprehensive synthesis of evidence from clinical trials and preclinical studies, we elucidate the potential benefits and challenges of combining FMT with ICIs across diverse cancer types. While some studies report improved outcomes, others indicate no benefit or potential adverse effects, emphasizing the complexity of host-microbiome interactions in cancer immunotherapy. We outline critical research directions, encompassing the need for large-scale, multi-center randomized controlled trials, in-depth microbial ecology studies, and the integration of multi-omics approaches with artificial intelligence. Regulatory and ethical challenges are critically addressed, underscoring the imperative for standardized protocols and rigorous long-term safety assessments. This comprehensive review seeks to guide future research endeavors and clinical applications of FMT-ICIs combination therapy, with the potential to improve cancer patient outcomes while ensuring both safety and efficacy. As this rapidly evolving field advances, maintaining a judicious balance between openness to innovation and cautious scrutiny is crucial for realizing the full potential of microbiome modulation in cancer immunotherapy.

Identification and analysis of the molecular targets of statins in colorectal cancer

Colorectal cancer (CRC) is the third most common cancer in the world. According to several types of research, statins may impact the development and treatment of CRC. This work aimed to use bioinformatics to discover the relationship between statin targets and differentially expressed genes (DEGs) in CRC patients and determine the possible molecular effect of statins on CRC suppression. We used CRC datasets from the GEO database to select CRC-related DEGs. DGIdb and STITCH databases were used to identify gene targets of subtypes of statin. Further, we identified the statin target of CRC DEGs hub genes by using a Venn diagram of CRC DEGs and statin targets. Funrich and enrichr databases were carried out for the KEGG pathway and gene ontology (GO) enrichment analysis, respectively. GSE74604 and GSE10950 were used to identify CRC DEGs. After analyzing datasets,1370 genes were identified as CRC DEGs, and 345 targets were found for statins. We found that 35 genes are CRC DEGs statin targets. We found that statin targets in CRC were enriched in the receptor and metallopeptidase activity for molecular function, cytoplasm and plasma membrane for cellular component, signal transduction, and cell communication for biological process genes were substantially enriched based on FunRich enrichment. Analysis of the KEGG pathways revealed that the overexpressed DEGs were enriched in the IL-17, PPAR, and Toll-like receptor signaling pathways. Finally, CCNB1, DNMT1, AURKB, RAC1, PPARGC1A, CDKN1A, CAV1, IL1B, and HSPD1 were identified as hub CRC DEGs statin targets. The genetic and molecular aspects of our findings reveal that statins might have a therapeutic effect on CRC.

PathwayTMB: A pathway-based tumor mutational burden analysis method for predicting the clinical outcome of cancer immunotherapy

Immunotherapy has become one of the most promising therapy methods for cancer, but only a small number of patients are responsive to it, indicating that more effective biomarkers are urgently needed. This study developed a pathway analysis method, named PathwayTMB, to identify genomic mutation pathways that serve as potential biomarkers for predicting the clinical outcome of immunotherapy. PathwayTMB first calculates the patient-specific pathway-based tumor mutational burden (PTMB) to reflect the cumulative extent of mutations for each pathway. It then screens mutated survival benefit-related pathways to construct an immune-related prognostic signature based on PTMB (IPSP). In a melanoma training set, IPSP-high patients presented a longer overall survival and a higher response rate than IPSP-low patients. Moreover, the IPSP showed a superior predictive effect compared with TMB. In addition, the prognostic and predictive value of the IPSP was consistently validated in two independent validation sets. Finally, in a multi-cancer dataset, PathwayTMB also exhibited good performance. Our results indicate that PathwayTMB could identify the mutation pathways for predicting immunotherapeutic survival, and their combination may serve as a potential predictive biomarker for immune checkpoint inhibitor therapy.

Computational Characterization of Undifferentially Expressed Genes with Altered Transcription Regulation in Lung Cancer

A transcriptome profiles the expression levels of genes in cells and has accumulated a huge amount of public data. Most of the existing biomarker-related studies investigated the differential expression of individual transcriptomic features under the assumption of inter-feature independence. Many transcriptomic features without differential expression were ignored from the biomarker lists. This study proposed a computational analysis protocol (mqTrans) to analyze transcriptomes from the view of high-dimensional inter-feature correlations. The mqTrans protocol trained a regression model to predict the expression of an mRNA feature from those of the transcription factors (TFs). The difference between the predicted and real expression of an mRNA feature in a query sample was defined as the mqTrans feature. The new mqTrans view facilitated the detection of thirteen transcriptomic features with differentially expressed mqTrans features, but without differential expression in the original transcriptomic values in three independent datasets of lung cancer. These features were called dark biomarkers because they would have been ignored in a conventional differential analysis. The detailed discussion of one dark biomarker, GBP5, and additional validation experiments suggested that the overlapping long non-coding RNAs might have contributed to this interesting phenomenon. In summary, this study aimed to find undifferentially expressed genes with significantly changed mqTrans values in lung cancer. These genes were usually ignored in most biomarker detection studies of undifferential expression. However, their differentially expressed mqTrans values in three independent datasets suggested their strong associations with lung cancer.

Loss of BLK expression as a potential predictor of poor prognosis and immune checkpoint blockade response in NSCLC and contribute to tumor progression

BACKGROUND

Immune checkpoint blockade (ICB) has been proved to have significant anti-tumor effect in the clinical treatment of non-small cell lung cancer (NSCLC). Therefore, biomarkers predicting ICB response can provide better treatment for patients with NSCLC.

METHODS

Differential expression genes (DEGs) were identified by ImmuCellAI database. Copy number alteration (CNA) was analyzed by cBioPortal. The predicted efficiency of 4 genes on cancer immunotherapy was assessed by ROC analysis. The survival value of BLK was analyzed by Kaplan-Meier plotter and Prognoscan analysis. Clinical significance of BLK IHC-TMA score in NSCLC was also explored. The CCK-8 assay, wound healing assay, western blot assay in vitro and subcutaneous xenograft experiments in vivo were used for investigating the functions of BLK. The RNA-sequencing were performed to screen BLK regulated genes and conducted for GO/KEGG enrichment analysis. The transcriptional regulatory factor of BLK promoter region was predicted by ChIP-seq analysis.

RESULTS

39 common DEGs between ICB Response (R) group and No Response (NR) group with NSCLC were identified, in which the CNA frequency of BLK deletion (> 6%) was found. The predicted efficiency of BLK on immunotherapy was performed best in NSCLC (AUC>0.7). Low expression of BLK was related to NSCLC with significantly poor prognosis. BLK overexpression can inhibit growth of NSCLC via activating apoptosis pathway, inhibiting the G2M checkpoint and Glycolysis pathway. The enrichment analysis indicated that BLK regulated genes related to oncogenic potential in NSCLC. Besides, BLK expression was inhibited via H3K27me3 modification in A549 and H1299 cells. BLK mRNA level was negatively correlated with methylation and positively correlated with the tumor purity in NSCLC.

CONCLUSION

Our study provides strong evidence that low expression of BLK may serve as a biomarker for poor prognosis in NSCLC, while response to ICB therapy and contributes to NSCLC tumor progression.

Targeting immune cell types of tumor microenvironment to overcome resistance to PD-1/PD-L1 blockade in lung cancer

Lung cancer is the common malignant tumor with the highest mortality rate. Lung cancer patients have achieved benefits from immunotherapy, including immune checkpoint inhibitors (ICIs) therapy. Unfortunately, cancer patients acquire adaptive immune resistance, leading to poor prognosis. Tumor microenvironment (TME) has been demonstrated to play a critical role in participating in acquired adaptive immune resistance. TME is associated with molecular heterogeneity of immunotherapy efficacy in lung cancer. In this article, we discuss how immune cell types of TME are correlated with immunotherapy in lung cancer. Moreover, we describe the efficacy of immunotherapy in driven gene mutations in lung cancer, including KRAS, TP53, EGFR, ALK, ROS1, KEAP1, ZFHX3, PTCH1, PAK7, UBE3A, TNF-α, NOTCH, LRP1B, FBXW7, and STK11. We also emphasize that modulation of immune cell types of TME could be a promising strategy for improving adaptive immune resistance in lung cancer.

Phosphorylation by IKKβ Promotes the Degradation of HMGCL via NEDD4 in Lung Cancer

Inflammation and metabolic reprogramming are hallmarks of cancer. How inflammation regulates cancer metabolism remains poorly understood. In this study, we found that 3-hydroxy-3-methylglutaryl-CoA lyase (HMGCL), the enzyme that catalyzes the catabolism of leucine and promotes the synthesis of ketone bodies, was downregulated in lung cancer. Downregulation of HMGCL was associated with a larger tumor size and a shorter overall survival time. In a functional study, overexpression of HMGCL increased the content of β-hydroxybutyrate (β-HB) and inhibited the tumorigenicity of lung cancer cells, and deletion of HMGCL promoted de novo tumorigenesis in KP (Kras^G12D;P53^f/f) mice. Mechanistically, tumor necrosis factor α (TNFα) treatment decreased the HMGCL protein level, and IKKβ interacted with HMGCL and phosphorylated it at Ser258, which destabilized HMGCL. Moreover, NEDD4 was identified as the E3 ligase for HMGCL and promoted its degradation. In addition, mutation of Ser258 to alanine inhibited the ubiquitination of HMGCL by NEDD4 and thus inhibited the anchorage-independent growth of lung cancer cells more efficiently than did wild-type HMGCL. In summary, this study demonstrated a link between TNFα-mediated inflammation and cancer metabolism.

Curcumin and Its Analogs in Non-Small Cell Lung Cancer Treatment: Challenges and Expectations

Researchers have made crucial advances in understanding the pathogenesis and therapeutics of non-small cell lung cancer (NSCLC), improving our understanding of lung tumor biology and progression. Although the survival of NSCLC patients has improved due to chemoradiotherapy, targeted therapy, and immunotherapy, overall NSCLC recovery and survival rates remain low. Thus, there is an urgent need for the continued development of novel NSCLC drugs or combination therapies with less toxicity. Although the anticancer effectiveness of curcumin (Cur) and some Cur analogs has been reported in many studies, the results of clinical trials have been inconsistent. Therefore, in this review, we collected the latest related reports about the anti-NSCLC mechanisms of Cur, its analogs, and Cur in combination with other chemotherapeutic agents via the Pubmed database (accessed on 18 June 2022). Furthermore, we speculated on the interplay of Cur and various molecular targets relevant to NSCLC with discovery studio and collected clinical trials of Cur against NSCLC to clarify the role of Cur and its analogs in NSCLC treatment. Despite their challenges, Cur/Cur analogs may serve as promising therapeutic agents or adjuvants for lung carcinoma treatment.

B Cell Receptor Signaling Pathway Mutation as Prognosis Predictor of Immune Checkpoint Inhibitors in Lung Adenocarcinoma by Bioinformatic Analysis

Purpose

The advent of immune checkpoint inhibitors (ICIs) is a revolutionary breakthrough. However, without the selection of a specific target population, the response rate of ICI therapy in lung adenocarcinoma (LUAD) is low, so a clinical challenge has arisen in effectively using biomarkers to determine which patients can benefit from ICI therapy.

Methods

In this study, patients were divided according to whether or not nonsynonymous mutations were present in the BCR signaling pathway, and univariate and multivariate Cox regression models were established based on a LUAD cohort treated with ICIs (Miao-LUAD). Then the relationship between the mutation status of the BCR signaling pathway and the prognosis of immunotherapy was examined. Finally, data from The Cancer Genome Atlas (TCGA) LUAD cohort, the Rizvi-LUAD, the Samstein-LUAD, and the Zhujiang Hospital of Southern Medical University LUAD (Local-LUAD) cohort were combined, and the mutation panorama, immunogenicity, tumor microenvironment (TME) and pathway enrichment analysis between the BCR signaling pathway mutant group (BCR signaling MUT) and the BCR signaling pathway wild group (BCR signaling WT) were comprehensively compared.

Results

It was found that, compared with the BCR signaling WT, the BCR signaling MUT had a significantly improved progression-free survival (PFS) rate and overall survival (OS) rate, higher immunogenicity (tumor mutational burden, neoantigen load, and DNA damage response signaling mutations), and anti-tumor immune microenvironment.

Conclusion

These results revealed that the mutation state of the BCR signaling pathway has potential as a biomarker to predict the efficacy of ICIs in LUAD.

Fatty Acid Metabolic Signaling Pathway Alternation Predict Prognosis of Immune Checkpoint Inhibitors in Glioblastoma

Introduction

Glioblastoma(GBM) is a highly malignant primary brain tumor. Even after undergoing surgery and chemotherapy, patients with this affliction still have little to no chance of survival. Current research on immunotherapy treatment for GBM shows that immune-checkpoint inhibitors (ICIs) may be a promising new treatment method. However, at present, the relationship between the fatty acid metabolic process and the prognosis of GBM patients who are receiving immunotherapy is not clear.

Methods

First, we downloaded a GBM cohort that had been treated with immunotherapy, which included the mutation and prognosis data, and the TCGA-GBM and Jonsson-GBM queues. CIBERSORT and single sample gene set enrichment analysis(ssGSEA) were used to evaluate immune cell scores. Gene set enrichment analysis (GSEA) was used to evaluate the patient’s accessment score. The pRRophetic algorithm was used to evaluate the drug sensitivity of each patient. Univariable and multivariate cox regression analyses, as well as the Kaplan-Meier (KM) method, were used to evaluate the relationship between the fatty acid metabolic process and the prognosis of GBM patients.

Results

The univariate and multivariate cox regression models showed that the fatty acid metabolic process mutant-type (MT) can be used as an independent predictor of the efficacy of immunotherapy for GBM patients. In addition, fatty acid metabolic process MT is related with significantly longer overall survival (OS) time than the wild-type(WT) variant. However, the mutation status of the fatty acid metabolic process has nothing to do with the prognosis of GBM patients who are receiving conventional treatment. Our analysis showed that fatty acid metabolic process MT correlated with significantly increased natural killer T (NKT) cells and significantly decreased CD8+T cells. At the same time, GSEA analysis revealed that fatty acid metabolic process MT was associated with significantly increased immune activation pathways and an enriched fraction of cytokine secretion compared with WT.

Conclusions

We found that fatty acid metabolic process MT may be used as an independent predictor of the efficacy of ICI treatment in GBM patients. Use of the fatty acid metabolic process MT will result in higher immunogenicity rates, a significant increase in the proportion of activated immune cells, and improvement of the immune microenvironment.