Machine learning reveals two heterogeneous subtypes to assist immune therapy based on lipid metabolism in lung adenocarcinoma

Informing immunotherapy with multi-omics driven machine learning

Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.

Analyzing the impact of metabolism on immune cells in tumor microenvironment to promote the development of immunotherapy

Tumor metabolism and tumor immunity are inextricably linked. Targeting the metabolism of tumors is a point worth studying in tumor immunotherapy. Recently, the influence of the metabolism of tumors and immune cells on the occurrence, proliferation, metastasis, and prognosis of tumors has attracted more attention. Tumor tissue forms a specific tumor microenvironment (TME). In addition to tumor cells, there are also immune cells, stromal cells, and other cells in TME. To adapt to the environment, tumor cells go through the metabolism reprogramming of various substances. The metabolism reprogramming of tumor cells may further affect the formation of the tumor microenvironment and the function of a variety of cells, especially immune cells, eventually promoting tumor development. Therefore, it is necessary to study the metabolism of tumor cells and its effects on immune cells to guide tumor immunotherapy. Inhibiting tumor metabolism may restore immune balance and promote the immune response in tumors. This article will describe glucose metabolism, lipid metabolism, amino acid metabolism, and immune cells in tumors. Besides, the impact of metabolism on the immune cells in TME is also discussed for analyzing and exploring tumor immunotherapy.

Comprehensive analysis illustrating the role of PANoptosis-related genes in lung cancer based on bioinformatic algorithms and experiments

Background: Recently, PANoptosis has aroused the interest of researchers for its role in cancers. However, the studies that investigated PANoptosis in lung cancer are still few. Methods: The public data were mainly collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database. R software was utilized for the analysis of public data. Quantitative real-time (qRT) polymerase chain reaction (PCR) was used to measure the RNA level of FADD. The cell proliferation ability was evaluated using the CCK8, colony formation, and 5-ethynyl-2'-deoxyuridine (EdU) assays. Western blot was used to detect the protein level of specific molecules. Flow cytometry analysis and TUNEL staining were used to evaluate cell apoptosis. Results: In our study, we collected the PANoptosis-related genes from previous studies. Through series analysis, we identified the FADD, an adaptor of PANoptosis and apoptosis, for further analysis. Results showed that FADD is one of the prominent risk factors in lung cancer, mainly localized in nucleoplasm and cytosol. We next performed immune infiltration analysis and biological enrichment to illustrate the underlying cause of FADD in lung cancer. Subsequently, we discovered that the patients with a high level of FADD might respond worse to immunotherapy but better to AICAR, bortezomib, docetaxel, and gemcitabine. In vitro experiments indicated that inhibiting FADD could reduce significantly the ability of cancerous lung cells to proliferate. Meanwhile, we found that the knockdown of FADD promotes the apoptosis and pyroptosis. Ultimately, a prognosis signature was identified based on the FADD-regulated genes, which showed satisfactory prediction efficiency on patients with lung cancer. Conclusion: Our result can provide a novel direction for future studies focused on the role of PANoptosis in lung cancer.

Investigation of M2 macrophage-related gene affecting patients prognosis and drug sensitivity in non-small cell lung cancer: Evidence from bioinformatic and experiments

Background

The progression process of lung cancer can be accelerated by M2 macrophages. However, genes that affect M2 macrophage polarization remain unidentified.

Methods

The Cancer Genome Atlas, Gene Expression Omnibus, and Arrayexpress databases were used to obtain open-access data. The analysis of public data was mostly performed with R studio. The RNA levels of specific genes were detected using quantitative real-time PCR. The proliferation ability of the cells was assessed by CCK8, colony formation, and EdU assays.

Results

Based on the multiple datasets, we noticed a poor prognosis in patients with high M2 macrophage infiltration. There were 114 genes differentially expressed between high and low M2 macrophages infiltrated samples, regarded as M2 macrophage-related genes. Subsequently, a prognosis prediction signature consisting of ABHD5, HS3ST2, TM6SF1, CAPZA2, LEPROT, HNMT, and MRO was identified and presented a satisfactory performance. The pathway enrichment results revealed a positive correlation between riskscore and enrichment scores for most immunotherapy-related positive terms. Also, there might be an increase in genomic instability among patients at high risk. Interestingly, low risk patients are most likely to benefit from PD-1 therapy, while high risk patients may benefit from CTLA-4 therapy. Meanwhile, the estimated IC50 of seven drugs differs significantly between two risk groups, including Cisplatin, Docetaxel, Doxorubicin, Gefitinib, Paclitaxel, Sunitinib and Vinorelbine. Moreover, further experiments indicated that HNMT was overexpressed and can enhance the proliferation ability in lung cancer cells.

Conclusions

In summary, our study identified the molecules significantly affecting M2 macrophage infiltration and identified a prognosis signature that robustly indicated patients prognosis. Moreover, we validated the cancer-promoting effect of HNMT using in vitro experiments.