Investigation of lipid metabolism dysregulation and the effects on immune microenvironments in pan-cancer using multiple omics data.

BACKGROUND: Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. Increasing evidences suggest that the alterations in tumor metabolism, including metabolite abundance and accumulation of metabolic products, lead to local immunosuppression in the tumor microenvironment. An integrated analysis of lipid metabolism in cancers from different tissues using multiple omics data may provide novel insight into the understanding of tumorigenesis and progression. RESULTS: Through systematic analysis of the multiple omics data from TCGA, we found that the most-widely altered lipid metabolism pathways in pan-cancer are fatty acid metabolism, arachidonic acid metabolism, cholesterol metabolism and PPAR signaling. Gene expression profiles of fatty acid metabolism show commonalities across pan-cancer, while the alteration in cholesterol metabolism and arachidonic acid metabolism differ with tissue origin, suggesting tissue specific lipid metabolism features in different tumor types. An integrated analysis of gene expression, DNA methylation and mutations revealed factors that regulate gene expression, including the differentially methylated sites and mutations of the lipid genes, as well as mutation and differential expression of the up-stream transcription factors for the lipid metabolism pathways. Correlation analysis of the proportion of immune cells in the tumor microenvironment and the expression of lipid metabolism genes revealed immune-related differentially expressed lipid metabolic genes, indicating the potential crosstalk between lipid metabolism and immune response. Genes related to lipid metabolism and immune response that are associated with poor prognosis were discovered including HMGCS2, GPX2 and CD36, which may provide clues for tumor biomarkers or therapeutic targets. CONCLUSIONS: Our study provides an integrated analysis of lipid metabolism in pan-cancer, highlights the perturbation of key metabolism processes in tumorigenesis and clarificates the regulation mechanism of abnormal lipid metabolism and effects of lipid metabolism on tumor immune microenvironment. This study also provides new clues for biomarkers or therapeutic targets of lipid metabolism in tumors

Multi-Omics Analysis of NCI-60 Cell Line Data Reveals Novel Metabolic Processes Linked with Resistance to Alkylating Anti-Cancer Agents

This study aimed to elucidate the molecular determinants influencing the response of cancer cells to alkylating agents, a major class of chemotherapeutic drugs used in cancer treatment. The study utilized data from the National Cancer Institute (NCI)-60 cell line screening program and employed a comprehensive multi-omics approach integrating transcriptomic, proteomic, metabolomic, and SNP data. Through integrated pathway analysis, the study identified key metabolic pathways, such as cysteine and methionine metabolism, starch and sucrose metabolism, pyrimidine metabolism, and purine metabolism, that differentiate drug-sensitive and drug-resistant cancer cells. The analysis also revealed potential druggable targets within these pathways. Furthermore, copy number variant (CNV) analysis, derived from SNP data, between sensitive and resistant cells identified notable differences in genes associated with metabolic changes (WWOX, CNTN5, DDAH1, PGR), protein trafficking (ARL17B, VAT1L), and miRNAs (MIR1302-2, MIR3163, MIR1244-3, MIR1302-9). The findings of this study provide a holistic view of the molecular landscape and dysregulated pathways underlying the response of cancer cells to alkylating agents. The insights gained from this research can contribute to the development of more effective therapeutic strategies and personalized treatment approaches, ultimately improving patient outcomes in cancer treatment

Disturbed Plasma Lipidomic Profiles in Females with Diffuse Large B-Cell Lymphoma: A Pilot Study

Lipidome dysregulation is a hallmark of cancer and inflammation. The global plasma lipidome and sub-lipidome of inflammatory pathways have not been reported in diffuse large B-cell lymphoma (DLBCL). In a pilot study of plasma lipid variation in female DLBCL patients and BMI-matched disease-free controls, we performed targeted lipidomics using LC-MRM to quantify lipid mediators of inflammation and immunity, and those known or hypothesised to be involved in cancer progression: sphingolipids, resolvin D1, arachidonic acid (AA)-derived oxylipins, such as hydroxyeicosatetraenoic acids (HETEs) and dihydroxyeicosatrienoic acids, along with their membrane structural precursors. We report on the role of the eicosanoids in the separation of DLBCL from controls, along with lysophosphatidylinositol LPI 20:4, implying notable changes in lipid metabolic and/or signalling pathways, particularly pertaining to AA lipoxygenase pathway and glycerophospholipid remodelling in the cell membrane. We suggest here the set of S1P, SM 36:1, SM 34:1 and PI 34:1 as DLBCL lipid signatures which could serve as a basis for the prospective validation in larger DLBCL cohorts. Additionally, untargeted lipidomics indicates a substantial change in the overall lipid metabolism in DLBCL. The plasma lipid profiling of DLBCL patients helps to better understand the specific lipid dysregulations and pathways in this cancer

Impact of energy metabolism, paraoxonase-1, and inflammation on cancer patient's prognosis and response to treatment

El càncer és una preocupació sanitària mundial i un gran repte per augmentar l'esperança i la qualitat de vida dels pacients. Considerant que el metabolisme relacionat amb el balanç energètic, l'estrès oxidatiu i la inflamació són processos clau en el desenvolupament i la progressió del càncer, aquesta tesi inclou diferents cohorts humanes de pacients amb càncer de pulmó, cap i coll, recte i mama 1) per investigar els canvis metabòlics circulatoris en pacients amb càncer i els efectes dels tractaments oncològics produïts en aquestes alteracions metabòliques, i 2) per trobar potencials marcadors biològics relacionats amb el pronòstic i la resposta al tractament dels pacients. Els nostres resultats van mostrar que els pacients amb diferents tipus de càncer presentaven diverses alteracions circulatòries en les concentracions dels metabòlits involucrats en el balanç energètic, en les variables associades amb la PON1 i en els paràmetres inflamatoris en comparació amb els controls. Algunes d'aquestes alteracions es van relacionar amb les característiques clinicopatològiques dels pacients. L'administració dels tractaments oncològics va produir una normalització parcial dels nivells circulatoris dels metabòlits associats al balanç energètic, mostrant concentracions similars a la dels controls. A més, els tractaments es van associar a un augment de la concentració de PON1 i a una major disminució de les activitats de PON1 i dels marcadors inflamatoris en comparació amb els valors previs al tractament. Les anàlisis multivariants i els algoritmes d'aprenentatge automàtic van proporcionar informació rellevant sobre les alteracions metabòliques del càncer, així com possibles marcadors biològics per a l'estratificació dels pacients amb càncer segons el seu pronòstic i resposta al tractament. Tanmateix, es necessitaran més estudis per confirmar i validar aquestes troballes.El cáncer es una preocupación sanitaria mundial y un gran reto para aumentar la esperanza y la calidad de vida. Considerando que el metabolismo relacionado con el balance energético, el estrés oxidativo y la inflamación son procesos clave en el desarrollo y la progresión del cáncer, esta tesis incluye diferentes cohortes humanas de pacientes con cáncer de pulmón, cabeza y cuello, recto y mama 1) para investigar los cambios metabólicos circulatorios en pacientes con cáncer y los efectos de los tratamientos oncológicos producidos en estas alteraciones metabólicas, y 2) para encontrar potenciales marcadores biológicos relacionados con el pronóstico y la respuesta al tratamiento de los pacientes. Nuestros resultados mostraron que los pacientes con diferentes tipos de cáncer presentaban diversas alteraciones circulatorias en las concentraciones de los metabólitos asociados al balance energético, en las variables asociadas con la PON1 y en los parámetros inflamatorios en comparación con los controles. Algunas de estas alteraciones estaban relacionadas con las características clinicopatológicas de los pacientes. La administración de los tratamientos oncológicos produjo una normalización parcial de los niveles circulatorios de los metabolitos asociados al balance energético, mostrando concentraciones similares a la de los controles. Además, los tratamientos se asociaron a un aumento de la concentración de PON1 y a una mayor disminución de las actividades de PON1 y de los marcadores inflamatorios en comparación con los valores previos al tratamiento. Los análisis multivariantes y los algoritmos de aprendizaje automático proporcionaron información relevante sobre las alteraciones metabólicas del cáncer, así como posibles marcadores biológicos para la estratificación de los pacientes con cáncer según su pronóstico y respuesta al tratamiento. Sin embargo, se necesitarán más estudios para confirmar y validar los estos hallazgos.Cancer is a worldwide health concern and a big challenge to increase the expectancy and quality of life. Considering that energy balance-related metabolism, OXS, and inflammation are key processes in cancer development and progression, the present thesis includes different human cohorts of patients with lung, head and neck, rectal, and breast cancer 1) to investigate the circulatory metabolic changes in cancer patients and the effects of oncological treatments produced on these metabolic alterations, and 2) to find potential biomarkers related to prognosis and response to treatment. Our results showed that patients with different types of cancer had several circulatory alterations in the concentrations of the energy-balance metabolites, PON1-related variables, and inflammatory parameters compared to controls. Some of these alterations were related to the clinicopathological characteristics of the patients. The administration of cancer treatments leads to a partial normalization of the circulatory levels of energy-balance metabolites showing similar metabolite concentrations from controls. Moreover, treatments were associated with an increase of PON1 concentration, and a further decrease of PON1 activities and inflammatory markers compared with pre-treatment values. Multivariate analyses and machine learning algorithms have provided relevant information about cancer metabolic alterations, as well as potential targets for cancer patients stratification according to their prognosis and response to treatment. However, further studies will be needed to confirm and validate the current findings

Epitranscriptomic regulation in breast cancer and PCB-induced liver disease.

Post-transcriptional RNA modifications including N6-methyladenosine (m6A) regulate mRNA stability, splicing, and translation. My research examined m6A in two disease models: breast cancer (BCa) and non-alcoholic fatty liver disease (NAFLD). Acquired resistance to endocrine therapies (ET) develops in approximately 20% of BCa patients with estrogen receptor α positive (ER+) tumors following treatment. The mechanisms by which tumor cells evade ET are not completely understood. Using a cell line model, we investigated the role of an m6A reader protein, HNRNPA2B1 (A2B1) that is upregulated in ET-resistant ER+ BCa cells. Stable overexpression of A2B1 in ET-sensitive MCF-7 cells (MCF-7-A2B1), results in ET resistance, whereas knockdown of A2B1 in ET-resistant cells restored ET-sensitivity. microRNAs (miRNAs) downregulated by transient overexpression of A2B1 were identified to target two key enzymes (PSAT1 and PHGDH) in the serine biosynthetic pathway (SSP) which is upregulated in ET-resistant BCa cells and in tumors from patients with ET-resistant disease. Using luciferase assays, PSAT1 and PHGDH were validated as bona fide targets of miRNAs downregulated by A2B1 (miR-145-5p and miR-424-5p targeting PSAT1, miR-34b-5p and miR-876-5p targeting PHGDH). Exogenous overexpression of the validated miRNAs decreased endogenous PSAT1 and PHGDH in ET-resistant BCa cells, resulting in increased sensitivity to ET in vitro. In the second model, alterations in the m6A epitranscriptome were identified in the livers of male C57Bl/6Jmice after a single, oral exposure to polychlorinated biphenyls (PCB), a class of persistent organic pollutants, in combination with 12 weeks on a high fat diet (HFD). Our results demonstrated that exposure to PCBs in combination with a HFD resulted in major changes to the mRNA and miRNA transcriptomes, and m6A epitranscriptome. Pathway analysis of the genes in which m6A peaks were altered identified pathways involved in the progression from steatosis to steatohepatitis in NAFLD. PCB exposures also resulted in changes to alternative splicing (AS) mechanisms and events, suggesting that PCB-induced m6A changes contribute to altered isoforms expression in NAFLD. Taken together, the results in this dissertation demonstrate the significant role of altered m6A in two common human diseases

Molecular and Metabolic Regulation of Immunosuppression in Metastatic Pancreatic Ductal Adenocarcinoma

Immunosuppression is a hallmark of pancreatic ductal adenocarcinoma (PDAC), contributing to early metastasis and poor patient survival. Compared to the localized tumors, current standard-of-care therapies have failed to improve the survival of patients with metastatic PDAC, that necessecitates exploration of novel therapeutic approaches. While immunotherapies such as immune checkpoint blockade (ICB) and therapeutic vaccines have emerged as promising treatment modalities in certain cancers, limited responses have been achieved in PDAC. Therefore, specific mechanisms regulating the poor response to immunotherapy must be explored. The immunosuppressive microenvironment driven by oncogenic mutations, tumor secretome, non-coding RNAs, and tumor microbiome persists throughout PDAC progression, allowing neoplastic cells to grow locally and metastasize distantly. The metastatic cells escaping the host immune surveillance are unique in molecular, immunological, and metabolic characteristics. Following chemokine and exosomal guidance, these cells metastasize to the organ-specific pre-metastatic niches (PMNs) constituted by local resident cells, stromal fibroblasts, and suppressive immune cells, such as the metastasis-associated macrophages, neutrophils, and myeloid-derived suppressor cells. The metastatic immune microenvironment differs from primary tumors in stromal and immune cell composition, functionality, and metabolism. Thus far, multiple molecular and metabolic pathways, distinct from primary tumors, have been identified that dampen immune effector functions, confounding the immunotherapy response in metastatic PDAC. This review describes major immunoregulatory pathways that contribute to the metastatic progression and limit immunotherapy outcomes in PDAC. Overall, we highlight the therapeutic vulnerabilities attributable to immunosuppressive factors and discuss whether targeting these molecular and immunological hot spots could improve the outcomes of PDAC immunotherapies

Discovery and Interpretation of Subspace Structures in Omics Data by Low-Rank Representation

Indiana University-Purdue University Indianapolis (IUPUI)Biological functions in cells are highly complicated and heterogenous, and can be reflected by omics data, such as gene expression levels. Detecting subspace structures in omics data and understanding the diversity of the biological processes is essential to the full comprehension of biological mechanisms and complicated biological systems. In this thesis, we are developing novel statistical learning approaches to reveal the subspace structures in omics data. Specifically, we focus on three types of subspace structures: low-rank subspace, sparse subspace and covariates explainable subspace. For low-rank subspace, we developed a semi-supervised model SSMD to detect cell type specific low-rank structures and predict their relative proportions across different tissue samples. SSMD is the first computational tool that utilizes semi-supervised identification of cell types and their marker genes specific to each mouse tissue transcriptomics data, for better understanding of the disease microenvironment and downstream disease mechanism. For sparsity-driven sparse subspace, we proposed a novel positive and unlabeled learning model, namely PLUS, that could identify cancer metastasis related genes, predict cancer metastasis status and specifically address the under-diagnosis issue in studying metastasis potential. We found PLUS predicted metastasis potential at diagnosis have significantly strong association with patient’s progression-free survival in their follow-up data. Lastly, to discover the covariates explainable subspace, we proposed an analytical pipeline based on covariance regression, namely, scCovReg. We utilized scCovReg to detect the pathway level second-order variations using scRNA-Seq data in a statistically powerful manner, and to associate the second-order variations with important subject-level characteristics, such as disease status. In conclusion, we presented a set of state-of-the-art computational solutions for identifying sparse subspaces in omics data, which promise to provide insights into the mechanism in complex diseases