Multimodal analysis suggests differential immuno-metabolic crosstalk in lung squamous cell carcinoma and adenocarcinoma

Transparent sparse graph pathway network for analyzing the internal relationship of lung cancer

While it is important to find the key biomarkers and improve the accuracy of disease models, it is equally important to understand their interaction relationships. In this study, a transparent sparse graph pathway network (TSGPN) is proposed based on the structure of graph neural networks. This network simulates the action of genes in vivo, adds to prior knowledge, and improves the model's accuracy. First, the graph connection was constructed according to protein-protein interaction networks and competing endogenous RNA (ceRNA) networks, from which some noise or unimportant connections were spontaneously removed based on the graph attention mechanism and hard concrete estimation. This realized the reconstruction of the ceRNA network representing the influence of other genes in the disease on mRNA. Next, the gene-based interpretation was transformed into a pathway-based interpretation based on the pathway database, and the hidden layer was added to realize the high-dimensional analysis of the pathway. Finally, the experimental results showed that the proposed TSGPN method is superior to other comparison methods in F1 score and AUC, and more importantly, it can effectively display the role of genes. Through data analysis applied to lung cancer prognosis, ten pathways related to LUSC prognosis were found, as well as the key biomarkers closely related to these pathways, such as HOXA10, hsa-mir-182, and LINC02544. The relationship between them was also reconstructed to better explain the internal mechanism of the disease.

The association between the amino acid transporter LAT1, tumor immunometabolic and proliferative features and menopausal status in breast cancer

L-type Amino Acid Transporter 1 (LAT1) facilitates the uptake of specific essential amino acids, and due to this quality, it has been correlated to worse patient outcomes in various cancer types. However, the relationship between LAT1 and various clinical factors, including menopausal status, in mediating LAT1's prognostic effects remains incompletely understood. This is particularly true in the unique subset of tumors that are both obesity-associated and responsive to immunotherapy, including breast cancer. To close this gap, we employed 6 sets of transcriptomic data using the Kaplan-Meier model in the Xena Functional Genomics Explorer, demonstrating that higher LAT1 expression diminishes breast cancer patients' survival probability. Additionally, we analyzed 3'-Deoxy-3'-18F-Fluorothymidine positron emission tomography-computed tomography (18F-FLT PET-CT) images found on The Cancer Imaging Archive (TCIA). After separating all patients based on menopausal status, we correlated the measured 18F-FLT uptake with various clinical parameters quantifying body composition, tumor proliferation, and immune cell infiltration. By analyzing a wealth of deidentified, open-access data, the current study investigates the impact of LAT1 expression on breast cancer prognosis, along with the menopausal status-dependent associations between tumor proliferation, immunometabolism, and systemic metabolism.

Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux

Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the tumor microenvironment (TME) is of emerging importance in cancer research and patient care. However, recent technologies only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and can potentially be extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable current methods are, particularly in TME characterization. Here, we present a computational framework METAFlux (METAbolic Flux balance analysis) to infer metabolic fluxes from bulk or single-cell transcriptomic data. Large-scale experiments using cell-lines, the cancer genome atlas (TCGA), and scRNA-seq data obtained from diverse cancer and immunotherapeutic contexts, including CAR-NK cell therapy, have validated METAFlux's capability to characterize metabolic heterogeneity and metabolic interaction amongst cell types.

Aspartoacylase promotes the process of tumour development and is associated with immune infiltrates in gastric cancer

BACKGROUND

Aspartoacylase (ASPA) is a gene that plays an important role in the metabolic reprogramming of cancer. However, the clinical relevance of ASPA in gastric cancer (GC) has not been demonstrated.

METHODS

The link between ASPA and the clinical features of GC was determined using two public genomic databases. The multivariate Cox proportional hazard model and generalised linear regression model were applied to examine whether the ASPA level is associated with the prognosis and other pathological factors. In addition, the role of specific genes in the infiltration of immune cells in the setting of GC was investigated using a further immunological database. The expression level of various proteins was detected using a western blotting assay. Transwell and methyl thiazolyl tetrazolium tests were applied for the detection of cellular invasion and proliferation, with small hairpin ribonucleic acid used to knockdown ASPA.

RESULTS

According to the multivariate Cox regression results, the down-regulated ASPA expression is a distinct prognostic factor. Furthermore, ASPA has significant positive correlations with the infiltration of immune cells in GC lesions. Compared to the non-cancer tissues, the GC tissues had a significantly lower level of ASPA expression (p < 0.05). Using knockdown and overexpression techniques, it was demonstrated that ASPA affects the capacity of cell lines for GC to both proliferate and invade.

CONCLUSION

Overall, ASPA could promote the occurrence and development of GC and presents a promising predictive biomarker for the disease since it is favourably connected with immune infiltrates and negatively correlated with prognosis.

The lung, the niche, and the microbe: Exploring the lung microbiome in cancer and immunity

The lung is a complex and unique organ system whose biology is strongly influenced by environmental exposure, oxygen abundance, connection to extrapulmonary systems via a dense capillary network, and an array of immune cells that reside in the tissue at steady state. The lung also harbors a low biomass community of commensal microorganisms that are dynamic during both health and disease with the capacity to modulate regulatory immune responses during diseases such as cancer. Lung cancer is the third most common cancer worldwide with the highest mortality rate amongst cancers due to the difficulty of an early diagnosis. This review discusses the current body of work addressing the interactions between the lung microbiota and the immune system, and how these two components of the pulmonary system are linked to lung cancer development and outcomes. Bringing in lessons from broader studies examining the effects of the gut microbiota on cancer outcomes, we highlight many challenges and gaps in this nascent field.

Visceral Obesity in Non-Small Cell Lung Cancer

While obesity measured by body mass index (BMI) has been paradoxically associated with reduced risk and better outcome for lung cancer, recent studies suggest that the harm of obesity becomes apparent when measured as visceral adiposity. However, the prevalence of visceral obesity and its associations with demographic and tumor features are not established. We therefore conducted an observational study of visceral obesity in 994 non-small cell lung cancer (NSCLC) patients treated during 2008-2020 at our institution. Routine computerized tomography (CT) images of the patients, obtained within a year of tumor resection or biopsy, were used to measure cross-sectional abdominal fat areas. Important aspects of the measurement approach such as inter-observer variability and time stability were examined. Visceral obesity was semi-quantified as visceral fat index (VFI), the fraction of fat area that was visceral. VFI was found to be higher in males compared to females, and in former compared to current or never smokers. There was no association of VFI with tumor histology or stage. A gene expression-based measure of tumor immunogenicity was negatively associated with VFI but had no bearing with BMI. Visceral obesity is appraisable in routine CT and can be an important correlate in lung cancer studies.

Navigate Towards the Immunotherapy Era: Value of Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer Patients With Brain Metastases

Brain metastases (BMs) in non-small-cell lung cancer (NSCLC) patients are associated with significant morbidity and poor prognosis. Immune checkpoint inhibitors (ICIs) have resulted in a paradigm shift in the management of advanced NSCLC. However, the value of ICIs in NSCLC patients with BMs remains unclear because patients with BMs are routinely excluded in numerous prospective trials on ICIs. Here, starting from the mechanisms of ICIs for BMs, we will reveal the value of ICIs by reviewing the efficacy and adverse effects of ICIs monotherapy as well as promising combination strategies, such as combinations with chemotherapy, radiotherapy, and anti-angiogenic drugs, etc. In addition, the methods of patient selection and response assessment will be summarized to assist clinical practice and further studies.

Insulin and cancer: a tangled web

For a century, since the pioneering work of Otto Warburg, the interwoven relationship between metabolism and cancer has been appreciated. More recently, with obesity rates rising in the U.S. and worldwide, epidemiologic evidence has supported a link between obesity and cancer. A substantial body of work seeks to mechanistically unpack the association between obesity, altered metabolism, and cancer. Without question, these relationships are multifactorial and cannot be distilled to a single obesity- and metabolism-altering hormone, substrate, or factor. However, it is important to understand the hormone-specific associations between metabolism and cancer. Here, we review the links between obesity, metabolic dysregulation, insulin, and cancer, with an emphasis on current investigational metabolic adjuncts to standard-of-care cancer treatment.