Amino Acid Metabolism-related Genes Research Articles

High protein-low carbohydrate diet can improve hypoxia tolerance of triploid rainbow trout (Oncorhynchus mykiss): Effects on physiological and metabolic responses to acute hypoxia

Triploid rainbow trout is a hypoxia-sensitive fish species, and is susceptible to death in summer with high water temperature induced acute hypoxia. The study was conducted to evaluate the effect of high protein diet and high carbohydrate diet on hypoxic tolerance of triploid rainbow trout. Two isolipidic and isoenergetic diets with low protein-high carbohydrate level (LPHC) and high protein-low carbohydrate level (HPLC) were specifically formulated. After the feeding trial with 80 days, two group fish were treated with acute hypoxic challenge. Plasma biochemical parameters, hepatic anti-stress, antioxidant and immune related genes expression, and hepatic glucose, lipid, protein and amino acid metabolism related genes expression were then analyzed. The results showed that compared with HPLC diet, LPHC diet could result in excessive plasma levels of alanine aminotransferase (ALT; 36.3 VS 14.0 U/L), aspartate aminotransferase (AST; 978 VS 366 U/L), alkaline phosphatase (ALP; 374 VS 204 U/L), creatine phosphokinase (CPK; 7538 VS 4937 U/L) and lactate dehydrogenase (LDH; 940 VS 451 U/L). In addition, HPLC diet could remodel metabolism of glucose, lipid and protein based on the mRNA expression levels of the genes regulating glycometabolism, lipometabolism, and protein and amino acids metabolism. HPLC diet also enhanced the mRNA expression levels of the genes regulating anti-stress (HSP70, HIF-1a and HIF-2a), antioxidant (Nrf2, SOD, CAT, GPx and HO-1) and immune (IFN-γ, IL-1β, TNF-α and IL-8). In total, LPHC diet could aggravate hypoxic stress of triploid rainbow trout, while HPLC diet could improve fish hypoxic tolerance.

Effects of dietary methionine and taurine interaction on growth performance, the profiles of amino acids, and protein metabolism of Chinese mitten crab, Eriocheir sinensis

The purpose of this study is to explore the interaction between dietary methionine and taurine levels on growth performance, the profiles of amino acids, protein metabolism, and how taurine spares methionine of Chinese mitten crab (Eriocheir sinensis). The crabs were divided into six groups which were fed six diets formulated with two Tau levels (0 and 0.4%) and three Met levels (0.6%, 0.9% and 1.2%). The results showed that the weight gain rate and specific growth rate were the highest in the crabs fed 0.4% Tau and 0.6% Met. Crabs fed 1.2% Met had the highest survival rate and lowest feed conversion ratio. Furthermore, the hepatopancreas and muscle amino acid content were significantly influenced by the interactive effects of Met and Tau (P < 0.05). Moreover, interactive effects of dietary methionine and taurine were significantly observed in the expressions of amino acid metabolism-related genes (P < 0.05). Additionally, crabs fed 1.2% Met and 0% Tau had the highest activities of cysteine decarboxylase (CSD) and cysteine dioxygenase (CDO). In conclusion, the addition of appropriate amount of taurine to the diet can reduce the requirement of methionine for Chinese mitten crab by regulating the activities of CSD and CDO. At the same time, the addition of methionine and taurine could affect the amino acids metabolism. Therefore, the diet formula of Chinese mitten crab needs to consider the metabolism of sulfur-containing amino acids.

Constructing a Novel Amino Acid Metabolism Signature: A New Perspective on Pheochromocytoma Diagnosis, Immune Landscape, and Immunotherapy.

Pheochromocytoma/paraganglioma (PGPG) is a rare neuroendocrine tumor. Amino acid metabolism is crucial for energy production, redox balance, and metabolic pathways in tumor cell proliferation. This study aimed to build a risk model using amino acid metabolism-related genes, enhancing PGPG diagnosis and treatment decisions. We analyzed RNA-sequencing data from the PCPG cohort in the GEO dataset as our training set and validated our findings using the TCGA dataset and an additional clinical cohort. WGCNA and LASSO were utilized to identify hub genes and develop risk prediction models. The single-sample gene set enrichment analysis, MCPCOUNTER, and ESTIMATE algorithm calculated the relationship between amino acid metabolism and immune cell infiltration in PCPG. The TIDE algorithm predicted the immunotherapy efficacy for PCPG patients. The analysis identified 292 genes with differential expression, which are involved in amino acid metabolism and immune pathways. Six genes (DDC, SYT11, GCLM, PSMB7, TYRO3, AGMAT) were identified as crucial for the risk prediction model. Patients with a high-risk profile demonstrated reduced immune infiltration but potentially higher benefits from immunotherapy. Notably, DDC and SYT11 showed strong diagnostic and prognostic potential. Validation through quantitative Real-Time Polymerase Chain Reaction and immunohistochemistry confirmed their differential expression, underscoring their significance in PCPG diagnosis and in predicting immunotherapy response. This study's integration of amino acid metabolism-related genes into a risk prediction model offers critical clinical insights for PCPG risk stratification, potential immunotherapy responses, drug development, and treatment planning, marking a significant step forward in the management of this complex condition.

The effect of fructose exposure on amino acid metabolism among Chinese community residents and its possible multi-omics mechanisms

The consumption of fructose has increased dramaticly during the last few decades, inducing a great increase in the risk of intrahepatic lipid accumulation, hypertriglyceridemia, hyperuricemia and cancer. However, the underlying mechanism has not yet been fully elucidated. Amino acid metabolism may play an important role in the process of the diseases caused by fructose, but there is still a lack of corresponding evidence. In present study, we provide an evidence of how fructose affects amino acids metabolism in 1895 ordinary residents in Chinese community using UPLC-QqQMS based amino acid targeted metabolomics and the underlying mechanism of fructose exposure how interferes with amino acid metabolism related genes and acetylated modification of proteome in the liver of rats model. We found people with high fructose exposure had higher levels of Asa, EtN, Asp, and Glu, and lower levels of 1MHis, PEtN, Arg, Gln, GABA, Aad, Hyl and Cys. The further mechanism study displayed amino acid metabolic genes of Aspa, Cndp1, Dbt, Dmgdh, and toxic metabolites such as N-acetylethanolamines accumulation, interference of urea cycle, as well as acetylated modification of key enzymes in glutamine metabolic network and glutamine derived NEAAs synthesis pathway in liver may play important roles in fructose caused reprogramming in amino acid metabolism. This research provides novel insights of the mechanism of amino acid metabolic disorder caused by fructose and supplies new targets for clinical therapy.

A Prognostic Model of Head and Neck Cancer Based on Amino Acid Metabolism-Related Signature and Its Implication for Immunosuppressive Microenvironment.

Amino acid metabolism has been implicated in tumorigenesis and tumor progression. Alterations in intracellular and extracellular metabolites associated with metabolic reprogramming in cancer have profound effects on gene expression, cell differentiation, and tumor immune microenvironment. However, the prognostic significance of amino acid metabolism in head and neck cancer remains to be further investigated. In this study, we identified 98 differentially expressed genes related to amino acid metabolism in head and neck cancer in The Cancer Genome Atlas. Using batch univariate Cox regression and Lasso regression, we extracted nine amino acid metabolism-related genes. Based on that, we developed the amino acid metabolism index. The prognostic value of this index was validated in two Gene Expression Omnibus cohorts. The results show that this model can help predict tumor recurrence and prognosis. The infiltration of immune cells in the tumor microenvironment was analyzed, and it was discovered that the high index is associated with an immunosuppressive microenvironment. In addition, this study demonstrated the impact of the amino acid metabolism index on clinical indicators, survival of patients with head and neck cancer, and the prediction of treatment response to immune checkpoint inhibitors. We conducted several cell experiments and demonstrated that epigenetic drugs could affect the index and enhance tumor immunity. In conclusion, our study demonstrates that the index not only has important prognostic value in head and neck cancer patients but also facilitates patient stratification for immunotherapy.

Comparative study of the digestion and metabolism related genes' expression changes during the postnatal food change in different dietary mammals.

The changes in the expression of genes related to digestion and metabolism may be various in different dietary mammals from juvenile to adult, especially, the giant panda (Ailuropoda melanoleuca) and red panda (Ailurus fulgens), which were once carnivores but have shifted to being specialized bamboo eaters, are unique features of their changes are more unclear. To elucidate the changing patterns of gene expression related to digestion and metabolism from juvenile to adult in different dietary mammals, we performed transcriptome analysis of the liver or pancreas in giant and red pandas, herbivorous rabbits (Oryctolagus cuniculus) and macaques (Macaca mulatta), carnivorous ferrets (Mustela putorius furo), and omnivorous mice (Mus musculus) from juvenile to adult. During the transition from juvenile to adulthood, giant and red pandas, as well as rabbits and macaques, show significant upregulation of key genes for carbohydrate metabolism, such as starch hydrolysis and sucrose metabolism, and unsaturated fatty acid metabolism, such as linoleic acid, while there is no significant difference in the expression of key genes for fatty acid β-oxidation. A large number of amino acid metabolism related genes were upregulated in adult rabbits and macaques compared to juveniles. While adult giant and red pandas mainly showed upregulation of key genes for arginine synthesis and downregulation of key genes for arginine and lysine degradation. In adult stages, mouse had significantly higher expression patterns in key genes for starch hydrolysis and sucrose metabolism, as well as lipid and protein metabolism. In contrast to general expectations, genes related to lipid, amino acid and protein metabolism were significantly higher expressed in adult group of ferrets, which may be related to their high metabolic levels. Our study elucidates the pattern of changes in the expression of genes related to digestion and metabolism from juvenile to adult in different dietary mammals, with giant and red pandas showing adaptations associated with specific nutritional limitations of bamboo.

Prognostic value of amino acid metabolism-related gene expression in invasive breast carcinoma.

An increasing number of studies indicated that metabolic reprogramming of amino acid metabolism may either promote or inhibit tumor progression. The purpose of this study was to investigate the ability of a gene risk signature associated with amino acid metabolism to predict the prognosis and immune characteristics of invasive breast carcinoma. LASSO Cox regression analysis was performed to construct and validate the prognostic risk signature based on the expression of 9 amino acid metabolism-related genes. The predictive value of the signature, immune characteristics, and chemotherapeutic drugs was also predicted. Finally, 9 significant genes were examined in MDA-MB-231 and MCF-7 cells, and the predicted chemotherapeutic drugs were also verified. The prognosis of the low-risk group was better than that of the high-risk group. The areas under the curve (AUCs) at 1, 2, and 3 years were 0.852, 0.790, and 0.736, respectively. In addition, the GSEA results for KEGG and GO revealed that samples with a high-risk score exhibited a variety of highly malignant manifestations. The high-risk group was characterized by an increased number of M2 macrophages, a high level of tumor purity, low levels of APC co-stimulation, cytolytic activity, HLA, para-inflammation, and type I IFN response. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) confirmed that MDA-MB-231 and MCF-7 cells express 9 amino acid metabolism-related genes differently. In addition, cell experiments were conducted to examine the effect of cephaeline-induced on cell viability, migration ability, and protein expression of the PI3K/AKT signaling pathway and HIF-1α. We established a risk signature based on 9 amino acid metabolism-related genes for invasive breast carcinoma. Further analyses revealed that this risk signature is superior to other clinical indexes in survival prediction and that the subgroups identified by the risk signature exhibit distinct immune characteristics. Cephaeline was determined to be a superior option for patients in high-risk groups.

Abstract 2200: Transcriptomic profiling identifies an amino acid metabolism-related prognostic gene signature in lung adenocarcinoma patients

Abstract Lung cancer is by far the most common cancer-related fatality. In addition to the existing standard treatment methods, it is urgent to develop new therapy methods to improve the survival rate of patients. Amino acid metabolism plays a crucial role in tumor development. Cancer cells rely on amino acids for replication, energy source, redox homeostasis, and release metabolites that recruit and stimulate immunosuppressive cells. This study constructed a novel prognostic signature model of lung adenocarcinoma (LUAD) composed of amino acid metabolism-related genes and assessed immune cell infiltration characteristics and predictive value for patient therapy. We screened differentially expressed genes involved in amino acid metabolic pathways in LUAD tumor tissues versus normal tissues from RNA-seq data of The Cancer Genome Atlas. Then we performed a multivariate cox analysis of 9 genes significantly associated with patients’ overall survival, which established a new prognostic signature. Dividing the patients into high- and low-risk groups using a cutoff value of -0.36, the outcome showed that the risk score of this model could predict survival of LUAD patients with relatively high accuracy (ROC-AUC (max)=0.73). Subsequently, we utilized the ssGSEA method to quantify the infiltration of immune cells. We found that several immune cell subpopulations were significantly altered abundance between the high- and low-risk patients. Immune cell subpopulations, such as mast cell, plasmacytoid dendritic cell, and central memory CD4 T cell, were significantly abundant in the low-risk patients. After ESTIMATE algorithm evaluation, a method for estimating the proportion of stromal and immune cells in tumor tissues based on gene expression profiles, we observed that immune and stroma scores were markedly upregulated in low-risk patients, suggesting that the immune and stromal activity were increased. The key molecules of this signature exhibited a significant correlation with the expression of immune checkpoint molecules. Moreover, multivariate cox regression model analysis was performed with patients’ age, gender, clinical stage, T stage, M stage, N stage and risk score. The results indicated that the risk score signature was a significant and independent prognostic biomarker for the prognostic prevision of LUAD patients.Finally, the risk score based on the amino acid metabolism-related signature was successfully validated by the data of LUAD patients in the GEO dataset GES30219.These findings suggest that the prognostic signature based on the expression of genes involved in amino acid metabolism may be useful and effective. We further characterized one of the 9 genes involved in tryptophan metabolism for its biological implications in tumor progression and construction of immuno-suppressive tumor microenvironment by experimental procedures with human LUAD cell lines. Citation Format: Huihui Xiang, Rika Kasajima, Hiroyuki Ito, Tomoyuki Yokose, Takashi Oshima, Tetsuro Sasada, Yohei Miyagi. Transcriptomic profiling identifies an amino acid metabolism-related prognostic gene signature in lung adenocarcinoma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2200.

A prognostic risk model, tumor immune environment modulation, and drug prediction of ferroptosis and amino acid metabolism-related genes in hepatocellular carcinoma.

The prognosis of hepatocellular carcinoma (HCC) is challenging due to its heterogeneity. Ferroptosis and amino acid metabolism have been shown to be closely related to HCC. We obtained HCC-related expression data from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. We then crossed differentially expressed genes (DEGs), amino acid metabolism genes, and ferroptosis-related genes (FRGs) to obtain amino acid metabolism-ferroptosis-related differentially expressed genes (AAM-FR DEGs). Moreover, we developed a prognostic model using Cox analysis, followed by a correlation analysis of risk scores with clinical characteristics. We also performed an immune microenvironment analysis and drug sensitivity analysis. Finally, the expression levels of model genes were verified by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemical assays. We found that the 18 AAM-FR DEGs were mainly enriched to the alpha-amino acid metabolic process and amino acid biosynthesis pathways. Cox analysis identified CBS, GPT2, SUV39H1, and TXNRD1 as prognostic biomarkers for the risk model construction. Our results showed that the risk scores differed between pathology stage, pathology T stage, and HBV, and the number of HCC patients in the two groups. In addition, the expression of PD-L1 and CTLA-4 was high in the high-risk group, and the half-maximal inhibitory concentration (IC50) of sorafenib also differed between the two groups. Finally, the experimental validation demonstrated that the expression of biomarkers was consistent with the study analysis. Therefore, in this study, we constructed and validated a prognostic model (CBS, GPT2, SUV39H1, and TXNRD1) related to ferroptosis and amino acid metabolism and examined their prognostic value for HCC.

Prognostic Significance of Amino Acid Metabolism-Related Genes in Prostate Cancer Retrieved by Machine Learning.

Prostate cancer is among the leading cancers according to both incidence and mortality. Due to the high molecular, morphological and clinical heterogeneity, the course of prostate cancer ranges from slow growth that usually does not require immediate therapeutic intervention to aggressive and fatal disease that spreads quickly. However, currently available biomarkers cannot precisely predict the course of a disease, and novel strategies are needed to guide prostate cancer management. Amino acids serve numerous roles in cancers, among which are energy production, building block reservoirs, maintenance of redox homeostasis, epigenetic regulation, immune system modulation and resistance to therapy. In this article, by using The Cancer Genome Atlas (TCGA) data, we found that the expression of amino acid metabolism-related genes is highly aberrant in prostate cancer, which holds potential to be exploited in biomarker design or in treatment strategies. This change in expression is especially evident for catabolism genes and transporters from the solute carrier family. Furthermore, by using recursive partitioning, we confirmed that the Gleason score is strongly prognostic for progression-free survival. However, the expression of the genes SERINC3 (phosphatidylserine and sphingolipids generation) and CSAD (hypotaurine generation) can refine prognosis for high and low Gleason scores, respectively. Therefore, our results hold potential for novel prostate cancer progression biomarkers.

Prediction of prognosis and immunotherapy response of amino acid metabolism genes in acute myeloid leukemia.

Amino acid (AA) metabolism plays a crucial role in cancer. However, its role in acute myeloid leukemia (AML) is still unavailable. We screened out AA metabolic genes, which related to prognosis, and analyzed their correlation with tumor immune microenvironment in AML. We evaluated 472 amino acid metabolism-related genes in 132 AML patients. The predictive risk model was developed according to differentially expressed genes, univariate Cox and LASSO analyses. We validated the risk signature by survival analysis and independence tests. Single-sample gene set enrichment analysis (ssGSEA), tumor immune microenvironment (TME), tumor mutation burden (TMB), functional enrichment, and the IC50 of drugs were assessed to explore the correlations among the risk model, immunity, and drug sensitivity of AML. Six amino acid metabolism-related genes were confirmed to develop the risk model, including TRH, HNMT, TFEB, SDSL, SLC43A2, and SFXN3. The high-risk subgroup had an immune "hot" phenotype and was related to a poor prognosis. The high-risk group was also associated with more activity of immune cells, such as Tregs, had higher expression of some immune checkpoints, including PD1 and CTLA4, and might be more susceptible to immunotherapy. Xenobiotic metabolism, the reactive oxygen species (ROS) pathway, fatty acid metabolism, JAK/STAT3, and the inflammatory response were active in the high-risk subgroup. Furthermore, the high-risk subgroup was sensitive to sorafenib, selumetinib, and entospletinib. ssGSEA discovered that the processes of glutamine, arginine, tryptophan, cysteine, histidine, L-serine, isoleucine, threonine, tyrosine, and L-phenylalanine metabolism were more active in the high-risk subgroup. This study revealed that AA metabolism-related genes were correlated with the immune microenvironment of AML patients and could predict the prognosis and immunotherapy response of AML patients.

Interactive effects of dietary leucine and isoleucine affect amino acid profile and metabolism through AKT/TOR signaling pathways in blunt snout bream (Megalobrama amblycephala).

The purpose of this research is to explore the interaction between dietary leucine and isoleucine levels on whole-body composition, plasma and liver biochemical indexes, amino acids deposition in the liver, and amino acid metabolism of blunt snout bream (Megalobrama amblycephala). The test fish (average weight: 56.00 ± 0.55g) were fed one of six diets at random containing two leucine levels (1.70% and 2.50%) and three isoleucine levels (1.00%, 1.20%, and 1.40%) for 8weeks. The results showed that the final weight and weight gain rate were the highest in the fish fed low-level leucine and high-level isoleucine diets (P > 0.05). Furthermore, the crude lipid content was significantly adjusted by diets with diverse levels of leucine and isoleucine (P < 0.05). In addition, interactive effects of these two branched-chain amino acids (BCAAs) were found on plasma total protein, blood ammonia, and blood urea nitrogen of test fish (P < 0.05). Additionally, the liver amino acid profiles were significantly influenced by the interactive effects of the two BCAAs (P < 0.05). Moreover, interactive effects of dietary leucine and isoleucine were significantly observed in the expressions of amino acid metabolism-related genes (P < 0.05). These findings suggested that dietary leucine and isoleucine had interaction. Meanwhile, the interaction between them was more conducive to the growth and quality improvement of blunt snout bream when the dietary leucine level was 1.70% and isoleucine level was 1.40%.

Comprehensive analysis of the amino acid metabolism-related gene signature for prognosis, tumor immune microenvironment, and candidate drugs in hepatocellular carcinoma.

Metabolic rewiring satisfies increased nutritional demands and modulates many oncogenic processes in tumors. Amino acid metabolism is abnormal in many malignancies. Metabolic reprogramming of amino acids not only plays a crucial role in sustaining tumor cell proliferation but also influences the tumor immune microenvironment. Herein, the aim of our study was to elucidate the metabolic signature of amino acids in hepatocellular carcinoma (HCC). Transcriptome profiles of HCC were obtained from the TCGA and ICGC databases. Based on the expression of amino acid metabolism-related genes (AAMRGs), we clustered the HCC samples into two molecular subtypes using the non-negative matrix factorization algorithm. Then, we constructed the amino acid metabolism-related gene signature (AAMRGS) by Cox regression and LASSO regression. Afterward, the clinical significance of the AAMRGS was evaluated. Additionally, we comprehensively analyzed the differences in mutational profiles, immune cell infiltration, immune checkpoint expression, and drug sensitivity between different risk subgroups. Furthermore, we examined three key gene expressions in liver cancer cells by quantitative real-time PCR and conducted the CCK8 assay to evaluate the influence of two chemotherapy drugs on different liver cancer cells. A total of 81 differentially expressed AAMRGs were screened between the two molecular subtypes, and these AAMRGs were involved in regulating amino acid metabolism. The AAMRGS containing GLS, IYD, and NQO1 had a high value for prognosis prediction in HCC patients. Besides this, the two AAMRGS subgroups had different genetic mutation probabilities. More importantly, the immunosuppressive cells were more enriched in the AAMRGS-high group. The expression level of inhibitory immune checkpoints was also higher in patients with high AAMRGS scores. Additionally, the two AAMRGS subgroups showed different susceptibility to chemotherapeutic and targeted drugs. In vitro experiments showed that gemcitabine significantly reduced the proliferative capacity of SNU449 cells, and rapamycin remarkedly inhibited Huh7 proliferation. The five HCC cells displayed different mRNA expression levels of GLS, IYD, and NQO1. Our study explored the features of amino acid metabolism in HCC and identified the novel AAMRGS to predict the prognosis, immune microenvironment, and drug sensitivity of HCC patients. These findings might help to guide personalized treatment and improve the clinical outcomes of HCC.

Amino acid metabolism-based molecular classification of colon adenocarcinomavia in silico analysis.

Amino acid metabolism is closely related to the occurrence and development of colon adenocarcinoma (COAD). Studies on the relationship between COAD and the expression of amino acid metabolism are still rare. Based on in silico analysis, we used 358 amino acid metabolism-related genes (AAMRGs) to determine the amino acid metabolism characteristics and then classified COAD into two distinct subtypes, namely AA1 and AA2. Then we analyzed the clinical characteristics, somatic mutation landscape, transcriptome profile, metabolism signatures, immune infiltration, and therapy sensitivity of these two subtypes. The AA1 subtype had inferior overall survival and was characterized by lower amino acid metabolic activity, higher tumor mutation burden, and higher immune cell infiltration, while AA2 displayed higher metabolic activity and relatively better survival. Furthermore, the AA1 subtype was likely to benefit from irinotecan in chemotherapy and immune checkpoint blockade therapy including programmed cell death protein-1 (PD-1) and cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) immune checkpoint inhibitor but was resistant to targeted therapy cetuximab. The AA2 subtype showed higher sensitivity to 5-fluorouracil and oxaliplatin. To provide perspectives on cell-specific metabolism for further investigation, we explored metabolic activity in different cell types including lymphocytes, mast cells, myeloid cells stromal cells, and epithelial cells via colorectal cancer single-cell data. Additionally, to assist in clinical decision-making and prognosis prediction, a 60-AAMRG-based classifier was generated and validated in an independent cohort.

A novel amino acid metabolism-related gene risk signature for predicting prognosis in clear cell renal cell carcinoma.

BackgroundRenal cancer is one of the most lethal cancers because of its atypical symptoms and metastatic potential. The metabolism of amino acids and their derivatives is essential for cancer cell survival and proliferation. Thus, the construction of the amino acid metabolism-related risk signature might enhance the accuracy of the prognostic model and shed light on the treatments of renal cancers.MethodsRNA expression and clinical data were downloaded from Santa Cruz (UCSC) Xena, GEO, and ArrayExpress databases. The “DESeq2” package identified the differentially expressed genes. Univariate COX analysis selected prognostic genes related to the metabolism of amino acids. Patients were divided into two clusters using the “ConsensusClusterPlus” package, and the CIBERSORT, ESTIMATE methods were explored to assess the immune infiltrations. The LASSO regression analysis constructed a risk model which was evaluated the prediction accuracy in two independent cohorts. The genomic alterations and drug sensitivity of 18-LASSO-genes were assessed. The differentially expressed genes between two clusters were used to perform functional enrichment analysis and weighted gene co-expression network analysis (WGCNA). Furthermore, external validation of TMEM72 expression was conducted in the FUSCC cohort containing 33 ccRCC patients.ResultsThe amino acid metabolism-related genes had significant correlations with prognosis. The patients in Cluster A demonstrated better survival, lower Treg cell proportion, higher ESTIMATE scores, and higher cuproptosis-related gene expressions. Amino acid metabolism-related genes with prognostic values were used to construct a risk model and patients in the low risk group were associated with improved outcomes. The Area Under Curve of the risk model was 0.801, 0.777, and 0.767 at the first, second, and third year respectively. The external validation cohort confirmed the stable prognostic value of the risk model. WGCNA identified four gene modules correlated with immune cell infiltrations and cuproptosis. We found that TMEM72 was downregulated in tumors by using TCGA, GEO datasets (p<0.001) and the FUSCC cohort (p=0.002).ConclusionOur study firstly constructed an 18 amino acid metabolism related signature to predict the prognosis in clear cell renal cell carcinoma. We also identified four potential gene modules potentially correlated with cuproptosis and identified TMEM72 downregulation in ccRCC which deserved further studies.

A multifaceted and feasible prognostic model of amino acid metabolism-related genes in the immune response and tumor microenvironment of head and neck squamous cell carcinomas.

We investigated the role of amino acid metabolism (AAM) in head and neck squamous cell carcinoma (HNSCC) tissues to explore its prognostic value and potential therapeutic strategies. A risk score based on four AAM-related genes (AMG) was constructed that could predict the prognosis of HNSCC. These four genes were up-regulated in HNSCC tissues and might act as oncogenes. Internal validation in The Cancer Genome Atlas (TCGA) by bootstrapping showed that patients with high-risk scores had a poorer prognosis than patients with low-risk scores, and this was confirmed in the Gene Expression Omnibus (GEO) cohort. There were also differences between the high-risk and low-risk groups in clinical information and different anatomical sites such as age, sex, TNM stage, grade stage, surgery or no surgery, chemotherapy, radiotherapy, no radiotherapy, neck lymph node dissection or not, and neck lymphovascular invasion, larynx, overlapping lesion of lip, and oral cavity and pharynx tonsil of overall survival (OS). Immune-related characteristics, tumor microenvironment (TME) characteristics, and immunotherapy response were significantly different between high- and low-risk groups. The four AMGs were also found to be associated with the expression of markers of various immune cell subpopulations. Therefore, our comprehensive approach revealed the characterization of AAM in HNSCC to predict prognosis and guide clinical therapy.

Analysis of the Prognostic Significance and Immune Infiltration of the Amino Acid Metabolism-Related Genes in Colon Adenocarcinoma.

Amino acid metabolization is verified to be a part in the progression of cancer. However, genes related to the amino acid metabolism have not been identified in colon adenocarcinoma (COAD). A systematic prognostic model of COAD becomes a pressing need. Among genes related to the amino acid metabolism, RIMKLB, ASPG, TH, MTAP, AZIN2, PSMB2, HDC, ACMSD, and PSMA8 were identified to construct a risk model. Kaplan–Meier (K–M) analyses demonstrated that the high-risk group achieved a poor prognosis. Area under the respective ROC (AUC) values indicated the robustness of the model. To highlight its clinical value, multivariate Cox was used to obtain the optimal variables to construct a nomogram. A higher tumor mutation burden was observed in the high-risk group. However, the low-risk group had a stronger immune infiltration. Seven molecular subtypes were found by consensus cluster. Twenty-two hub genes were identified related to the ESTIMATE score using WGCNA. In brief, our research constructed a stable prognostic model related to the amino acid metabolism in COAD, revealing its connection to the immune microenvironment. The model guided the outcome of COAD and the direction of immunotherapy.

Amino acid metabolism genes associated with immunotherapy responses and clinical prognosis of colorectal cancer

Based on amino acid metabolism-related genes (AAMRGs), this study aimed at screening out key prognosis-related genes and finding the underlying correlation between the amino acid metabolism and tumor immune microenvironment of colorectal cancer. A total of 448 amino acid metabolism-related genes were obtained from MsigDB. The risk signature was built based on differential expression genes, univariate Cox, and LASSO analyses with 403 patients’ data downloaded from the TCGA database. Survival analysis and independence tests were performed to confirm the validity of the risk signature. Single-sample gene set enrichment analysis (ssGSEA), tumor mutation burden (TMB), the score of tumor immune dysfunction and exclusion (TIDE), the immunophenoscore obtained from The Cancer Immunome Atlas database, and the IC50 of drugs were used to find the relationship among the risk signature, immune status, immunotherapy response, and drug sensitivity of colorectal cancer. We identified five amino acid metabolism-related genes for the construction of the risk signature, including ENOPH1, ACAT1, ALDH4A1, FAS, and ASPG. The low-risk group was significantly associated with a better prognosis (p < 0.0001). In the entire set, the area under the curve (AUC) for 1, 3, and 5 years was 0.717, 0.734, and 0.764, respectively. We also discovered that the low-risk subgroup was related to more activity of immune cells, had higher expression of some immune checkpoints, and was more likely to benefit from immunotherapy. ssGSEA revealed that except the processes of glutamine histidine, lysine, tyrosine, and L-phenylalanine metabolism, the other amino acid metabolism pathways were more active in the samples with the low risk scores, whereas the activities of synthesis and transportation of most amino acids were similar. Hedgehog signaling, WNT/β-catenin signaling, mitotic, notch signaling, and TGF-β signaling were the top five pathways positively associated with the risk score. To sum up, AAMRGs were associated with the immune microenvironment of CRC patients and could be applied as biomarkers to predict the prognosis and immunotherapy response of patients.

Fecal Microbial Changes in Response to Finishing Pigs Directly Fed With Fermented Feed.

The present study investigated the effects of fermented complete feed (FCF) on fecal microbial composition during the grower-finisher period. A total of 20 pigs (Duroc × Landrace × Yorkshire, 48.74± 1.49 kg) were divided randomly into two groups: the CN group (pigs fed with a basal diet) and the FCF group (pigs fed with FCF). After a 60-day trial period, 3 pigs with middle-weight from each treatment were selected for fecal sampling and fecal microbiota analysis. The results showed that the FCF significantly increased operational taxonomic units (OUT) numbers, alpha diversity (Simpson index and Shannon index), and beta diversity, which means that FCF increased the fecal microbiota diversity. At the phylum level, the abundance of Tenericutes, Spirochaetae, Verrucomicrobia, and Cyanobacteria were changed in pigs fed with FCF; and at the genus level, the abundance of Christensenellaceae_R-7_group, Treponema_2, Ruminococcaceae_UCG-005, Prevotellaceae_UCG-003, Phascolarctobacterium, Roseburia, and Prevotella_9 were changed in pigs fed with FCF. The linear discriminant analysis effect size (LEfSe) analysis showed that Roseburia and Prevotella_9 genera were increased, while Tenericutes phyla and Streptococcus, Christensenellaceae_R-7_group, and Lactobacillus genera were decreased in the FCF group compared to the CN group. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) results predicted that the relative abundance of infectious diseases: parasitic associated genes, xenobiotics biodegradation, and metabolism-associated genes were significantly reduced in the FCF group when compared with the CN group, and the relative abundance of signal transduction associated genes, amino acid metabolism-related genes, and replication and repair associated genes were significantly higher in the FCF group when compared with the CN group. In addition, the relative abundance of transport and catabolism-associated genes, membrane transport-associated genes, and biosynthesis of other secondary metabolite-associated genes tended to be higher in the FCF group when compared with the CN group; and the relative abundance of immune diseases associated genes tended to be lower in the FCF group when compared with the CN group. In conclusion, the FCF influenced the alpha and beta diversity of the fecal microbiota of pigs.

A Prognostic Model for Colon Adenocarcinoma Patients Based on Ten Amino Acid Metabolism Related Genes

BackgroundAmino acid metabolism plays a vital role in cancer biology. However, the application of amino acid metabolism in the prognosis of colon adenocarcinoma (COAD) has not yet been explored. Here, we construct an amino acid metabolism-related risk model to predict the survival outcome of COAD and improve clinical decision making.MethodsThe RNA-sequencing-based transcriptome for 524 patients with COAD from The Cancer Genome Atlas (TCGA) was selected as a training set. The integrated Gene Expression Omnibus (GEO) dataset with 1,430 colon cancer samples was used for validation. Differential expression of amino acid metabolism-related genes (AAMRGs) was identified for prognostic gene selection. Univariate cox regression analysis, LASSO-penalized Cox regression analysis, and multivariate Cox regression analysis were applied to construct a prognostic risk model. Moreover, the correlation between risk score and microsatellite instability, immunotherapy response, and drug sensitivity were analyzed.ResultsA prognostic signature was constructed based on 10 AAMRGs, including ASPG, DUOX1, GAMT, GSR, MAT1A, MTAP, PSMD12, RIMKLB, RPL3L, and RPS17. Patients with COAD were divided into high-risk and low-risk group based on the medianrisk score. Univariate and multivariate Cox regression analysis revealed that AAMRG-related signature was an independent risk factor for COAD. Moreover, COAD patients in the low-risk group were more sensitive to immunotherapy targeting PD-1 and CTLA-4.ConclusionOur study constructed a prognostic signature based on 10 AAMRGs, which could be used to build a novel prognosis model and identify potential drug candidates for the treatment of COAD.