Fatty acid binding protein 5 regulates lipogenesis and tumor growth in lung adenocarcinoma

Heterogeneity of Lipid Metabolism and its Clinical and Immune Correlation in Lung Adenocarcinoma

INTRODUCTION

The role of lipid metabolism in lung adenocarcinoma (LUAD) is not completely researched. Lipid metabolism reprogramming is a characteristic of malignancies and contributes to carcinogenesis and progression. The transcriptome and scRNA- seq data and clinical information were downloaded from the public databases.

METHODS

Lipid metabolism pathways were collected from the MSigDB database, and molecular subtypes were classified based on lipid metabolism features via consensus clustering. The bidirectional crosstalk between immune cells and malignant cells was analyzed. Differences in lipid metabolism at the single-cell level and their correlation with the tumor microenvironment (TME) were also studied. LUAD patients were classified into two subtypes, showing distinct mutation and lipid metabolism features based on lipid metabolism characteristics. Meanwhile, significant differences in the overall survival, clinical characteristics, and immune landscape were observed between the two subtypes. We also found that clust1 had higher oxidative stress status. There were 116 differentially expressed genes between the two subtypes, which were significantly associated with cell cycle progression. We identified 4001 immune cells, including 483 malignant cells and 3518 normal cells, and found active intercellular communication and significant differences in lipid metabolism characteristics between the malignant cells and normal cells. Furthermore, several lipid metabolism pathways were found to be associated with TME factors, including hypoxia and angiogenesis.

RESULT

The current findings indicated that lipid metabolism was involved in the development and cellular heterogeneity of LUAD and revealed widespread reprogramming across multiple cellular elements in the TME of LUAD.

CONCLUSION

This characterization improved the current understanding of tumor biology and enabled the identification of novel targets for immunotherapy.

ALKBH5 enhances lipid metabolism reprogramming by increasing stability of FABP5 to promote pancreatic neuroendocrine neoplasms progression in an m6A-IGF2BP2-dependent manner

The process of post-transcriptional regulation has been recognized to be significantly impacted by the presence of N6-methyladenosine (m6A) modification. As an m6A demethylase, ALKBH5 has been shown to contribute to the progression of different cancers by increasing expression of several oncogenes. Hence, a better understanding of the key targets of ALKBH5 in cancer cells could potentially lead to the development of new therapeutic targets. However, the specific role of ALKBH5 in pancreatic neuroendocrine neoplasms (pNENs) remains largely unknown. Here, we demonstrated that ALKBH5 was up-regulated in pNENs and played a critical role in tumor growth and lipid metabolism. Mechanistically, ALKBH5 over-expression was found to increase the expression of FABP5 in an m6A-IGF2BP2 dependent manner, leading to disorders in lipid metabolism. Additionally, ALKBH5 was found to activate PI3K/Akt/mTOR signaling pathway, resulting in enhanced lipid metabolism and proliferation abilities. In conclusion, our study uncovers the ALKBH5/IGF2BP2/FABP5/mTOR axis as a mechanism for aberrant m6A modification in lipid metabolism and highlights a new molecular basis for the development of therapeutic strategies for pNENs treatment.

Applying machine learning algorithms to develop a survival prediction model for lung adenocarcinoma based on genes related to fatty acid metabolism

Background: The progression of lung adenocarcinoma (LUAD) may be related to abnormal fatty acid metabolism (FAM). The present study investigated the relationship between FAM-related genes and LUAD prognosis. Methods: LUAD samples from The Cancer Genome Atlas were collected. The scores of FAM-associated pathways from the Kyoto Encyclopedia of Genes and Genomes website were calculated using the single sample gene set enrichment analysis. ConsensusClusterPlus and cumulative distribution function were used to classify molecular subtypes for LUAD. Key genes were obtained using limma package, Cox regression analysis, and six machine learning algorithms (GBM, LASSO, XGBoost, SVM, random forest, and decision trees), and a RiskScore model was established. According to the RiskScore model and clinical features, a nomogram was developed and evaluated for its prediction performance using a calibration curve. Differences in immune abnormalities among patients with different subtypes and RiskScores were analyzed by the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data, CIBERSORT, and single sample gene set enrichment analysis. Patients' drug sensitivity was predicted by the pRRophetic package in R language. Results: LUAD samples had lower scores of FAM-related pathways. Three molecular subtypes (C1, C2, and C3) were defined. Analysis on differential prognosis showed that the C1 subtype had the most favorable prognosis, followed by the C2 subtype, and the C3 subtype had the worst prognosis. The C3 subtype had lower immune infiltration. A total of 12 key genes (SLC2A1, PKP2, FAM83A, TCN1, MS4A1, CLIC6, UBE2S, RRM2, CDC45, IGF2BP1, ANGPTL4, and CD109) were screened and used to develop a RiskScore model. Survival chance of patients in the high-RiskScore group was significantly lower. The low-RiskScore group showed higher immune score and higher expression of most immune checkpoint genes. Patients with a high RiskScore were more likely to benefit from the six anticancer drugs we screened in this study. Conclusion: We developed a RiskScore model using FAM-related genes to help predict LUAD prognosis and develop new targeted drugs.

Comprehensive analysis of Cuproplasia and immune microenvironment in lung adenocarcinoma

Background: Trace elements such as copper are essential for human health. Recently the journal Nat Rev Cancer has put forward the concept of Cuproplasia, a way of promoting tumor growth through reliance on copper. We attempted to conduct a comprehensive analysis of Cuproplasia-related genes in lung adenocarcinoma (LUAD) to explore the mechanism of action of Cuproplasia-related genes in LUAD. Method: Transcriptome data and clinical information of LUAD were obtained from TCGA-LUAD and GSE31210, and prognostic models of Cuproplasia-related genes were constructed and verified by regression analysis of GSVA, WGCNA, univariate COX and lasso. The signal pathways affected by Cuproplasia-related genes were analyzed by GO, KEGG and hallmarK pathway enrichment methods. Five immunocell infiltration algorithms and IMVIGOR210 data were used to analyze immune cell content and immunotherapy outcomes in the high-low risk group. Results: In the results of WGCNA, BROWN and TURQUOISE were identified as modules closely related to Cuproplasia score. In the end, lasso regression analysis established a Cuproplasia-related signature (CRS) based on 24 genes, and the prognosis of high-risk populations was worse in TCGA-LUAD and GSE31210 datasets. The enrichment analysis showed that copper proliferation was mainly through chromosome, cell cycle, dna replication, g2m checkpoint and other pathways. Immunoinfiltration analysis showed that there were differences in the content of macrophages among the four algorithms. And IMVIGOR210 found that the lower the score, the more effective the immunotherapy was. Conclusion: The Cuproplasia related gene can be used to predict the prognosis and immunotherapy outcome of LUAD patients, and may exert its effect by affecting chromosome-related pathways and macrophages.

FABP5 regulates lipid metabolism to facilitate pancreatic neuroendocrine neoplasms progression via FASN mediated Wnt/β-catenin pathway

Pancreatic neuroendocrine neoplasms (pNENs) are among the most frequently occurring neuroendocrine neoplasms (NENs) and require targeted therapy. High levels of fatty acid binding protein 5 (FABP5) are involved in tumor progression, but its role in pNENs remains unclear. We investigated the mRNA and protein levels of FABP5 in pNEN tissues and cell lines and found them to be upregulated. We evaluated changes in cell proliferation using CCK-8, colony formation, and 5-ethynyl-2'-deoxyuridine assays and examined the effects on cell migration and invasion using transwell assays. We found that knockdown of FABP5 suppressed the proliferation, migration, and invasion of pNEN cell lines, while overexpression of FABP5 had the opposite effect. Co-immunoprecipitation experiments were performed to clarify the interaction between FABP5 and fatty acid synthase (FASN). We further showed that FABP5 regulates the expression of FASN via the ubiquitin proteasome pathway and both proteins facilitate the progression of pNENs. Our study demonstrated that FABP5 acts as an oncogene by promoting lipid droplet deposition and activating the WNT/β-catenin signaling pathway. Moreover, the carcinogenic effects of FABP5 can be reversed by orlistat, providing a novel therapeutic intervention option.

The Multifunctional Family of Mammalian Fatty Acid-Binding Proteins

Fatty acid-binding proteins (FABPs) are small lipid-binding proteins abundantly expressed in tissues that are highly active in fatty acid (FA) metabolism. Ten mammalian FABPs have been identified, with tissue-specific expression patterns and highly conserved tertiary structures. FABPs were initially studied as intracellular FA transport proteins. Further investigation has demonstrated their participation in lipid metabolism, both directly and via regulation of gene expression, and in signaling within their cells of expression. There is also evidence that they may be secreted and have functional impact via the circulation. It has also been shown that the FABP ligand binding repertoire extends beyond long-chain FAs and that their functional properties also involve participation in systemic metabolism. This article reviews the present understanding of FABP functions and their apparent roles in disease, particularly metabolic and inflammation-related disorders and cancers.

Delineation of fatty acid metabolism in gastric cancer: Therapeutic implications

BACKGROUND

The prognosis of gastric cancer is extremely poor. Metabolic reprogramming involving lipids has been associated with cancer occurrence and progression.

AIM

To illustrate fatty acid metabolic mechanisms in gastric cancer, detect core genes, develop a prognostic model, and provide treatment options.

METHODS

Raw data from The Cancer Genome Atlas and Gene Expression Omnibus databases were collected and analyzed. Differentially expressed fatty acid metabolism genes were identified and incorporated into a risk model based on least absolute shrinkage and selection operator regression analysis. Then, patients from The Cancer Genome Atlas were assigned to high- and low-risk cohorts according to the mean value of the risk score as the threshold, which was verified in the Gene Expression Omnibus database. Relationships between chemotherapeutic sensitivity and tumor microenvironment features were assessed.

RESULTS

An integrated evaluation was performed in this study. Fatty acid metabolism-related genes were used to construct the risk model. Patients classified into the high-risk cohort were considered to be resistant to chemotherapy based on results of the "pRRophetic" R package. Patients in the high-risk cohort were associated with type I/II interferon activation, increased inflammation level, immune cell infiltration, and tumor immune dysfunction based on the exclusion algorithm, indicating the potential benefit of immunotherapy in these patients.

CONCLUSION

We constructed a fatty acid-related risk score model to assess the comprehensive fatty acid features in gastric cancer and validated its vital role in prognosis, chemotherapy sensitivity, and immunotherapy.

Delineation of fatty acid metabolism in gastric cancer: Therapeutic implications

BACKGROUND

The prognosis of gastric cancer is extremely poor. Metabolic reprogramming involving lipids has been associated with cancer occurrence and progression.

AIM

To illustrate fatty acid metabolic mechanisms in gastric cancer, detect core genes, develop a prognostic model, and provide treatment options.

METHODS

Raw data from The Cancer Genome Atlas and Gene Expression Omnibus databases were collected and analyzed. Differentially expressed fatty acid metabolism genes were identified and incorporated into a risk model based on least absolute shrinkage and selection operator regression analysis. Then, patients from The Cancer Genome Atlas were assigned to high- and low-risk cohorts according to the mean value of the risk score as the threshold, which was verified in the Gene Expression Omnibus database. Relationships between chemotherapeutic sensitivity and tumor microenvironment features were assessed.

RESULTS

An integrated evaluation was performed in this study. Fatty acid metabolism-related genes were used to construct the risk model. Patients classified into the high-risk cohort were considered to be resistant to chemotherapy based on results of the “pRRophetic” R package. Patients in the high-risk cohort were associated with type I/II interferon activation, increased inflammation level, immune cell infiltration, and tumor immune dysfunction based on the exclusion algorithm, indicating the potential benefit of immunotherapy in these patients.

CONCLUSION

We constructed a fatty acid-related risk score model to assess the comprehensive fatty acid features in gastric cancer and validated its vital role in prognosis, chemotherapy sensitivity, and immunotherapy.

Metabolic dependencies and targets in ovarian cancer

Reprogramming of cellular metabolism is a hallmark of cancer. Cancer cells undergo metabolic adaptations to maintain tumorigenicity and survive under the attack of immune cells and chemotherapy in the tumor microenvironment. Metabolic alterations in ovarian cancer in part overlap with findings from other solid tumors and in part reflect unique traits. Altered metabolic pathways not only facilitate ovarian cancer cells' survival and proliferation but also endow them to metastasize, acquire resistance to chemotherapy, maintain cancer stem cell phenotype and escape the effects of anti-tumor immune defense. In this review, we comprehensively review the metabolic signatures of ovarian cancer and their impact on cancer initiation, progression, and resistance to treatment. We highlight novel therapeutic strategies targeting metabolic pathways under development.

Oncogenic role and potential regulatory mechanism of fatty acid binding protein 5 based on a pan-cancer analysis

As one member of fatty acid binding proteins (FABPs), FABP5 makes a contribution in the occurrence and development of several tumor types, but existing analysis about FABP5 and FABP5-related molecular mechanism remains limited. Meanwhile, some tumor patients showed limited response rates to current immunotherapy, and more potential targets need to be explored for the improvement of immunotherapy. In this study, we made a pan-cancer analysis of FABP5 based on the clinical data from The Cancer Genome Atlas database for the first time. FABP5 overexpression was observed in many tumor types, and was statistically associated with poor prognosis of several tumor types. Additionally, we further explored FABP5-related miRNAs and corresponding lncRNAs. Then, miR-577-FABP5 regulatory network in kidney renal clear cell carcinoma as well as CD27-AS1/GUSBP11/SNHG16/TTC28-AS1-miR-22-3p-FABP5 competing endogenous RNA regulatory network in liver hepatocellular carcinoma were constructed. Meanwhile, Western Blot and reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) analysis were used to verify miR-22-3p-FABP5 relationship in LIHC cell lines. Moreover, the potential relationships of FABP5 with immune infiltration and six immune checkpoints (CD274, CTLA4, HAVCR2, LAG3, PDCD1 and TIGIT) were discovered. Our work not only deepens the understanding of FABP5's functions in multiple tumors and supplements existing FABP5-related mechanisms, but also provides more possibilities for immunotherapy.

Single-cell and spatial transcriptome analyses revealed cell heterogeneity and immune environment alternations in metastatic axillary lymph nodes in breast cancer

BACKGROUND

Tumor heterogeneity plays essential roles in developing cancer therapies, including therapies for breast cancer (BC). In addition, it is also very important to understand the relationships between tumor microenvironments and the systematic immune environment.

METHODS

Here, we performed single-cell, VDJ sequencing and spatial transcriptome analyses on tumor and adjacent normal tissue as well as axillar lymph nodes (LNs) and peripheral blood mononuclear cells (PBMCs) from 8 BC patients.

RESULTS

We found that myeloid cells exhibited environment-dependent plasticity, where a group of macrophages with both M1 and M2 signatures possessed high tumor specificity spatially and was associated with worse patient survival. Cytotoxic T cells in tumor sites evolved in a separate path from those in the circulatory system. T cell receptor (TCR) repertoires in metastatic LNs showed significant higher consistency with TCRs in tumor than those in nonmetastatic LNs and PBMCs, suggesting the existence of common neo-antigens across metastatic LNs and primary tumor cites. In addition, the immune environment in metastatic LNs had transformed into a tumor-like status, where pro-inflammatory macrophages and exhausted T cells were upregulated, accompanied by a decrease in B cells and neutrophils. Finally, cell interactions showed that cancer-associated fibroblasts (CAFs) contributed most to shaping the immune-suppressive microenvironment, while CD8⁺ cells were the most signal-responsive cells.

CONCLUSIONS

This study revealed the cell structures of both micro- and macroenvironments, revealed how different cells diverged in related contexts as well as their prognostic capacities, and displayed a landscape of cell interactions with spatial information.

A Systematic Role of Metabolomics, Metabolic Pathways, and Chemical Metabolism in Lung Cancer

Lung cancer (LC) is considered as one of the leading causes of cancer-associated mortalities. Cancer cells' reprogrammed metabolism results in changes in metabolite concentrations, which can be utilized to identify a distinct metabolic pattern or fingerprint for cancer detection or diagnosis. By detecting different metabolic variations in the expression levels of LC patients, this will help and enhance early diagnosis methods as well as new treatment strategies. The majority of patients are identified at advanced stages after undergoing a number of surgical procedures or diagnostic testing, including the invasive procedures. This could be overcome by understanding the mechanism and function of differently regulated metabolites. Significant variations in the metabolites present in the different samples can be analyzed and used as early biomarkers. They could also be used to analyze the specific progression and type as well as stages of cancer type making it easier for the treatment process. The main aim of this review article is to focus on rewired metabolic pathways and the associated metabolite alterations that can be used as diagnostic and therapeutic targets in lung cancer diagnosis as well as treatment strategies.

Macrophage-related molecular subtypes in lung adenocarcinoma identify novel tumor microenvironment with prognostic and therapeutic implications

Background: Lung adenocarcinoma (LUAD) is a life-threatening malignant tumor, contributing for the largest cancer burden worldwide. Tumor microenvironment (TME) is composed of various immune cells, stromal cells and tumor cells, which is highly associated with the cancer prognosis and the response to immunotherapy, in which macrophages in TME have been revealing a potential target for cancer treatment. In this study, we sought to further explore the role of macrophages in LUAD progression and establish a risk model related to macrophages for LUAD.

Methods: We explored immune-related pathways that might be affected by counting positively associated genes in macrophages. Molecular typing was also constructed by mining macrophage-associated genes with prognostic value through COX regression and other analyses. RiskScore prognostic models were constructed using lasso regression and stepwise multifactorial regression analysis. The differences on clinical characteristics among three subtypes (C1, C2, and C3) and RiskScore subtypes were analyzed in TCGA dataset. Immunological algorithms such as TIMER, ssGSEA, MCP-Counter, ESTIMATE, and TIDE were used to calculate the level of difference in immune infiltration between the different subtypes. The TCGA mutation dataset processed by mutect2 was used to demonstrate the frequency of mutations between different molecular subtypes. Finally, nomograms, calibration curves, and decision curves were created to assess the predictive accuracy and reliability of the model.

Results: The C1 subtype demonstrated the best prognostic outcome, accompanied by higher levels of immune infiltration and lower mutation frequency, while the majority of patients in the C1 subtype were women under 65 years of age. Myeloid-derived suppressor cell (MDSC) scores were higher in the C3 subtype, suggesting a more severe immune escape, which may have contributed to the tumor evading the immune system resulting in a poorer prognosis for patients. In addition, our RiskScore prognostic model had good predictive accuracy and reliability.

Conclusion: This paper provides a study of macrophage-related pathways, immunosuppression, and their mechanisms of action in lung cancer, along with targets for future treatment to guide the optimal treatment of lung cancer.

Classification of stomach adenocarcinoma based on fatty acid metabolism-related genes frofiling

Background: Fatty acid metabolism (FAM)-related genes play a key role in the development of stomach adenocarcinoma (STAD). Although immunotherapy has led to a paradigm shift in STAD treatment, the overall response rate of immunotherapy for STAD is low due to heterogeneity of the tumor immune microenvironment (TIME). How FAM-related genes affect TIME in STAD remains unclear.

Methods: The univariate Cox regression analysis was performed to screen prognostic FAM-related genes using transcriptomic profiles of the Cancer Genome Atlas (TCGA)-STAD cohort. Next, the consensus clustering analysis was performed to divide the STAD cohort into two groups based on the 13 identified prognostic genes. Then, gene set enrichment analysis (GSEA) was carried out to identify enriched pathways in the two groups. Furthermore, we developed a prognostic signature model based on 7 selected prognostic genes, which was validated to be capable in predicting the overall survival (OS) of STAD patients using the univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analyses. Finally, the “Estimation of STromal and Immune cells in MAlignant Tumours using Expression data” (ESTIMATE) algorithm was used to evaluate the stromal, immune, and ESTIMATE scores, and tumor purity of each STAD sample.

Results: A total of 13 FAM-related genes were identified to be significantly associated with OS in STAD patients. Two molecular subtypes, which we named Group 1 and Group 2, were identified based on these FAM-related prognostic genes using the consensus clustering analysis. We showed that Group 2 was significantly correlated with poor prognosis and displayed higher programmed cell death ligand 1 (PD-L1) expressions and distinct immune cell infiltration patterns. Furthermore, using GSEA, we showed that apoptosis and HCM signaling pathways were significantly enriched in Group 2. We constructed a prognostic signature model using 7 selected FAM-related prognostic genes, which was proven to be effective for prediction of STAD (HR = 1.717, 95% CI = 1.105–1.240, p < 0.001). After classifying the patients into the high- and low-risk groups based on our model, we found that patients in the high-risk group tend to have more advanced T stages and higher tumor grades, as well as higher immune scores. We also found that the risk scores were positively correlated with the infiltration of certain immune cells, including resting dendritic cells (DCs), and M2 macrophages. We also demonstrated that elevated expression of gamma-glutamyltransferase 5 (GGT5) is significantly associated with worse OS and disease-free survival (DFS), more advanced T stage and higher tumor grade, and increased immune cell infiltration, suggesting that STAD patients with high GGT5 expression in the tumor tissues might have a better response to immunotherapy.

Conclusion: FAM-related genes play critical roles in STAD prognosis by shaping the TIME. These genes can regulate the infiltration of various immune cells and thus are potential therapeutic targets worthy of further investigation. Furthermore, GGT5 was a promising marker for predicting immunotherapeutic response in STAD patients.