Development and Validation of an E2F-Related Gene Signature to Predict Prognosis of Patients With Lung Squamous Cell Carcinoma

Systematic integration of machine learning algorithms to develop immune escape-related signatures to improve clinical outcomes in lung adenocarcinoma patients

Background

Immune escape has recently emerged as one of the barriers to the efficacy of immunotherapy in lung adenocarcinoma (LUAD). However, the clinical significance and function of immune escape markers in LUAD have largely not been clarified.

Methods

In this study, we constructed a stable and accurate immune escape score (IERS) by systematically integrating 10 machine learning algorithms. We further investigated the clinical significance, functional status, TME interactions, and genomic alterations of different IERS subtypes to explore potential mechanisms. In addition, we validated the most important variable in the model through cellular experiments.

Results

The IERS is an independent risk factor for overall survival, superior to traditional clinical variables and published molecular signatures. IERS-based risk stratification can be well applied to LUAD patients. In addition, high IERS is associated with stronger tumor proliferation and immunosuppression. Low IERS exhibited abundant lymphocyte infiltration and active immune activity. Finally, high IERS is more sensitive to first-line chemotherapy for LUAD, while low IERS is more sensitive to immunotherapy.

Conclusion

In conclusion, IERS may serve as a promising clinical tool to improve risk stratification and clinical management of individual LUAD patients and may enhance the understanding of immune escape.

Development and validation of a combined glycolysis and immune prognostic signature for lung squamous cell carcinoma

Background: The involvement of glycolysis in the regulation of the tumor immune microenvironment has become a novel research field. In this study, the specific functions and clinical significance of glycolysis-related genes (GRGs) and immune-related genes (IRGs) were systematically characterized in lung squamous cell carcinoma (LUSC).

Methods: We evaluated the prognostic value, interactions, somatic mutations, and copy-number variations of GRGs and IRGs in LUSC from a dataset of The Cancer Genome Atlas (TCGA). An integrated glycolysis–immune score (GIS) model was generated by random forest algorithm and stepwise Cox regression analysis. The predictive power of the GIS was examined by survival analysis, receiver operating characteristics, univariate and multivariate analyses, and subgroup analysis. The correlations between GIS and biological functions, glycolysis, immune activity, immune cell infiltration, and genomic changes were analyzed, and the potential of GIS to guide clinical treatment decisions was evaluated.

Results: A total of 54 prognostic GRGs and IRGs were identified, and a strong correlation was noted among them. However, most of them had somatic mutations and a high incidence of CNV. The GIS model that contained two GRGs (PYGB and MDH1) and three IRGs (TSLP, SERPIND1, and GDF2) was generated and a high GIS indicated poor survival. Moreover, we found that low GIS was associated with immune pathway activation, M1 macrophage infiltration, and higher immune scores. Finally, patients with low GIS were more sensitive to chemotherapy and immunotherapy.

Conclusion: An integrated model based on glycolysis and immune genes can distinguish the biological functions and immune infiltration patterns of individual tumors, quantitatively estimate the prognosis of patients with LUSC, and guide chemotherapy and immunotherapy decisions.

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

Background

Lipid metabolism pivotally contributes to the incidence and development of lung adenocarcinoma (LUAD). The interaction of lipid metabolism and tumor microenvironment (TME) has become a new research direction.

Methods

Using the 1107 LUAD records from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, a comprehensive exploration was performed on the heterogeneous lipid metabolism subtypes based on lipid metabolism genes (LMGs) and immune-related genes (LRGs). The clinical significance, functional status, TME interaction and genomic changes of different subtypes were further studied. A new scoring system, lipid-immune score (LIS), was developed and validated.

Results

Two heterogeneous subtypes, which express more LMGs and show the characteristics of tumor metabolism and proliferation, are defined as lipid metabolism phenotypes. The prognosis of lipid metabolism phenotype is poor, and it is more common in patients with tumor progression. Expressing more IRGs, enrichment of immunoactive pathways and infiltration of effector immune cells are defined as immunoactive phenotypes. The immunoactive phenotype has a better prognosis and stronger anti-tumor immunity and is more sensitive to immunotherapy. In addition, KEAP1 is a driving mutant gene in the lipid metabolism subtype. Finally, LIS was developed and confirmed to be a robust predictor of overall survival (OS) and immunotherapy in LUAD patients.

Conclusion

Two heterogeneous subtypes of LUAD (lipid metabolism subtype and immune activity subtype) were identified to evaluate prognosis and immunotherapy sensitivity. Our research promotes the understanding of the interaction between lipid metabolism and TME and offers a novel direction for clinical management and precision therapy aimed to LUAD patients.

Metabolic reprogramming and crosstalk of cancer-related fibroblasts and immune cells in the tumor microenvironment

It is notorious that cancer cells alter their metabolism to adjust to harsh environments of hypoxia and nutritional starvation. Metabolic reprogramming most often occurs in the tumor microenvironment (TME). TME is defined as the cellular environment in which the tumor resides. This includes surrounding blood vessels, fibroblasts, immune cells, signaling molecules and the extracellular matrix (ECM). It is increasingly recognized that cancer cells, fibroblasts and immune cells within TME can regulate tumor progression through metabolic reprogramming. As the most significant proportion of cells among all the stromal cells that constitute TME, cancer-associated fibroblasts (CAFs) are closely associated with tumorigenesis and progression. Multitudinous studies have shown that CAFs participate in and promote tumor metabolic reprogramming and exert regulatory effects via the dysregulation of metabolic pathways. Previous studies have demonstrated that curbing the substance exchange between CAFs and tumor cells can dramatically restrain tumor growth. Emerging studies suggest that CAFs within the TME have emerged as important determinants of metabolic reprogramming. Metabolic reprogramming also occurs in the metabolic pattern of immune cells. In the meanwhile, immune cell phenotype and functions are metabolically regulated. Notably, immune cell functions influenced by metabolic programs may ultimately lead to alterations in tumor immunity. Despite the fact that multiple previous researches have been devoted to studying the interplays between different cells in the tumor microenvironment, the complicated relationship between CAFs and immune cells and implications of metabolic reprogramming remains unknown and requires further investigation. In this review, we discuss our current comprehension of metabolic reprogramming of CAFs and immune cells (mainly glucose, amino acid, and lipid metabolism) and crosstalk between them that induces immune responses, and we also highlight their contributions to tumorigenesis and progression. Furthermore, we underscore potential therapeutic opportunities arising from metabolism dysregulation and metabolic crosstalk, focusing on strategies targeting CAFs and immune cell metabolic crosstalk in cancer immunotherapy.

Development and validation of a robust necroptosis related classifier for colon adenocarcinoma

Background: Necroptosis, a novel form of apoptosis, plays a crucial function in the progression of colon adenocarcinoma (COAD) and is expected to be triggered in cancer therapy for enhancing anti-tumor immunity. However, the function of necroptosis in tumors and its relationship with the tumor microenvironment (TME) remains largely unclear.

Methods: Necroptosis-related genes (NRGs) were collected from high-quality literature. Using The Cancer Genome Atlas (TCGA) (https://cancergenome.nih.gov) and the Gene Expression Omnibus (GEO) (www.ncbi.nlm.nih.gov/geo) meta-cohorts, a robust risk model was constructed to systematically examine the clinical value, functional status, the role of TME based on the risk model, as also the genomic variations.

Results: A risk model containing nine NRGs, including TNF receptor-associated factor (TRAF2), TNF receptor 1 associated via death domain (TRADD), ubiquitin carboxyl-terminal hydrolase 21 (USP21), TNF receptor superfamily, member 6 (FAS), tumor necrosis factor receptor superfamily 10B (TNFRSF10B), mitogen-activated protein kinase 8 (MAPK8), mixed lineage kinase domain-like (MLKL), TNF receptor-associated factor 5 (TRAF5), and recombinant receptor-interacting serine-threonine kinase 3 (RIPK3), was constructed. The risk model’s stability and accuracy were demonstrated in training, as also the validation cohorts; it was verified as an independent prognostic model for COAD. High-risk group patients developed “cold” tumors having active tumor proliferation and immunosuppression, while those in the low-risk group developed “hot” tumors with active immune and cell killing functions. Moreover, a higher number of copy number variations in the genome and fewer somatic mutations were found in high-risk group patients. Furthermore, higher sensitivity towards immunotherapy and chemotherapy was seen in patients of the low-risk group.

Conclusion: A reliable risk model based on NRGs to assess patient prognosis and guide clinical decision-making was constructed and validated. Our findings may contribute to the understanding of necroptosis and are expected to aid clinical management and guide precision treatment for patients with COAD.

Deep learning reveals cuproptosis features assist in predict prognosis and guide immunotherapy in lung adenocarcinoma

BACKGROUND

Cuproptosis is a recently found non-apoptotic cell death type that holds promise as an emerging therapeutic modality in lung adenocarcinoma (LUAD) patients who develop resistance to radiotherapy and chemotherapy. However, the Cuproptosis' role in the onset and progression of LUAD remains unclear.

METHODS

Cuproptosis-related genes (CRGs) were identified by a co-expression network approach based on LUAD cell line data from radiotherapy, and a robust risk model was developed using deep learning techniques based on prognostic CRGs and explored the value of deep learning models systematically for clinical applications, functional enrichment analysis, immune infiltration analysis, and genomic variation analysis.

RESULTS

A three-layer artificial neural network risk model was constructed based on 15 independent prognostic radiotherapy-related CRGs. The risk model was observed as a robust independent prognostic factor for LUAD in the training as well as three external validation cohorts. The patients present in the low-risk group were found to have immune "hot" tumors exhibiting anticancer activity, whereas the high-risk group patients had immune "cold" tumors with active metabolism and proliferation. The high-risk group patients were more sensitive to chemotherapy whereas the low-risk group patients were more sensitive to immunotherapy. Genomic variants did not vary considerably among both groups of patients.

CONCLUSION

Our findings advance the understanding of cuproptosis and offer fresh perspectives on the clinical management and precision therapy of LUAD.

Sphingolipid metabolism is associated with osteosarcoma metastasis and prognosis: Evidence from interaction analysis

BACKGROUND

Metabolism is widely involved in the occurrence and development of cancer. However, its role in osteosarcoma (OS) has not been elucidated.

METHODS

The open-accessed data included in this study were downloaded from The Cancer Genome Atlas (TCGA) database (TARGET-OS project). All the analysis was performed in R environments.

RESULTS

Based on the single sample gene set enrichment analysis algorithm, we quantified 21 metabolism terms in OS patients. Among these, sphingolipid metabolism was upregulated in the metastatic OS tissue and associated with a worse prognosis, therefore aroused our interest and selected for further analysis. Our result showed that sphingolipid metabolism could activate the Notch signaling and angiogenesis pathway, which might be responsible for the metastasis ability and poor prognosis. A protein-protein interaction network was constructed to illustrate the interaction of the differentially expressed genes between high and low sphingolipid metabolism. Immune analysis showed that multiple immune terms were upregulated in patients with high sphingolipid metabolism activity. Then, a prognosis model was established based on the identified DEGs between patients with high and low sphingolipid metabolism, which showed great prediction efficiency. Pathway enrichment showed the pathway of myogenesis, spermatogenesis, peroxisome, KRAS signaling, pancreas beta cells, apical surface, MYC target, WNT beta-catenin signaling, late estrogen response and apical junction was significantly enriched in high risk patients. Moreover, we found that the model genes MAGEB1, NPIPA2, PLA2G4B and MAGEA3 could effectively indicate sphingolipid metabolism and risk group.

CONCLUSIONS

In summary, our result showed that sphingolipid metabolism is associated with osteosarcoma metastasis and prognosis, which has the potential to be a therapeutic target for OS.