Integration of metabolomic and transcriptomic data reveals metabolic pathway alteration in breast cancer and impact of related signature on survival

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

A Multiomics, Molecular Atlas of Breast Cancer Survivors

Breast cancer imposes a significant burden globally. While the survival rate is steadily improving, much remains to be elucidated. This observational, single time point, multiomic study utilizing genomics, proteomics, targeted and untargeted metabolomics, and metagenomics in a breast cancer survivor (BCS) and age-matched healthy control cohort (N = 100) provides deep molecular phenotyping of breast cancer survivors. In this study, the BCS cohort had significantly higher polygenic risk scores for breast cancer than the control group. Carnitine and hexanoyl carnitine were significantly different. Several bile acid and fatty acid metabolites were significantly dissimilar, most notably the Omega-3 Index (O3I) (significantly lower in BCS). Proteomic and metagenomic analyses identified group and pathway differences, which warrant further investigation. The database built from this study contributes a wealth of data on breast cancer survivorship where there has been a paucity, affording the ability to identify patterns and novel insights that can drive new hypotheses and inform future research. Expansion of this database in the treatment-naïve, newly diagnosed, controlling for treatment confounders, and through the disease progression, can be leveraged to profile and contextualize breast cancer and breast cancer survivorship, potentially leading to the development of new strategies to combat this disease and improve the quality of life for its victims.

Multiomics insights on the onset, progression, and metastatic evolution of breast cancer

Breast cancer is the most common malignant neoplasm in women. Despite progress to date, 700,000 women worldwide died of this disease in 2020. Apparently, the prognostic markers currently used in the clinic are not sufficient to determine the most appropriate treatment. For this reason, great efforts have been made in recent years to identify new molecular biomarkers that will allow more precise and personalized therapeutic decisions in both primary and recurrent breast cancers. These molecular biomarkers include genetic and post-transcriptional alterations, changes in protein expression, as well as metabolic, immunological or microbial changes identified by multiple omics technologies (e.g., genomics, epigenomics, transcriptomics, proteomics, glycomics, metabolomics, lipidomics, immunomics and microbiomics). This review summarizes studies based on omics analysis that have identified new biomarkers for diagnosis, patient stratification, differentiation between stages of tumor development (initiation, progression, and metastasis/recurrence), and their relevance for treatment selection. Furthermore, this review highlights the importance of clinical trials based on multiomics studies and the need to advance in this direction in order to establish personalized therapies and prolong disease-free survival of these patients in the future.

Endocrine-disrupting compounds and metabolomic reprogramming in breast cancer

Endocrine-disrupting chemicals pose a growing threat to human health through their increasing presence in the environment and their potential interactions with the mammalian endocrine systems. Due to their structural similarity to hormones like estrogen, these chemicals can interfere with endocrine signaling, leading to many deleterious effects. Exposure to estrogenic endocrine-disrupting compounds (EDC) is a suggested risk factor for the development of breast cancer, one of the most frequently diagnosed cancers in women. However, the mechanisms through which EDCs contribute to breast cancer development remain elusive. To rapidly proliferate, cancer cells undertake distinct metabolic programs to utilize existing nutrients in the tumor microenvironment and synthesize macromolecules de novo. EDCs are known to dysregulate cell signaling pathways related to cellular metabolism, which may be an important mechanism through which they exert their cancer-promoting effects. These altered pathways can be studied via metabolomic analysis, a new advancement in -omics technologies that can interrogate molecular pathways that favor cancer development and progression. This review will summarize recent discoveries regarding EDCs and the metabolic reprogramming that they may induce to facilitate the development of breast cancer.

Increased ENT2 expression and its association with altered purine metabolism in cell lines derived from different stages of colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent malignant cancer types worldwide. Although the purine metabolism pathway is vital for cancer cell survival, little is known about the role of equilibrative nucleoside transporter 2 (ENT2) in CRC development and its association with purine metabolites. The aim of the present study was to evaluate the levels of hypoxanthine phosphoribosyl transferase (HPRT), hypoxanthine and uric acid (UA), as well as xanthine oxidase (XO) activity, and investigate their association with ENT2 expression levels in a normal human colon cell line and CRC cell lines derived from different stages of CRC. These analyses were performed using the normal colon CCD-841CoN cell line and a panel of human CRC cell lines comprising SW480, HCT15 and HCT116, which represent Dukes' B, C and D stages, respectively. Reverse transcription-quantitative PCR was performed to determine the level of ENT2 mRNA expression. In cells of all CRC stages, the levels of HPRT and hypoxanthine were significantly higher (P<0.05), while XO activity and UA levels were significantly decreased (P<0.05), compared with those in the CCD-841CoN cell line. ENT2 expression was found to be elevated in cells derived from all stages of CRC. The Dukes' D stage cell line had higher levels of HPRT and hypoxanthine, although its ENT2 level was not significantly lower than that of the Dukes' B and C stage cell lines. Increased levels of HPRT and hypoxanthine in various stages of CRC may indicate an increase in the activity of the salvage pathway. The increased expression of ENT2 implies the importance of the ENT2 protein in facilitating hypoxanthine transport, which is required for enhanced DNA synthesis via hypoxanthine recycling. In conclusion, ENT2 may have potential as a target in the development of CRC therapeutics.

Metabolic Phenotyping in Prostate Cancer Using Multi-Omics Approaches

Prostate cancer (PCa), one of the most frequently diagnosed cancers among men worldwide, is characterized by a diverse biological heterogeneity. It is well known that PCa cells rewire their cellular metabolism to meet the higher demands required for survival, proliferation, and invasion. In this context, a deeper understanding of metabolic reprogramming, an emerging hallmark of cancer, could provide novel opportunities for cancer diagnosis, prognosis, and treatment. In this setting, multi-omics data integration approaches, including genomics, epigenomics, transcriptomics, proteomics, lipidomics, and metabolomics, could offer unprecedented opportunities for uncovering the molecular changes underlying metabolic rewiring in complex diseases, such as PCa. Recent studies, focused on the integrated analysis of multi-omics data derived from PCa patients, have in fact revealed new insights into specific metabolic reprogramming events and vulnerabilities that have the potential to better guide therapy and improve outcomes for patients. This review aims to provide an up-to-date summary of multi-omics studies focused on the characterization of the metabolomic phenotype of PCa, as well as an in-depth analysis of the correlation between changes identified in the multi-omics studies and the metabolic profile of PCa tumors.

AMPD1 Is Associated With the Immune Response and Serves as a Prognostic Marker in HER2-Positive Breast Cancer

Background

Although immunotherapy has been used in the treatment of metastatic triple negative breast cancer (TNBC), its therapeutic influence on human epidermal growth factor receptor 2 (HER2)-positive subtype remains controversial. It is therefore imperative to find biomarkers that can predict the immune response in HER2+ BC.

Methods

ESTIMATE was utilized to compute the ImmuneScore and StromalScore from data obtained from TCGA database, and differentially expressed genes (DEGs) were identified. In addition, univariate Cox regression was used to assess candidate genes such as AMPD1, CD33, and CCR5. Gene set enrichment analysis (GSEA) was used to further understand AMPD1-associated pathways. Moreover, immunohistochemical analyses were performed to further reveal the relationship among AMPD1, CD4 and CD8 genes.

Results

The expression of AMPD1 was markedly associated with disease outcome and tumor-infiltrating immune cells (TICs). In addition, AMPD1 was associated with lymph node status, age and the expression of PD-L1 and PD-L2. High AMPD1 expression was linked to longer overall survival (OS). Upregulated expression of AMPD1 correlated with the enrichment of immune-related signaling pathways. In addition, immunohistochemical analyses demonstrated a co-expression profile among AMPD1, CD4 and CD8 genes.

Conclusions

Taken together, our data demonstrated that AMPD1 might serve as a novel biomarker for predicting the immune response and disease outcome in HER2+ BC.

Tissue-based metabolomics reveals metabolic signatures and major metabolic pathways of gastric cancer with help of transcriptomic data from TCGA

PURPOSE

The aim of the present study was to screen differential metabolites of gastric cancer (GC) and identify the key metabolic pathways of GC.

METHODS

GC (n=28) and matched paracancerous (PC) tissues were collected, and LC-MS/MS analysis were performed to detect metabolites of GC and PC tissues. Metabolite pathways based on differential metabolites were enriched by MetaboAnalyst, and genes related to metabolite pathways were identified using the KEGGREST function of the R software package. Transcriptomics data from The Cancer Genome Atlas (TCGA) was analyzed to obtain differentially expressed genes (DEGs) of GC. Overlapping genes were acquired from metabonimics and transcriptomics data. Pathway enrichment analysis was performed using String. The protein expression of genes was validated by the Human Protein Atlas (HPA) database.

RESULTS

A total of 325 key metabolites were identified, 111 of which were differentially expressed between the GC and PC groups. Seven metabolite pathways enriched by MetaboAnalyst were chosen, and 361 genes were identified by KEGGREST. A total of 2831 DEGs were identified from the TCGA cohort. Of these, 1317 were down-regulated, and 1636 were up-regulated. Twenty-two overlapping genes were identified between genes related to metabolism and DEGs. Glycerophospholipid (GPL) metabolism is likely associated with GC, of which AGPAT9 and ETNPPL showed lower expressed in GC tissues.

CONCLUSIONS

We investigated the tissue-based metabolomics profile of GC, and several differential metabolites were identified. GPL metabolism may affect on progression of GC.

Potential Significance and Clinical Value Explorations of Calmin (CLMN) in Breast Invasive Carcinoma

Objective

Function of calmin (CLMN) was rarely reported in human diseases, especially in tumor. Present study initially assessed the significance of CLMN in breast invasive carcinoma (BRCA).

Methods

Expressions of CLMN containing mRNA and protein in BRCA was firstly assessed, and association of CLMN mRNA expression with clinical phenotypes of BRCA patients was analyzed as well. Prognostic value of CLMN in BRCA was subsequently predicted based on the clinical characteristics of patients. Finally, the potential biological function associated with CLMN involved in BRCA was revealed.

Results

(1) The mRNA expression of CLMN was lower in BRCA compared with that in normal patients (P<0.001). However, result of CLMN total protein expression was opposite (P<0.05). (2) The mRNA expression of CLMN was statistically associated with BRCA patient’s age, gender, PR status, ER status, histological type, tumor stage, copy number, and methylation level (all P<0.05). (3) Compared with low expression group, high expression of CLMN was conducive to the overall survival of BRCA patients (P=0.0011). Detailed, survival difference between CLMN high and low expression groups was observed in patients with stage 1 (P=0.0250), positive ER status (P=0.0042), negative HER status (P=0.0433), luminal A (P=0.0065), luminal B (P=0.0123) and positive lymph node status (P=0.0069). Pathway analysis suggested that CLMN mainly participated in cell cycle process (P<0.05) and exerted inhibition effect on the cell cycle involved in BRCA (P<0.05).

Conclusion

CLMN mRNA high expression prolonged the survival time of patients and caused a favorable prognosis. The positive function of CLMN in BRCA required further investigation in future work.

Multi-Omic Approaches to Breast Cancer Metabolic Phenotyping: Applications in Diagnosis, Prognosis, and the Development of Novel Treatments

Breast cancer (BC) is characterized by high disease heterogeneity and represents the most frequently diagnosed cancer among women worldwide. Complex and subtype-specific gene expression alterations participate in disease development and progression, with BC cells known to rewire their cellular metabolism to survive, proliferate, and invade. Hence, as an emerging cancer hallmark, metabolic reprogramming holds great promise for cancer diagnosis, prognosis, and treatment. Multi-omics approaches (the combined analysis of various types of omics data) offer opportunities to advance our understanding of the molecular changes underlying metabolic rewiring in complex diseases such as BC. Recent studies focusing on the combined analysis of genomics, epigenomics, transcriptomics, proteomics, and/or metabolomics in different BC subtypes have provided novel insights into the specificities of metabolic rewiring and the vulnerabilities that may guide therapeutic development and improve patient outcomes. This review summarizes the findings of multi-omics studies focused on the characterization of the specific metabolic phenotypes of BC and discusses how they may improve clinical BC diagnosis, subtyping, and treatment.

An integrated study of metabolomics and transcriptomics to reveal the anti-primary dysmenorrhea mechanism of Akebiae Fructus

ETHNOPHARMACOLOGICAL RELEVANCE

Akebiae Fructus, a Tujia minority folk medicine and a well-known traditional Chinese medicine for soothing the liver, regulating Qi, promoting blood circulation and relieving pain, is widely used in the treatment of primary dysmenorrhea. However, little is known about its underlying mechanism.

AIM OF THE STUDY

To explore the effect of Akebiae Fructus on primary dysmenorrhea model induced by estradiol benzoate and oxytocin, and to provide better understanding of the mechanism of Akebiae Fructus for primary dysmenorrhea treatment.

MATERIALS AND METHODS

The primary dysmenorrhea mouse model was used in this study. Except for the control group and the normal administration group, the mice of other groups were subcutaneously injected with estradiol benzoate (10 mg/kg/d) for 10 consecutive days. From the 5th day of the ten-day model period, the positive control groups were given 0.075 g/kg ibuprofen and 7.5 g/kg Leonurus granule, the drug groups were given 0.2 g/kg, 0.4 g/kg, 0.8 g/kg Akebiae Fructus extract, the normal administration group was given 0.8 g/kg Akebiae Fructus extract, and the same volume saline was given in the control group. On the tenth day, oxytocin (10 U/kg) was peritoneally injected after estradiol benzoate injected 1 h. After the oxytocin injection, writhing behavior was observed for 30 min. Then the uterine tissue was collected to measure the level of PGF2α and PGE2, and for histological analysis and transcriptomics analysis. Meanwhile, plasma and urine samples were collected for metabolomic analysis.

RESULTS

Akebiae Fructus inhibited the writhing, decreased the PGF2α level and ameliorated the morphological changes. 32 potential metabolic biomarkers in plasma and 17 in urine were found for primary dysmenorrhea, and after Akebiae Fructus treatment, 25 metabolites in plasma and 14 in urine were restored. These altered metabolites were mainly involved in lipid, amino acid and organic acid metabolism. For the transcriptomic study, a total of 2244 differentially expressed genes (1346 up-regulated and 898 down-regulated) were obtained between the control and model group, and 148 differentially expressed genes (DEGs) were found related with Akebiae Fructus treatment of primary dysmenorrhea. Correlation analysis was carried out based on the transcriptomic and metabolomic data. 5 differentially expressed genes (Plpp3, Sgpp2, Arg1, Adcy8, Ak5) were found related with the enrichment metabolic pathways. The mechanism by which Akebiae Fructus ameliorates primary dysmenorrhea may account for the regulation of the gene expression to control the key enzymes in the sphingolipid metabolism, arginine and proline metabolism, glycerophospholipid metabolism and purine metabolism, inhibiting the abnormal secretion of PGF2α, alleviating the uterine contraction and reducing inflammation and pain.

CONCLUSIONS

Akebiae Fructus could effectively alleviate the symptoms of primary dysmenorrhea, regulate metabolic disorders, and control the related gene expression in primary dysmenorrhea. The study may provide clues for further study of Akebiae Fructus treatment on primary dysmenorrhea.

Identification of potential biomarkers and metabolic pathways based on integration of metabolomic and transcriptomic data in the development of breast cancer

OBJECTIVE

Breast cancer (BC) is the most common type of malignant tumor and the most common cause of cancer-related mortality among women. Metabolic reprogramming is considered a hallmark of cancer, and the study of BC metabolism may be the key to the development of new strategies for diagnosis and treatment. In this study, we aimed to explore the potential metabolites and gene biomarkers for BC through the integration of metabolomics and transcriptomic data, which could further understand BC tumor biology.

METHODS

Transcriptome dataset GSE139038 was downloaded to explore the differentially expressed genes (DEGs) between BC and normal control (NC) samples. Metabolomics dataset MTBLS326 was downloaded and preprocessed to obtain altered metabolites. Then, the principal component analysis (PCA) and linear models were used to reveal DEGs-metabolites relations. Finally, the pathway enrichment analysis of altered metabolites was performed.

RESULTS

A total of 280 DEGs and eight metabolites were explored between BC and NC samples. The liner module analysis investigated 28 DEGs-metabolites interactions including WASP family member 3 (WASF3)-lactate, ras-related protein Rab-7B (RAB7B)-lactate, and methyltransferase-like 7A (METTL7A)-pyruvate. Finally, pathways analysis showed that these metabolites (such as lactate and pyruvate) were mainly enriched in pathways like disorders of the Krebs cycle.

CONCLUSIONS

Combining with the transcriptomic and metabolomics data, we found that lactate, pyruvate, WASF3, RAB7B, and METTL7A might be used as novel biomarkers and potential therapeutic targets for BC. In addition, the disorders of the Krebs cycle pathway might affect the progression of BC.

Characteristics of immune cell infiltration and associated diagnostic biomarkers in ulcerative colitis: results from bioinformatics analysis

Ulcerative colitis (UC) is a type of refractory and recurrent inflammatory disorder that occurs in colon and rectum. Immune cell infiltration plays a critical role in UC progression; therefore, this study aims to explore potential biomarkers for UC and to analyze characteristics of immune cell infiltration based on the bioinformatic analysis. In this study, 248 differentially expressed genes (DEGs) were screened, and the top 20 immune-related hub genes and pathways were assessed. Moreover, four candidate diagnostic biomarkers (DPP10, S100P, AMPD1, and ASS1) were identified and validated. Immune cell infiltration analysis identified 13 differentially infiltrated immune cells (IICs) in UC samples compared to normal samples, and the result showed that two IICs only expressed in UC samples. In addition, the present research found that DPP10 was negatively correlated with neutrophils, S100P exhibited a positive correlation with resting CD4 memory T cells, AMPD1 was positively correlated with M2 macrophages, and ASS1 was inversely associated with neutrophils and positively related to CD8 T cells. Taken together, these findings indicated that DPP10, S100P, AMPD1, and ASS1 may act as diagnostic biomarkers for UC, and that differential IICs may help to illustrate the progression of UC.

Human Plasma Metabolomics for Biomarker Discovery: Targeting the Molecular Subtypes in Breast Cancer

PURPOSE

The aim of this study is to identify differential metabolomic signatures in plasma samples of distinct subtypes of breast cancer patients that could be used in clinical practice as diagnostic biomarkers for these molecular phenotypes and to provide a more individualized and accurate therapeutic procedure.

METHODS

Untargeted LC-HRMS metabolomics approach in positive and negative electrospray ionization mode was used to analyze plasma samples from LA, LB, HER2+ and TN breast cancer patients and healthy controls in order to determine specific metabolomic profiles through univariate and multivariate statistical data analysis.

RESULTS

We tentatively identified altered metabolites displaying concentration variations among the four breast cancer molecular subtypes. We found a biomarker panel of 5 candidates in LA, 7 in LB, 5 in HER2 and 3 in TN that were able to discriminate each breast cancer subtype with a false discovery range corrected p-value < 0.05 and a fold-change cutoff value > 1.3. The model clinical value was evaluated with the AUROC, providing diagnostic capacities above 0.85.

CONCLUSION

Our study identifies metabolic profiling differences in molecular phenotypes of breast cancer. This may represent a key step towards therapy improvement in personalized medicine and prioritization of tailored therapeutic intervention strategies.

Targeted Metabolomics Analyses Reveal Specific Metabolic Alterations in High-Grade Prostate Cancer Patients

Prostate cancer (PCa) is a hormone-dependent tumor characterized by an extremely heterogeneous prognosis. Despite recent advances in partially uncovering some of the biological processes involved in its progression, there is still an urgent need for identifying more accurate and specific prognostic procedures to differentiate between disease stages. In this context, targeted approaches, focused on mapping dysregulated metabolic pathways, could play a critical role in identifying the mechanisms driving tumorigenesis and metastasis. In this study, a targeted analysis of the nuclear magnetic resonance-based metabolomic profile of PCa patients with different tumor grades, guided by transcriptomics profiles associated with their stages, was performed. Serum and urine samples were collected from 73 PCa patients. Samples were classified according to their Gleason score (GS) into low-GS (GS < 7) and high-GS PCa (GS ≥ 7) groups. A total of 36 metabolic pathways were found to be dysregulated in the comparison between different PCa grades. Particularly, the levels of glucose, glycine and 1-methlynicotinamide, metabolites involved in energy metabolism and nucleotide synthesis were significantly altered between both groups of patients. These results underscore the potential of targeted metabolomic profiling to characterize relevant metabolic changes involved in the progression of this neoplastic process.

Integrating transcriptome and metabolome variability to reveal pathogenesis of esophageal squamous cell carcinoma

BACKGROUND

Esophageal Squamous Cell Carcinoma (ESCC) is an aggressive malignancy, leading to more than 250,000 deaths in China every year. However, the pathogenesis of ESCC remains unclear, which hinders the diagnosis and treatment of the disease in clinic.

METHOD

To elucidate underlying mechanism and identify potential biomarkers, an integrative strategy of combining transcriptome and metabolome has been implemented to find potential causal genes and metabolites for ESCC.

RESULTS

At the transcriptional level, dysregulated genes in ESCC patients were identified and pathway enrichment analysis discovered tyrosine metabolic pathway as a promising target. Subsequently, up- and down-stream metabolites of tyrosine pathway were explored through targeted metabolome approach. Five metabolites, i.e. phenylalanine, 4-hydroxyphenyllactic acid, 3,4-dihydroxyphenylalanine, 3,4-dihydroxyphenylacetic acid and tyrosine were identified as diagnosis biomarkers for ESCC and metastatic ESCC patients. A biological model incorporating both transcriptional and metabolic dysregulation was also established to illustrate the potential mechanism of tumorigenesis and metastasis for ESCC.

CONCLUSION

Integrative transcriptomics and metabolomics analysis suggested that tyrosine pathway was essential for the tumorigenesis and metastasis of ESCC primarily through altering immune response and regulating tumor microenvironment. This research sheds light on the pathogenesis of ESCC and discovers potential biomarkers for the diagnosis of the disease.

A Prognostic Model Based on Six Metabolism-Related Genes in Colorectal Cancer

An increasing number of studies have shown that abnormal metabolism processes are closely correlated with the genesis and progression of colorectal cancer (CRC). In this study, we systematically explored the prognostic value of metabolism-related genes (MRGs) for CRC patients. A total of 289 differentially expressed MRGs were screened based on The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database (MSigDB), and 72 differentially expressed transcription factors (TFs) were obtained from TCGA and the Cistrome Project database. The clinical samples obtained from TCGA were randomly divided at a ratio of 7 : 3 to obtain the training group (n = 306) and the test group (n = 128). After univariate and multivariate Cox regression analyses, we constructed a prognostic model based on 6 MRGs (AOC2, ENPP2, ADA, GPD1L, ACADL, and CPT2). Kaplan–Meier survival analysis of the training group, validation group, and overall samples proved that the model had statistical significance in predicting the outcomes of patients. Independent prognosis analysis suggested that this risk score might serve as an independent prognosis factor for CRC patients. Moreover, we combined the prognostic model and the clinical characteristics in a nomogram to predict the overall survival of CRC patients. Furthermore, gene set enrichment analysis (GSEA) was conducted to identify the enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in the high- and low-risk groups, which might provide novel therapeutic targets for CRC patients. We discovered through the protein-protein interaction (PPI) network and TF-MRG regulatory network that 7 hub genes were retrieved from the PPI network and 4 kinds of differentially expressed TFs (NR3C1, MYH11, MAF, and CBX7) positively regulated 4 prognosis-associated MRGs (GSTM5, PTGIS, ENPP2, and P4HA3).

Analysis of prognostic genes in the tumor microenvironment of lung adenocarcinoma

Background

Prognostic genes in the tumor microenvironment play an important role in immune biological processes and the response of cancer to immunotherapy. Thus, we aimed to assess new biomarkers that are associated with immune/stromal cells in lung adenocarcinomas (LUAD) using the ESTIMATE algorithm, which also significantly affects the prognosis of cancer.

Methods

The RNA sequencing (RNA-Seq) and clinical data of LUAD were downloaded from the the Cancer Genome Atlas (TCGA ). The immune and stromal scores were calculated for each sample using the ESTIMATE algorithm. The LUAD gene chip expression profile data and the clinical data (GSE37745, GSE11969, and GSE50081) were downloaded from the Gene Expression Omnibus (GEO) for subsequent validation analysis. Differentially expressed genes were calculated between high and low score groups. Univariate Cox regression analysis was performed on differentially expressed genes (DEGs) between the two groups to obtain initial prognosis genes. These were verified by three independent LUAD cohorts from the GEO database. Multivariate Cox regression was used to identify overall survival-related DEGs. UALCAN and the Human Protein Atlas were used to analyze the mRNA /protein expression levels of the target genes. Immune cell infiltration was evaluated using the Tumor Immune Estimation Resource (TIMER) and CIBERSORT methods, and stromal cell infiltration was assessed using xCell.

Results

In this study, immune scores and stromal scores are significantly associated with the clinical characteristics of LUAD, including T stage, M stage, pathological stage, and overall survival time. 530 DEGs (18 upregulated and 512 downregulated) were found to coexist in the difference analysis with the immune scores and stromal scores subgroup. Univariate Cox regression analysis showed that 286 of the 530 DEGs were survival-related genes (p < 0.05). Of the 286 genes initially identified, nine prognosis-related genes (CSF2RB, ITK, FLT3, CD79A, CCR4, CCR6, DOK2, AMPD1, and IGJ) were validated from three separate LUAD cohorts. In addition, functional analysis of DEGs also showed that various immunoregulatory molecular pathways, including regulation of immune response and the chemokine signaling pathways, were involved. Five genes (CCR6, ITK, CCR4, DOK2, and AMPD1) were identified as independent prognostic indicators of LUAD in specific data sets. The relationship between the expression levels of these genes and immune genes was assessed. We found that CCR6 mRNA and protein expression levels of LUAD were greater than in normal tissues. We evaluated the infiltration of immune cells and stromal cells in groups with high and low levels of expression of CCR6 in the TCGA LUAD cohort. In summary, we found a series of prognosis-related genes that were associated with the LUAD tumor microenvironment.

Identification of an energy metabolism‑related gene signature in ovarian cancer prognosis

Changes in energy metabolism may be potential biomarkers and therapeutic targets for cancer as they frequently occur within cancer cells. However, basic cancer research has failed to reach a consistent conclusion on the function(s) of mitochondria in energy metabolism. The significance of energy metabolism in the prognosis of ovarian cancer remains unclear; thus, there remains an urgent need to systematically analyze the characteristics and clinical value of energy metabolism in ovarian cancer. Based on gene expression patterns, the present study aimed to analyze energy metabolism‑associated characteristics to evaluate the prognosis of patients with ovarian cancer. A total of 39 energy metabolism‑related genes significantly associated with prognosis were obtained, and three molecular subtypes were identified by nonnegative matrix factorization clustering, among which the C1 subtype was associated with poor clinical outcomes of ovarian cancer. The immune response was enhanced in the tumor microenvironment. A total of 888 differentially expressed genes were identified in C1 compared with the other subtypes, and the results of the pathway enrichment analysis demonstrated that they were enriched in the 'PI3K‑Akt signaling pathway', 'cAMP signaling pathway', 'ECM‑receptor interaction' and other pathways associated with the development and progression of tumors. Finally, eight characteristic genes (tolloid‑like 1 gene, type XVI collagen, prostaglandin F2α, cartilage intermediate layer protein 2, kinesin family member 26b, interferon inducible protein 27, growth arrest‑specific gene 1 and chemokine receptor 7) were obtained through LASSO feature selection; and a number of them have been demonstrated to be associated with ovarian cancer progression. In addition, Cox regression analysis was performed to establish an 8‑gene signature, which was determined to be an independent prognostic factor for patients with ovarian cancer and could stratify sample risk in the training, test and external validation datasets (P<0.01; AUC >0.8). Gene Set Enrichment Analysis results revealed that the 8‑gene signature was involved in important biological processes and pathways of ovarian cancer. In conclusion, the present study established an 8‑gene signature associated with metabolic genes, which may provide new insights into the effects of energy metabolism on ovarian cancer. The 8‑gene signature may serve as an independent prognostic factor for ovarian cancer patients.

Transcriptomics- and metabolomics-based integration analyses revealed the potential pharmacological effects and functional pattern of in vivo Radix Paeoniae Alba administration

Background

Radix Paeoniae Alba (RPA) and other natural medicines have remarkable curative effects and are widely used in traditional Chinese Medicine (TCM). However, due to their multi-component and multi-target characteristics, it is difficult to study the detailed pharmacological mechanisms for those natural medicines in vivo. Therefore, their real effects on organisms is still uncertain.

Methods

RPA was selected as research object, the present study was designed to study the complex mechanisms of RPA in vivo by integrating and interpreting the transcriptomic based RNA-seq and metabolomic based NMR spectrum after RPA administration in mice. A variety of dimension-reduction algorithms and classifier models were applied to the processing of high-throughput data.

Results

Among serum metabolites, the contents of PC and glucose were significantly increased, while the contents of various amino acids, lipids and their metabolites were significantly decreased in mice after RPA administration. Based on the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, differential analysis showed that the liver was the site where RPA exerted a significant effect, which confirmed the rationality of “meridian tropism” in the theory in TCM. In addition, RPA played a role in lipid metabolism by regulating genes encoding enzymes of the glycerolipid metabolism pathway, such as 1-acyl-sn-glycerol-3-phosphate acyltransferase (Agpat), phosphatidate phosphatase (Lpin), phospholipid phosphatase (Plpp) and endothelial lipase (Lipg). We also found that RPA regulates several substance addiction pathways in the brain, such as the cocaine addiction pathway, and the related targets were predicted based on the sequencing data from pathological model in the GEO database. The overall effective pattern of RPA was intuitively presented with a multidimensional radar map through a self-designed model which found that liver and brain were mainly regulated by RPA compared with the traditional meridian tropism theory.

Conclusions

Overall this study expanded the potential application of RPA and provided possible targets and directions for further mechanism study, meanwhile, it also established a multi-dimensional evaluation model to represent the overall effective pattern of TCM for the first time. In the future, such study based on the high-throughput data sets can be used to interpret the theory of TCM and to provide a valuable research model and clinical medication reference for the TCM researchers and doctors.

Multiplatform Investigation of Plasma and Tissue Lipid Signatures of Breast Cancer Using Mass Spectrometry Tools

Plasma and tissue from breast cancer patients are valuable for diagnostic/prognostic purposes and are accessible by multiple mass spectrometry (MS) tools. Liquid chromatography-mass spectrometry (LC-MS) and ambient mass spectrometry imaging (MSI) were shown to be robust and reproducible technologies for breast cancer diagnosis. Here, we investigated whether there is a correspondence between lipid cancer features observed by desorption electrospray ionization (DESI)-MSI in tissue and those detected by LC-MS in plasma samples. The study included 28 tissues and 20 plasma samples from 24 women with ductal breast carcinomas of both special and no special type (NST) along with 22 plasma samples from healthy women. The comparison of plasma and tissue lipid signatures revealed that each one of the studied matrices (i.e., blood or tumor) has its own specific molecular signature and the full interposition of their discriminant ions is not possible. This comparison also revealed that the molecular indicators of tissue injury, characteristic of the breast cancer tissue profile obtained by DESI-MSI, do not persist as cancer discriminators in peripheral blood even though some of them could be found in plasma samples.

Integration of Transcriptome and Metabolome Provides Unique Insights to Pathways Associated With Obese Breast Cancer Patients

Information regarding transcriptome and metabolome has significantly contributed to identifying potential therapeutic targets for the management of a variety of cancers. Obesity has profound effects on both cancer cell transcriptome and metabolome that can affect the outcome of cancer therapy. The information regarding the potential effects of obesity on breast cancer (BC) transcriptome, metabolome, and its integration to identify novel pathways related to disease progression are still elusive. We assessed the whole blood transcriptome and serum metabolome, as circulating metabolites, of obese BC patients compared them with non-obese BC patients. In these patients' samples, 186 significant differentially expressed genes (DEGs) were identified, comprising 156 upregulated and 30 downregulated. The expressions of these gene were confirmed by qRT-PCR. Furthermore, 96 deregulated metabolites were identified as untargeted metabolomics in the same group of patients. These detected DEGs and deregulated metabolites enriched in many cellular pathways. Further investigation, by integration analysis between transcriptomics and metabolomics data at the pathway levels, revealed seven unique enriched pathways in obese BC patients when compared with non-obese BC patients, which may provide resistance for BC cells to dodge the circulating immune cells in the blood. In conclusion, this study provides information on the unique pathways altered at transcriptome and metabolome levels in obese BC patients that could provide an important tool for researchers and contribute further to knowledge on the molecular interaction between obesity and BC. Further studies are needed to confirm this and to elucidate the exact underlying mechanism for the effects of obesity on the BC initiation or/and progression.

Sodium-Dependent Glucose Transporter 1 (SGLT1) Stabled by HER2 Promotes Breast Cancer Cell Proliferation by Activation of the PI3K/Akt/mTOR Signaling Pathway in HER2+ Breast Cancer

Aerobic glycolysis is a hallmark of tumor cells. SGLT1 plays a vital role in glucose metabolism. However, whether SGLT1 could promote cell growth and proliferation in breast cancer remains unclear. Here, we investigated the expression of SGLT1 in breast cancer and examined its role in malignant behavior and prognosis. Further, we examined the SGLT1 expression in breast cancer tissues and its relationship with clinicopathologic characteristics. We clarified that SGLT1 was overexpressed in HER2+ breast cancer cell lines and was affected by HER2 status. We further found that SGLT1 affected breast cancer cell proliferation and patient survival by mediating cell survival pathway activation. SGLT1 was overexpressed in HER2+ breast cancers and associated with lymph node metastasis and HER2+ status. Inhibition of HER2 decreased SGLT1 expression, and the extracellular acidification rate was also reduced in the UACC812 and SKBR3 cell lines. These changes could be reversed by proteasome inhibitor treatment. Knockdown of SGLT1 blocked PI3K/Akt/mTOR signaling, thereby inhibiting cell proliferation. Further, we demonstrated that high SGLT1 was significantly correlated with shorter survival in all breast cancer patients and specifically in HER2+ breast cancer patients. Therefore, we conclude that SGLT1 is overexpressed in HER2+ breast cancer, thereby promoting cell proliferation and shortening survival by activating PI3K/Akt/mTOR signaling. This study submits that SGLT1 is promising not only as a novel biomarker of HER2+ breast cancer subtype but also as a potential drug target.

Application of metabolomics in the diagnosis of breast cancer: a systematic review

Breast cancer (BC) remains the most frequent type of cancer in females worldwide. However, the pathogenesis of BC is still under the cloud, along with the huge challenge of early diagnosis, which is widely acknowledged as the key to a successful therapy. Metabolomics, a newborn innovative technique in recent years, has demonstrated great potential in cancer-related researches. The aim of this review is to look back on clinical and cellular metabolomic studies in the diagnosis of BC over the past decade, and provide a systematic summary of metabolic biomarkers and pathways related to BC diagnosis.

Super-Enhancer-Associated Hub Genes In Chronic Myeloid Leukemia Identified Using Weighted Gene Co-Expression Network Analysis

Purpose

Super-enhancer (SE)-associated oncogenes extensively potentiate the uncontrolled proliferation capacity of cancer cells. In this study, we aimed to identify the SE-associated hub genes associated with the clinical characteristics of chronic myeloid leukemia (CML).

Methods

Eigengenes from CML clinical modules were determined using weighted gene co-expression network analysis (WGCNA). Overlapping genes between eigengenes and SE-associated genes were used to construct protein–protein interaction (PPI) networks and annotate for pathway enrichment analysis. Expression patterns of the top-ranked SE-associated hub genes were further determined in CML patients and healthy controls via real-time PCR. After treatment of K562 cells with the BRD4 inhibitor, JQ1, for 24 hrs, mRNA and protein levels of SE-associated hub genes were evaluated using real-time PCR and Western blotting, respectively. H3K27ac, H3K4me1 and BRD4 ChIP-seq signal peaks were used to predict and identify SEs visualized by the Integrative Genomics Viewer.

Results

The yellow module was significantly related to the status and pathological phase of CML. SE-associated hub candidate genes were mainly enriched in the cell cycle pathway. Based on the PPI networks of hub genes and the top rank of degree, five SE-associated genes were identified: specifically, BUB1, CENPO, KIF2C, ORC1, and RRM2. Elevated expression of these five genes was not only related to CML status and phase but also positively regulated by SE and suppressed by the BRD4 inhibitor, JQ1, in K562 cells. Strong signal peaks of H3K27ac, H3K4me1 and BRD4 ChIP-seq of the five genes were additionally observed close to the predicted SE regions.

Conclusion

This is the first study to characterize SE-associated genes linked to clinical characteristics of CML via weighted gene co-expression network analysis. Our results support a novel mechanism involving aberrant expression of hub SE-associated genes in CML patients and K562 cells, and these genes will be potential new therapeutic targets for human leukemia.