Transcriptional landscape of human cancers

Reference-free inferring of transcriptomic events in cancer cells on single-cell data

BACKGROUND

Cancerous cells' identity is determined via a mixture of multiple factors such as genomic variations, epigenetics, and the regulatory variations that are involved in transcription. The differences in transcriptome expression as well as abnormal structures in peptides determine phenotypical differences. Thus, bulk RNA-seq and more recent single-cell RNA-seq data (scRNA-seq) are important to identify pathogenic differences. In this case, we rely on k-mer decomposition of sequences to identify pathogenic variations in detail which does not need a reference, so it outperforms more traditional Next-Generation Sequencing (NGS) analysis techniques depending on the alignment of the sequences to a reference.

RESULTS

Via our alignment-free analysis, over esophageal and glioblastoma cancer patients, high-frequency variations over multiple different locations (repeats, intergenic regions, exons, introns) as well as multiple different forms (fusion, polyadenylation, splicing, etc.) could be discovered. Additionally, we have analyzed the importance of less-focused events systematically in a classic transcriptome analysis pipeline where these events are considered as indicators for tumor prognosis, tumor prediction, tumor neoantigen inference, as well as their connection with respect to the immune microenvironment.

CONCLUSIONS

Our results suggest that esophageal cancer (ESCA) and glioblastoma processes can be explained via pathogenic microbial RNA, repeated sequences, novel splicing variants, and long intergenic non-coding RNAs (lincRNAs). We expect our application of reference-free process and analysis to be helpful in tumor and normal samples differential scRNA-seq analysis, which in turn offers a more comprehensive scheme for major cancer-associated events.

Transcriptomic characteristics according to tumor size and SUVmax in papillary thyroid cancer patients

The SUVmax is a measure of FDG uptake and is related with tumor aggressiveness in thyroid cancer, however, its association with molecular pathways is unclear. Here, we investigated the relationship between SUVmax and gene expression profiles in 80 papillary thyroid cancer (PTC) patients. We conducted an analysis of DEGs and enriched pathways in relation to SUVmax and tumor size. SUVmax showed a positive correlation with tumor size and correlated with glucose metabolic process. The genes that indicate thyroid differentiation, such as SLC5A5 and TPO, were negatively correlated with SUVmax. Unsupervised analysis revealed that SUVmax positively correlated with DNA replication(r = 0.29, p = 0.009), pyrimidine metabolism(r = 0.50, p < 0.0001) and purine metabolism (r = 0.42, p = 0.0001). Based on subgroups analysis, we identified that PSG5, TFF3, SOX2, SL5A5, SLC5A7, HOXD10, FER1L6, and IFNA1 genes were found to be significantly associated with tumor aggressiveness. Both high SUVmax PTMC and macro-PTC are enriched in pathways of DNA replication and cell cycle, however, gene sets for purine metabolic pathways are enriched only in high SUVmax macro-PTC but not in high SUVmax PTMC. Our findings demonstrate the molecular characteristics of high SUVmax tumor and metabolism involved in tumor growth in differentiated thyroid cancer.

Impaired dNKAP function drives genome instability and tumorigenic growth in Drosophila epithelia

Mutations or dysregulated expression of NF-kappaB-activating protein (NKAP) family genes have been found in human cancers. How NKAP family gene mutations promote tumor initiation and progression remains to be determined. Here, we characterized dNKAP, the Drosophila homolog of NKAP, and showed that impaired dNKAP function causes genome instability and tumorigenic growth in a Drosophila epithelial tumor model. dNKAP-knockdown wing imaginal discs exhibit tumorigenic characteristics, including tissue overgrowth, cell-invasive behavior, abnormal cell polarity, and cell adhesion defects. dNKAP knockdown causes both R-loop accumulation and DNA damage, indicating the disruption of genome integrity. Further analysis showed that dNKAP knockdown induces c-Jun N-terminal kinase (JNK)-dependent apoptosis and causes aberrant cell proliferation in distinct cell populations. Activation of the Notch and JAK/STAT signaling pathways contributes to the tumorigenic growth of dNKAP-knockdown tissues. Furthermore, JNK signaling is essential for dNKAP depletion-mediated cell invasion. Transcriptome analysis of dNKAP-knockdown tissues confirmed the misregulation of signaling pathways involved in promoting tumorigenesis and revealed abnormal regulation of metabolic pathways. dNKAP knockdown and oncogenic Ras, Notch, or Yki mutations show synergies in driving tumorigenesis, further supporting the tumor-suppressive role of dNKAP. In summary, this study demonstrates that dNKAP plays a tumor-suppressive role by preventing genome instability in Drosophila epithelia and thus provides novel insights into the roles of human NKAP family genes in tumor initiation and progression.

A deep profile of gene expression across 18 human cancers

Clinically and biologically valuable information may reside untapped in large cancer gene expression data sets. Deep unsupervised learning has the potential to extract this information with unprecedented efficacy but has thus far been hampered by a lack of biological interpretability and robustness. Here, we present DeepProfile, a comprehensive framework that addresses current challenges in applying unsupervised deep learning to gene expression profiles. We use DeepProfile to learn low-dimensional latent spaces for 18 human cancers from 50,211 transcriptomes. DeepProfile outperforms existing dimensionality reduction methods with respect to biological interpretability. Using DeepProfile interpretability methods, we show that genes that are universally important in defining the latent spaces across all cancer types control immune cell activation, while cancer type-specific genes and pathways define molecular disease subtypes. By linking DeepProfile latent variables to secondary tumor characteristics, we discover that tumor mutation burden is closely associated with the expression of cell cycle-related genes. DNA mismatch repair and MHC class II antigen presentation pathway expression, on the other hand, are consistently associated with patient survival. We validate these results through Kaplan-Meier analyses and nominate tumor-associated macrophages as an important source of survival-correlated MHC class II transcripts. Our results illustrate the power of unsupervised deep learning for discovery of novel cancer biology from existing gene expression data.

Weighted Combination of Łukasiewicz implication and Fuzzy Jaccard similarity in Hybrid Ensemble Framework (WCLFJHEF) for Gene Selection

A framework is developed for gene expression analysis by introducing fuzzy Jaccard similarity (FJS) and combining Łukasiewicz implication with it through weights in hybrid ensemble framework (WCLFJHEF) for gene selection in cancer. The method is called weighted combination of Łukasiewicz implication and fuzzy Jaccard similarity in hybrid ensemble framework (WCLFJHEF). While the fuzziness in Jaccard similarity is incorporated by using the existing Gödel fuzzy logic, the weights are obtained by maximizing the average F-score of selected genes in classifying the cancer patients. The patients are first divided into different clusters, based on the number of patient groups, using average linkage agglomerative clustering and a new score, called WCLFJ (weighted combination of Łukasiewicz implication and fuzzy Jaccard similarity). The genes are then selected from each cluster separately using filter based Relief-F and wrapper based SVMRFE (Support Vector Machine with Recursive Feature Elimination). A gene (feature) pool is created by considering the union of selected features for all the clusters. A set of informative genes is selected from the pool using sequential backward floating search (SBFS) algorithm. Patients are then classified using Naïve Bayes'(NB) and Support Vector Machine (SVM) separately, using the selected genes and the related F-scores are calculated. The weights in WCLFJ are then updated iteratively to maximize the average F-score obtained from the results of the classifier. The effectiveness of WCLFJHEF is demonstrated on six gene expression datasets. The average values of accuracy, F-score, recall, precision and MCC over all the datasets, are 95%, 94%, 94%, 94%, and 90%, respectively. The explainability of the selected genes is shown using SHapley Additive exPlanations (SHAP) values and this information is further used to rank them. The relevance of the selected gene set are biologically validated using the KEGG Pathway, Gene Ontology (GO), and existing literatures. It is seen that the genes that are selected by WCLFJHEF are candidates for genomic alterations in the various cancer types. The source code of WCLFJHEF is available at http://www.isical.ac.in/~shubhra/WCLFJHEF.html.

Systematic analysis of DNA methylation-mediated TF dysregulation on lncRNAs reveals critical roles in tumor immunity

Emerging evidence suggests that DNA methylation affects transcriptional regulation and expression perturbations of long non-coding RNAs (lncRNAs) in cancer. However, a comprehensive investigation into the transcriptional control of DNA methylation-mediated dysregulation of transcription factors (TFs) on lncRNAs has been lacking. Here, we integrated the transcriptome, methylome, and regulatome across 21 human cancers and systematically identified the transcriptional regulation of DNA methylation-mediated TF dysregulations (DMTDs) on lncRNAs. Our findings reveal that TF regulation of lncRNAs is significantly impacted by DNA methylation. Comparative analysis of DMTDs on mRNAs revealed a conserved pattern of TFs involvement. Pan-cancer Methylation TFs (MethTFs) and Methylation LncRNAs (MethLncRNAs) were identified, and were found to be closely associated with cancer hallmarks and clinical features. In-depth analysis of co-expressed mRNAs with pan-cancer MethLncRNAs unveiled frequent disruptions in cancer immunity, particularly in the context of inflammatory response. Furthermore, we identified five immune-related network modules that contribute to immune cell infiltration in cancer. Immune-related subtypes were subsequently classified, characterized by high levels of immune cell infiltration, expression of immunomodulatory genes, and relevant immune cytolytic activity score, major histocompatibility complex score, response to chemotherapy, and prognosis. Our findings provide valuable insights into cancer immunity from the epigenetic and transcriptional regulation perspective.

Small-molecule inhibitors targeting FOXM1: Current challenges and future perspectives in cancer treatments

Forkhead box (FOX) protein M1 (FOXM1) is a critical proliferation-associated transcription factor (TF) that is aberrantly overexpressed in the majority of human cancers and has also been implicated in poor prognosis. A comprehensive understanding of various aspects of this molecule has revealed its role in, cell proliferation, cell migration, invasion, angiogenesis and metastasis. The FOXM1 as a TF directly or indirectly regulates the expression of several target genes whose dysregulation is associated with almost all hallmarks of cancer. Moreover, FOXM1 expression is associated with chemoresistance to different anti-cancer drugs. Several studies have confirmed that suppression of FOXM1 enhanced the drug sensitivity of various types of cancer cells. Current data suggest that small molecule inhibitors targeting FOXM1 in combination with anticancer drugs may represent a novel therapeutic strategy for chemo-resistant cancers. In this review, we discuss the clinical utility of FOXM1, further, we summarize and discuss small-molecule inhibitors targeting FOXM1 and categorize them according to their mechanisms of targeting FOXM1. Despite great progress, small-molecule inhibitors targeting FOXM1 face many challenges, and we present here all small-molecule FOXM1 inhibitors in different stages of development. We discuss the current challenges and provide insights on the future application of FOXM1 inhibition to the clinic.

Abnormal downregulation of 10-formyltetrahydrofolate dehydrogenase promotes the progression of oral squamous cell carcinoma by activating PI3K/Akt/Rb pathway

BACKGROUND

10-formyltetrahydrofolate dehydrogenase (ALDH1L1) is a major folate enzyme, which is usually underexpressed in malignant tumors and competes with tumors for the same folate substrate. However, the specific role and mechanisms of ALDH1L1 in oral squamous cell carcinoma (OSCC) remainsobscure.

METHODS

The expression level of ALDH1L1 in paired OSCC tissues and adjacent noncancerous tissues were detected by quantitative realtime PCR, Western blot and immunohistochemistry. The relationship between ALDH1L1 expression and clinical characteristics was analyzed. Besides, CCK8, EdU staining, colony formation, wound healing, transwell invasion, apoptosis, cell cycle assays and nude mice tumor bearing experiments were employed to assess the role of ALDH1L1 in OSCC. To explore the underlying mechanisms of these effects, cell cycle-related markers were examined.

RESULTS

In this study, we revealed that ALDH1L1 expression was significantly reduced in OSCC, and its downregulation was associated with the malignancy of the tumor and poor prognosis of patients. In vivo and in vitro experiments, downregulation of ALDH1L1 in OSCC significantly inhibited the occurrence of NADP⁺ -dependent catalytic reactions and facilitated tumor cell growth, migration, invasion, survival, cell cycle progression, and xenograft tumor growth. On the contrary, re-expression of ALDH1L1 plays a similar role to anti-folate therapy, promoting NADPH production and suppressing the progression of OSCC. Furthermore, ALDH1L1 overexpressing obviously inhibited the expression of PI3K, p-Akt, CDK2, CDK6, Cyclin D1, Cyclin D3, and Rb in OSCC cells, and promoted the expression of p27. LY294002 and 740 Y-P were used to confirm the inhibitory effects of ALDH1L1 on OSCC progression through PI3K/Akt/Rb pathway.

CONCLUSION

Our findings highlight the clinical value of ALDH1L1 as a prognostic marker and the potential of a new target for anti-folate therapy.

Tumor decellularization reveals proteomic and mechanical characteristics of the extracellular matrix of primary liver cancer

Tumor initiation and progression are critically dependent on interaction of cancer cells with their cellular and extracellular microenvironment. Alterations in the composition, integrity, and mechanical properties of the extracellular matrix (ECM) dictate tumor processes including cell proliferation, migration, and invasion. Also in primary liver cancer, consisting of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), the dysregulation of the extracellular environment by liver fibrosis and tumor desmoplasia is pertinent. Yet, the exact changes occurring in liver cancer ECM remain uncharacterized and underlying tumor-promoting mechanisms remain largely unknown. Herein, an integrative molecular and mechanical approach is used to extensively characterize the ECM of HCC and CCA tumors by utilizing an optimized decellularization technique. We identified a myriad of proteins in both tumor and adjacent liver tissue, uncovering distinct malignancy-related ECM signatures. The resolution of this approach unveiled additional ECM-related proteins compared to large liver cancer transcriptomic datasets. The differences in ECM protein composition resulted in divergent mechanical properties on a macro- and micro-scale that are tumor-type specific. Furthermore, the decellularized tumor ECM was employed to create a tumor-specific hydrogel that supports patient-derived tumor organoids, which provides a new avenue for personalized medicine applications. Taken together, this study contributes to a better understanding of alterations to composition, stiffness, and collagen alignment of the tumor ECM that occur during liver cancer development.

Impact of Laboratory-Adapted Intracellular Trypanosoma cruzi Strains on the Activity Profiles of Compounds with Anti-T. cruzi Activity

Chagas disease is caused by infection with the protozoan parasite, Trypanosoma cruzi. The disease causes ~12,000 deaths annually and is one of the world's 20 neglected tropical diseases, as defined by the World Health Organisation. The drug discovery pipeline for Chagas disease currently has few new clinical candidates, with high attrition rates an ongoing issue. To determine if the Trypanosoma cruzi strain utilised to assess in vitro compound activity impacts activity, a comparison of laboratory-adapted T. cruzi strains from differing geographical locations was undertaken for a selection of compounds with anti-T. cruzi activity. To minimise the possible effect of differences in experimental methodology, the same host cell and multiplicity of infection were utilised. To determine whether the compound exposure time influenced results, activity was determined following exposure for 48 and 72 h of incubation. To ascertain whether replication rates affected outcomes, comparative rates of replication of the T. cruzi strains were investigated, using the nucleoside analogue, 5-ethynyl-2'-deoxyuridine. Minimal differences in the in vitro activity of compounds between strains were observed following 48 h incubation, whereas significant differences were observed following 72 h incubation, in particular for the cytochrome P450 inhibitors tested and the cell cycle inhibitor, camptothecin. Thus, the use of panels of laboratory adapted strains in vitro may be dependent on the speed of action that is prioritised. For the identification of fast-acting compounds, an initial shorter duration assay using a single strain may be used. A longer incubation to identify compound activity may alternatively require profiling of compounds against multiple T. cruzi strains.

Sex-Specific miRNA Differences in Liquid Biopsies from Subjects with Solid Tumors and Healthy Controls

Dysregulation of epigenetic mechanisms has been recognized to play a crucial role in cancer development, but these mechanisms vary between sexes. Therefore, we focused on sex-specific differences in the context of cancer-based data from a recent study. A total of 12 cell-free DNA methylation targets in CpG-rich promoter regions and 48 miRNAs were analyzed by qPCR in plasma samples from 8 female and 7 male healthy controls as well as 48 female and 80 male subjects with solid tumors of the bladder, brain, colorectal region (CRC), lung, stomach, pancreas, and liver. Due to the small sample size in some groups and/or the non-balanced distribution of men and women, sex-specific differences were evaluated statistically only in healthy subjects, CRC, stomach or pancreas cancer patients, and all cancer subjects combined (n female/male-8/7, 14/14, 8/15, 6/6, 48/80, respectively). Several miRNAs with opposing expressions between the sexes were observed for healthy subjects (miR-17-5p, miR-26b-5p); CRC patients (miR-186-5p, miR-22-3p, miR-22-5p, miR-25-3p, miR-92a-3p, miR-16-5p); stomach cancer patients (miR-133a-3p, miR-22-5p); and all cancer patients combined (miR-126-3p, miR-21-5p, miR-92a-3p, miR-183-5p). Moreover, sex-specific correlations that were dependent on cancer stage were observed in women (miR-27a-3p) and men (miR-17-5p, miR-20a-5p). Our results indicate the complex and distinct role of epigenetic regulation, particularly miRNAs, depending not only on the health status but also on the sex of the patient. The same miRNAs could have diverse effects in different tissues and opposing effects between the biological sexes, which should be considered in biomarker research.

Increased FOXM1 Expression was Associated with the Prognosis and the Recruitment of Neutrophils in Endometrial Cancer

Background

Although the biological functions of Forkhead box protein M1 (FOXM1) were explored in a variety of cancer, to date, however, little attention has been paid to the situation of FOXM1 in EC endometrial cancer (EC).

Method

Bioinformatics analysis, including GEPIA, TIMER, cBioPortal, LinkedOmics, and STRING were used to analyze the FOXM1 gene expression, genetic alteration, and immune cell infiltration in EC. IHC staining, qPCR, cell viability, and migration assay were applied to identify the functions of FOXM1 in EC.

Results

FOXM1 was highly expressed in EC tissues and closely correlated with the prognosis of EC patients. FOXM1 knockdown inhibited EC cell proliferation and invasion as well as migration. FOXM1 genetic alteration was verified in EC patients. Coexpression network of FOXM1 indicated that it had roles in the EC cell cycle and the infiltration of immune cells in EC. Furthermore, bioinformatic and immunohistochemical analysis indicated that FOXM1 induced the increased CD276 expression and also enhanced the neutrophil recruitment in EC.

Conclusion

Our present study discovered a novel role of FOXM1 in EC, suggesting FOXM1 could be treated as a potential prognostic biomarker and immunotherapeutic target in EC diagnosis and treatment.

The Pan-Cancer Multi-Omics Landscape of FOXO Family Relevant to Clinical Outcome and Drug Resistance

The forkhead box O (FOXO) transcription factors (TFs) family are frequently mutated, deleted, or amplified in various human cancers, making them attractive candidates for therapy. However, their roles in pan-cancer remain unclear. Here, we evaluated the expression, prognostic value, mutation, methylation, and clinical features of four FOXO family genes (FOXO1, FOXO3, FOXO4, and FOXO6) in 33 types of cancers based on the Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) databases. We used a single sample gene set enrichment analysis (ssGSEA) algorithm to establish a novel index called "FOXOs score". Moreover, we investigated the association between the FOXOs score and tumor microenvironment (TME), the responses to multiple treatments, along with drug resistance. We found that the FOXO family genes participated in tumor progression and were related to the prognosis in various types of cancer. We calculated the FOXOs score and found that it was significantly correlated with multiple malignant pathways in pan-cancer, including Wnt/beta-catenin signaling, TGF-beta signaling, and hedgehog signaling. In addition, the FOXOs score was also associated with multiple immune-related characteristics. Furthermore, the FOXOs score was sensitive for predicting the efficacy of diverse treatments in multiple cancers, especially immunotherapy. In conclusion, FOXO family genes were vital in pan-cancer and were strongly correlated with the TME. A high FOXOs score indicated an excellent immune-activated TME and sensitivity to multiple treatments. Hence, the FOXOs score might potentially be used as a biomarker in patients with a tumor.

Microbes-mediated synthesis strategies of metal nanoparticles and their potential role in cancer therapeutics

Past few years have seen a paradigm shift towards ecofriendly, green and biological fabrication of metal nanoparticles (MNPs) for diverse nanomedicinal applications especially in cancer nanotheranostics. Besides, the well-known green synthesis methods of plant materials, the potential of the microbial world (bacteria, fungi, alga, etc.) in biofabrication is equally realized. Biomolecules and enzymes in the microbial cells are capable of catalyzing the biosynthesis process. These microbial derived inorganic nanoparticles have been frequently evaluated as potential agents in cancer therapies revealing exciting results. Through, cellular and molecular pathways, these microbial derived nanoparticles are capable of killing the cancer cells. Considering the recent developments in the anticancer applications of microbial derived inorganic MNPs, a dire need was felt to bring the available information to a single document. This manuscript reviews not only the mechanistic aspects of the microbial derived MNPs but also include the diverse mechanisms that governs their anticancer potential. Besides, an updated literature review is presented that includes studies of 2019-onwards.

PLP1 may serve as a potential diagnostic biomarker of uterine fibroids

Objective: We aim to identify the crucial genes or potential biomarkers associated with uterine fibroids (UFs), which may provide clinicians with evidence about the diagnostic biomarker of UFs and reveal the mechanism of its progression.

Methods: The gene expression and genome-wide DNA methylation profiles were obtained from Gene Expression Omnibus database (GEO). GSE45189, GSE31699, and GSE593 datasets were included. GEO2R and Venn diagrams were used to analyze the differentially expressed genes (DEGs) and extract the hub genes. Gene Ontology (GO) analysis was performed by the online tool Database for Annotation, Visualization, and Integrated Discovery (DAVID). The mRNA and protein expression of hub genes were validated by RT-qPCR, western blot, and immunohistochemistry. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value.

Results: We detected 22 DEGs between UFs and normal myometrium, which were enriched in cell maturation, apoptotic process, hypoxia, protein binding, and cytoplasm for cell composition. By finding the intersection of the data between differentially expressed mRNA and DNA methylation profiles, 3 hub genes were identified, including transmembrane 4 L six family member 1 (TM4SF1), TNF superfamily member 10 (TNFSF10), and proteolipid protein 1 (PLP1). PLP1 was validated to be up-regulated significantly in UFs both at mRNA and protein levels. The area under the ROC curve (AUC) of PLP1 was 0.956, with a sensitivity of 79.2% and a specificity of 100%. Conclusion: Overall, our results indicate that PLP1 may be a potential diagnostic biomarker for uterine fibroids.

Targeted demethylation of the SLC5A7 promotor inhibits colorectal cancer progression

Background

SLC5A7 (solute carrier family 5 member 7), also known as choline transporter 1 (CHT1), is downregulated in colorectal cancer (CRC) and functions as a tumor suppressor. However, the mechanisms underlying the inactivation of SLC5A7 in CRC remain to be elucidated.

Results

In the present study, two broad-spectrum demethylation agents (azacitidine and decitabine) employed to treat CRC cells significantly upregulated SLC5A7 expression. Further results based on the CRC cohort and TCGA database indicated that SLC5A7 promoter methylation inversely correlated with SLC5A7 expression, and the status of SLC5A7 promotor methylation showed a promising prognostic value for patients with CRC. Next, the dCas9-multiGCN4/scFv-TET1CD-based precision demethylation system was constructed, which could significantly and specifically promote SLC5A7 expression in CRC cells through sgRNA targeting the SLC5A7 promoter. Both in vitro and in vivo experiments demonstrated that targeted demethylation of SLC5A7 by dCas9-multiGCN4/scFv-TET1CD-sgSLC5A7 inhibited tumor growth by stabilizing p53 and regulating downstream targets.

Conclusions

Collectively, DNA promoter methylation caused inactivation of SLC5A7 in CRC, and targeted demethylation of SLC5A7 might be a therapeutic target for CRC and other cancers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01308-5.

Gut microbiota composition in colorectal cancer patients is genetically regulated

The risk of colorectal cancer (CRC) depends on environmental and genetic factors. Among environmental factors, an imbalance in the gut microbiota can increase CRC risk. Also, microbiota is influenced by host genetics. However, it is not known if germline variants influence CRC development by modulating microbiota composition. We investigated germline variants associated with the abundance of bacterial populations in the normal (non-involved) colorectal mucosa of 93 CRC patients and evaluated their possible role in disease. Using a multivariable linear regression, we assessed the association between germline variants identified by genome wide genotyping and bacteria abundances determined by 16S rRNA gene sequencing. We identified 37 germline variants associated with the abundance of the genera Bacteroides, Ruminococcus, Akkermansia, Faecalibacterium and Gemmiger and with alpha diversity. These variants are correlated with the expression of 58 genes involved in inflammatory responses, cell adhesion, apoptosis and barrier integrity. Genes and bacteria appear to be involved in the same processes. In fact, expression of the pro-inflammatory genes GAL, GSDMD and LY6H was correlated with the abundance of Bacteroides, which has pro-inflammatory properties; abundance of the anti-inflammatory genus Faecalibacterium correlated with expression of KAZN, with barrier-enhancing functions. Both the microbiota composition and local inflammation are regulated, at least partially, by the same germline variants. These variants may regulate the microenvironment in which bacteria grow and predispose to the development of cancer. Identification of these variants is the first step to identifying higher-risk individuals and proposing tailored preventive treatments that increase beneficial bacterial populations.

Advances and Trends in Omics Technology Development

The human history has witnessed the rapid development of technologies such as high-throughput sequencing and mass spectrometry that led to the concept of “omics” and methodological advancement in systematically interrogating a cellular system. Yet, the ever-growing types of molecules and regulatory mechanisms being discovered have been persistently transforming our understandings on the cellular machinery. This renders cell omics seemingly, like the universe, expand with no limit and our goal toward the complete harness of the cellular system merely impossible. Therefore, it is imperative to review what has been done and is being done to predict what can be done toward the translation of omics information to disease control with minimal cell perturbation. With a focus on the “four big omics,” i.e., genomics, transcriptomics, proteomics, metabolomics, we delineate hierarchies of these omics together with their epiomics and interactomics, and review technologies developed for interrogation. We predict, among others, redoxomics as an emerging omics layer that views cell decision toward the physiological or pathological state as a fine-tuned redox balance.

Highly Expressing SCARA5 Promotes Proliferation and Migration of Esophageal Squamous Cell Carcinoma

Background

Thrombospondin type 1 domain-containing 7A (THSD7A) was reported to play a procancer role in esophageal squamous cell carcinoma (ESCC). The aim of the study was to screen the downstream functional genes of THSD7A and explore their functions in ESCC, based on the reported research into THSD7A function and on gene microarrays.

Methods

We adopted quantitative reverse-transcription polymerase chain reaction (qRT-PCR) and Celigo high-content screening (HCS) technology to screen the downstream genes of THSD7A. The expression level of target genes was examined by PCR, western blot, and immunohistochemistry (IHC). The effects of these target genes on ESCC malignant biological behavior were performed in vivo and in vitro. The Kaplan-Meier (K-M) survival analysis and Cox regression were used to analyze the prognostic significance of target genes in ESCC patients. Experiments in the literature on liver cancer (LC) were repeated to verify the functions of these genes in different tumors. We further explored the cancer-promoting mechanism of target genes in ESCC by sequencing of the genes' exons.

Results

Scavenger receptor class A member 5 (SCARA5) was proved to be the downstream driving gene of THSD7A. SCARA5 promoted cell proliferation and migration but inhibited apoptosis in ESCC. IHC results confirmed that SCARA5 expression in ESCC exceeded that in normal tissues. The K-M survival analysis indicated that SCARA5 expression quantity was not related to prognosis, but tumor volume and T classification were both the independent prognostic factors. Repetition of experiments in LC in the literature confirmed that SCARA5 had exactly opposite functions in EC and LC.

Conclusion

SCARA5 was related to the development and occurrence of ESCC. Our findings suggested that it was a potentially diagnostic individualized therapeutic target for ESCC in the future and that its application could possibly be combined with that of upstream THSD7A gene.

Berberine Inhibits FOXM1 Dependent Transcriptional Regulation of POLE2 and Interferes With the Survival of Lung Adenocarcinoma

Background: Berberine is one of the most interesting and promising natural anticancer drugs. POLE2 is involved in many cellular functions such as DNA replication and is highly expressed in a variety of cancers. However, the specific molecular mechanism of berberine interfering with POLE2 expression in lung adenocarcinoma (LUAD) is still unknown to a great extent.

Method: The KEGG database (Release 91.0) and Gene Ontology (GO) category database were used for functional annotation of differentially expressed genes after berberine treatment. Reproducibility assessment using TCGA dataset. The biological functions of berberine in LUAD were investigated by a series of in vitro and in vivo experiments: MTT, colony formation, mouse xenograft and plasmid transfection. The molecular mechanisms of berberine were demonstrated by plasmid transfection, quantitative RT-PCR and Western blotting.

Result: The elevated expression of FOXM1 and the high enrichment of DNA replication pathway were confirmed in LUAD by microarray and TCGA analysis, and were positively correlated with poor prognosis. Functionally, berberine inhibited the proliferation and survival of LUAD cell lines in vitro and in vivo. Mechanistically, berberine treatment down regulated the expression of FOXM1which closely related to survival, survival related genes in Cell cycle and DNA replication pathway, and significantly down regulated the expression of survival related POLE2. Interestingly, we found that the transcription factor FOXM1 could act as a bridge between berberine and POLE2.

Conclusion: Berberine significantly inhibited LUAD progression via the FOXM1/POLE2, and FOXM1/POLE2 may act as a clinical prognostic factor and a therapeutic target for LUAD. Berberine may be used as a promising therapeutic candidate for LUAD patients.

An integrated analysis of DNA promoter methylation, microRNA regulation, and gene expression in gastric adenocarcinoma

Background

Gastric adenocarcinoma (GAC), a common type of gastric cancer, poses a significant public health threat worldwide. This study aimed to determine the transcriptional regulatory mechanisms of GAC.

Methods

HTSeq-FPKM raw data were obtained from The Cancer Genome Atlas Stomach Adenocarcinoma data collection. Subsequently, the limma package in R was used to identify differentially expressed genes (DEGs). Differentially methylated genes (DMGs), DEGs, and differentially expressed microRNAs (miRNAs) in normal, and tumor tissues of the same patients were screened and compared using R software tools. A functional enrichment analysis was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) for various DEGs, DMGs, promoter methylation, and miRNAs. DEG-specific methylation and transcription factors were analyzed using ENCODE ChIP-seq.

Results

DEGs were centrally modified by the histone trimethylation of lysine 27 on histone H3 (H3K27me3). Upstream transcription factors of DEGs were enriched in different ChIP-seq clusters, such as Forkhead Box M1, E2F Transcription Factor 4, and suppressor of zest 12. Integrated regulatory networks of DEGs, promoter methylation, and miRNAs were constructed. Two miRNAs (hsa-mir-1 and hsa-mir-133a) and four DEGs (A disintegrin and metalloproteinase domain 12, transcription factor AP-2 alpha, solute carrier family 5 member 7, and cadherin 19) separately played important roles in the integrated regulatory network. Therefore, these DEGs, DMGs, promoter methylation, and miRNAs may play an important role in GAC pathogenesis.

Conclusions

In summary, the present study results provide insights into the oncogenesis and progression of GAC, thus accelerating the development of novel targeted GAC therapies.

Tumor relevant protein functional interactions identified using bipartite graph analyses

An increased surge of -omics data for the diseases such as cancer allows for deriving insights into the affiliated protein interactions. We used bipartite network principles to build protein functional associations of the differentially regulated genes in 18 cancer types. This approach allowed us to combine expression data to functional associations in many cancers simultaneously. Further, graph centrality measures suggested the importance of upregulated genes such as BIRC5, UBE2C, BUB1B, KIF20A and PTH1R in cancer. Pathway analysis of the high centrality network nodes suggested the importance of the upregulation of cell cycle and replication associated proteins in cancer. Some of the downregulated high centrality proteins include actins, myosins and ATPase subunits. Among the transcription factors, mini-chromosome maintenance proteins (MCMs) and E2F family proteins appeared prominently in regulating many differentially regulated genes. The projected unipartite networks of the up and downregulated genes were comprised of 37,411 and 41,756 interactions, respectively. The conclusions obtained by collating these interactions revealed pan-cancer as well as subtype specific protein complexes and clusters. Therefore, we demonstrate that incorporating expression data from multiple cancers into bipartite graphs validates existing cancer associated mechanisms as well as directs to novel interactions and pathways.

Spatial variation in gene expression of Tasmanian devil facial tumors despite minimal host transcriptomic response to infection

BACKGROUND

Transmissible cancers lie at the intersection of oncology and infectious disease, two traditionally divergent fields for which gene expression studies are particularly useful for identifying the molecular basis of phenotypic variation. In oncology, transcriptomics studies, which characterize the expression of thousands of genes, have identified processes leading to heterogeneity in cancer phenotypes and individual prognoses. More generally, transcriptomics studies of infectious diseases characterize interactions between host, pathogen, and environment to better predict population-level outcomes. Tasmanian devils have been impacted dramatically by a transmissible cancer (devil facial tumor disease; DFTD) that has led to widespread population declines. Despite initial predictions of extinction, populations have persisted at low levels, due in part to heterogeneity in host responses, particularly between sexes. However, the processes underlying this variation remain unknown.

RESULTS

We sequenced transcriptomes from healthy and DFTD-infected devils, as well as DFTD tumors, to characterize host responses to DFTD infection, identify differing host-tumor molecular interactions between sexes, and investigate the extent to which tumor gene expression varies among host populations. We found minimal variation in gene expression of devil lip tissues, either with respect to DFTD infection status or sex. However, 4088 genes were differentially expressed in tumors among our sampling localities. Pathways that were up- or downregulated in DFTD tumors relative to normal tissues exhibited the same patterns of expression with greater intensity in tumors from localities that experienced DFTD for longer. No mRNA sequence variants were associated with expression variation.

CONCLUSIONS

Expression variation among localities may reflect morphological differences in tumors that alter ratios of normal-to-tumor cells within biopsies. Phenotypic variation in tumors may arise from environmental variation or differences in host immune response that were undetectable in lip biopsies, potentially reflecting variation in host-tumor coevolutionary relationships among sites that differ in the time since DFTD arrival.

Pan-cancer analysis reveals the oncogenic role of 3-hydroxy-3-methylglutaryl-CoA synthase 1

BACKGROUND

Emerging studies reveals that 3-hydroxy-3-methylglutaryl-CoA synthase 1 (HMGCS1) plays vital oncogenic roles in a broad spectrum of human cancers, but there is no pan-cancer evidence on the relationship between HMGCS1 and various tumor types.

AIM

To explore the potential role of HMGCS1 across various tumor types based on big clinical data.

METHODS

We conducted a pan-cancer analysis across more than 30 tumor types, based on the most comprehensive database available, including TCGA, GSCA, clinical proteomic tumor analysis consortium, Kaplan-Meier Plotter dataset, GEPIA2, TIMER2, STRING, and GDSC dataset.

RESULTS

HMGCS1 was highly expressed and negatively correlated with the prognosis in most cancer types. The infiltration levels of cancer associated fibroblast and CD8+ T-cell were closely associated with HMGCS1 expression. Amplification was the most common genetic alteration of HMGCS1 in different cancers, while the frequency of mutation was low. Besides, ACAT2 and MVD were closely correlated and bind to HMGCS1. Pathway enrichment analysis indicated that HMGCS1 was actively involved in steroid biosynthesis. Moreover, high HMGCS1 expression could reduce the sensitivity to most drugs in the GDSC dataset.

CONCLUSIONS

Our study revealed the potential oncogenic role of HMGCS1 in cancers.

Knockout of Putative Tumor Suppressor Aldh1l1 in Mice Reprograms Metabolism to Accelerate Growth of Tumors in a Diethylnitrosamine (DEN) Model of Liver Carcinogenesis

Simple Summary

Cancers often loose the enzyme of folate metabolism ALDH1L1. We proposed that such loss is advantageous for the malignant tumor growth and tested this hypothesis in mice proficient or deficient (gene knockout) in ALDH1L1 expression. Liver cancer in both groups was induced by injection of chemical carcinogen diethylnitrosamine. While the number of tumors observed in ALDH1L1 proficient and deficient mice was similar, tumors grew faster and to a larger size in the knockout mice. We conclude that the ALDH1L1 loss promotes liver tumor growth without affecting tumor initiation or multiplicity. Accelerated growth of tumors lacking the enzyme was linked to several metabolic pathways, which are beneficial for rapid proliferation.

Abstract

Cytosolic 10-formyltetrahydrofolate dehydrogenase (ALDH1L1) is commonly downregulated in human cancers through promoter methylation. We proposed that ALDH1L1 loss promotes malignant tumor growth. Here, we investigated the effect of the Aldh1l1 mouse knockout (Aldh1l1^−/−) on hepatocellular carcinoma using a chemical carcinogenesis model. Fifteen-day-old male Aldh1l1 knockout mice and their wild-type littermate controls (Aldh1l1^+/+) were injected intraperitoneally with 20 μg/g body weight of DEN (diethylnitrosamine). Mice were sacrificed 10, 20, 28, and 36 weeks post-DEN injection, and livers were examined for tumor multiplicity and size. We observed that while tumor multiplicity did not differ between Aldh1l1^−/− and Aldh1l1^+/+ animals, larger tumors grew in Aldh1l1^−/− compared to Aldh1l1^+/+ mice at 28 and 36 weeks. Profound differences between Aldh1l1^−/− and Aldh1l1^+/+ mice in the expression of inflammation-related genes were seen at 10 and 20 weeks. Of note, large tumors from wild-type mice showed a strong decrease of ALDH1L1 protein at 36 weeks. Metabolomic analysis of liver tissues at 20 weeks showed stronger differences in Aldh1l1^+/+ versus Aldh1l1^−/− metabotypes than at 10 weeks, which underscores metabolic pathways that respond to DEN in an ALDH1L1-dependent manner. Our study indicates that Aldh1l1 knockout promoted liver tumor growth without affecting tumor initiation or multiplicity.

PEG3 controls lipogenesis through ACLY

Peg3 (Paternally expressed gene 3) is an imprinted gene encoding a DNA-binding protein that is a well-known transcriptional repressor. Previous studies have shown that the mutant phenotypes of Peg3 are associated with the over-expression of genes involved in lipid metabolism. In the current study, we investigated four potential downstream genes of Peg3, which were identified through ChIP-seq data: Acly, Fasn, Idh1, and Hmgcr. In vivo binding of PEG3 to the promoter region of these key genes involved in lipogenesis was subsequently confirmed through individual ChIP experiments. We observed the opposite response of Acly expression levels against the variable gene dosages of Peg3, involving 0x, 1x, and 2x Peg3. This suggests the transcriptional repressor role of Peg3 in the expression levels of Acly. Another set of analyses showed a sex-biased response in the expression levels of Acly, Fasn, and Idh1 against 0x Peg3 with higher levels in female and lower levels in male mammary glands. These results overall highlight that Peg3 may be involved in regulating the expression levels of several key genes in adipogenesis.

FOXM1D potentiates PKM2-mediated tumor glycolysis and angiogenesis

Tumor growth, especially in the late stage, requires adequate nutrients and rich vasculature, in which PKM2 plays a convergent role. It has been reported that PKM2, together with FOXM1D, is upregulated in late-stage colorectal cancer and associated with metastasis; however, their underlying mechanism for promoting tumor progression remains elusive. Herein, we revealed that FOXM1D potentiates PKM2-mediated glycolysis and angiogenesis through multiple protein-protein interactions. In the presence of FBP, FOXM1D binds to tetrameric PKM2 and assembles a heterooctamer, restraining PKM2 metabolic activity by about a half and thereby promoting aerobic glycolysis. Furthermore, FOXM1D interacts with PKM2 and NF-κB and induces their nuclear translocation with the assistance of the nuclear transporter importin 4. Once in the nucleus, PKM2 and NF-κB complexes subsequently augment VEGFA transcription. The increased VEGFA is secreted extracellularly via exosomes, an event potentiated by the interaction of FOXM1 with VPS11, eventually promoting tumor angiogenesis. Based on these findings, our study provides another insight into the role of PKM2 in the regulation of glycolysis and angiogenesis.

Co-regulation and function of FOXM1/RHNO1 bidirectional genes in cancer

The FOXM1 transcription factor is an oncoprotein and a top biomarker of poor prognosis in human cancer. Overexpression and activation of FOXM1 is frequent in high-grade serous carcinoma (HGSC), the most common and lethal form of human ovarian cancer, and is linked to copy number gains at chromosome 12p13.33. We show that FOXM1 is co-amplified and co-expressed with RHNO1, a gene involved in the ATR-Chk1 signaling pathway that functions in the DNA replication stress response. We demonstrate that FOXM1 and RHNO1 are head-to-head (i.e., bidirectional) genes (BDG) regulated by a bidirectional promoter (BDP) (named F/R-BDP). FOXM1 and RHNO1 each promote oncogenic phenotypes in HGSC cells, including clonogenic growth, DNA homologous recombination repair, and poly-ADP ribosylase inhibitor resistance. FOXM1 and RHNO1 are one of the first examples of oncogenic BDG, and therapeutic targeting of FOXM1/RHNO1 BDG is a potential therapeutic approach for ovarian and other cancers.

Mining the plasma-proteome associated genes in patients with gastro-esophageal cancers for biomarker discovery

Gastro-esophageal (GE) cancers are one of the major causes of cancer-related death in the world. There is a need for novel biomarkers in the management of GE cancers, to yield predictive response to the available therapies. Our study aims to identify leading genes that are differentially regulated in patients with these cancers. We explored the expression data for those genes whose protein products can be detected in the plasma using the Cancer Genome Atlas to identify leading genes that are differentially regulated in patients with GE cancers. Our work predicted several candidates as potential biomarkers for distinct stages of GE cancers, including previously identified CST1, INHBA, STMN1, whose expression correlated with cancer recurrence, or resistance to adjuvant therapies or surgery. To define the predictive accuracy of these genes as possible biomarkers, we constructed a co-expression network and performed complex network analysis to measure the importance of the genes in terms of a ratio of closeness centrality (RCC). Furthermore, to measure the significance of these differentially regulated genes, we constructed an SVM classifier using machine learning approach and verified these genes by using receiver operator characteristic (ROC) curve as an evaluation metric. The area under the curve measure was > 0.9 for both the overexpressed and downregulated genes suggesting the potential use and reliability of these candidates as biomarkers. In summary, we identified leading differentially expressed genes in GE cancers that can be detected in the plasma proteome. These genes have potential to become diagnostic and therapeutic biomarkers for early detection of cancer, recurrence following surgery and for development of targeted treatment.

Proteomic Alterations Associated with Oral Cancer Patients with Tobacco Using Habits

Tobacco abuse is a major risk factor associated with the development of oral squamous cell carcinoma. Differences in molecular aberrations induced by tobacco exposure by chewing or smoking form are not well studied in case of oral cancer. We used tandem mass tag-based quantitative proteomic approach to delineate proteomic alterations in oral cancer patients based on their history of tobacco using habits (patients who chewed tobacco, patients who smoked tobacco, and those with no history of tobacco consumption). Our data identified distinct dysregulation of biological processes and pathways in each patient cohort. Bioinformatics analysis of dysregulated proteins identified in our proteomic study revealed dysregulation of collagen formation and antigen processing/presentation pathway in oral cancer patients who smoked tobacco, whereas proteins associated with the process of keratinization showed enrichment in patients who chewed tobacco. In addition, we identified overexpression of proteins involved in immune pathways and downregulation of muscle contraction-mediated signaling events in all three cohorts, irrespective of tobacco using habits. This study lays the groundwork for identification of protein markers that may aid in identification of high-risk patients for cancer development based on the history of tobacco exposure habits.

PEG3 mutation is associated with elevated tumor mutation burden and poor prognosis in breast cancer

Background: Breast cancer is the second most common malignancy in women and considered as a severe health burden. PEG3 mutations have been observed in several cancers. However, the associations of PEG3 mutation with tumor mutation burden (TMB) and prognosis in breast cancer have not been investigated. Methods: In our study, the somatic mutation data of 986 breast cancer patients from The Cancer Genome Atlas (TCGA) were analyzed. Results: It showed that PEG3 had a relatively high mutation rate (2%). After calculated the TMB in PEG3 mutant and PEG3 wild-type groups, we found the TMB value was significantly higher in PEG3 mutant samples than that in PEG3 wild-type samples (P = 5.6e-07), which was independent of the confounding factors including age, stage, mutations of BRCA1, BRCA2 and POLE (odd ratio, 0.45; 95% CI, 0.20–0.98; P=0.044). Survival analysis revealed that PEG3 mutant samples had inferior survival outcome compared with the PEG3 wild-type samples after adjusted for the confounding factors above (hazard ratio, 0.27; 95% CI: 0.12–0.57; P<0.001). Conclusion: These results illustrated that PEG3 mutation was associated with high TMB and inferior prognosis, suggesting PEG3 mutation might play a guiding role in prognosis prediction and immunotherapy selection in breast cancer.

Gene Co-Expression Analysis of Human RNASEH2A Reveals Functional Networks Associated with DNA Replication, DNA Damage Response, and Cell Cycle Regulation

Simple Summary

RNASEH2A is the catalytic subunit of the ribonuclease (RNase) H2 ternary complex that plays an important role in maintaining DNA stability in cells. Recent studies have shown that the RNASEH2A subunit alone is highly expressed in certain cancer cell types. Via a series of bioinformatics approaches, we found that RNASEH2A is highly expressed in human proliferative tissues and many cancers. Our analyses reveal a possible involvement of RNASEH2A in cell cycle regulation in addition to its well established role in DNA replication and DNA repair. Our findings underscore that RNASEH2A could serve as a biomarker for cancer diagnosis and a therapeutic target.

Abstract

Ribonuclease (RNase) H2 is a key enzyme for the removal of RNA found in DNA-RNA hybrids, playing a fundamental role in biological processes such as DNA replication, telomere maintenance, and DNA damage repair. RNase H2 is a trimer composed of three subunits, RNASEH2A being the catalytic subunit. RNASEH2A expression levels have been shown to be upregulated in transformed and cancer cells. In this study, we used a bioinformatics approach to identify RNASEH2A co-expressed genes in different human tissues to underscore biological processes associated with RNASEH2A expression. Our analysis shows functional networks for RNASEH2A involvement such as DNA replication and DNA damage response and a novel putative functional network of cell cycle regulation. Further bioinformatics investigation showed increased gene expression in different types of actively cycling cells and tissues, particularly in several cancers, supporting a biological role for RNASEH2A but not for the other two subunits of RNase H2 in cell proliferation. Mass spectrometry analysis of RNASEH2A-bound proteins identified players functioning in cell cycle regulation. Additional bioinformatic analysis showed that RNASEH2A correlates with cancer progression and cell cycle related genes in Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA) Pan Cancer datasets and supported our mass spectrometry findings.

One-carbon metabolism in cancer cells: a critical review based on a core model of central metabolism

One-carbon metabolism (1C-metabolism), also called folate metabolism because the carbon group is attached to folate-derived tetrahydrofolate, is crucial in metabolism. It is at the heart of several essential syntheses, particularly those of purine and thymidylate. After a short reminder of the organization of 1C-metabolism, I list its salient features as reported in the literature. Then, using flux balance analysis, a core model of central metabolism and the flux constraints for an 'average cancer cell metabolism', I explore the fundamentals underlying 1C-metabolism and its relationships with the rest of metabolism. Some unreported properties of 1C-metabolism emerge, such as its potential roles in mitochondrial NADH exchange with cytosolic NADPH, participation in NADH recycling, and optimization of cell proliferation.

USP2-Related Cellular Signaling and Consequent Pathophysiological Outcomes

Ubiquitin specific protease (USP) 2 is a multifunctional deubiquitinating enzyme. USP2 modulates cell cycle progression, and therefore carcinogenesis, via the deubiquitination of cyclins and Aurora-A. Other tumorigenic molecules, including epidermal growth factor and fatty acid synthase, are also targets for USP2. USP2 additionally prevents p53 signaling. On the other hand, USP2 functions as a key component of the CLOCK/BMAL1 complex and participates in rhythmic gene expression in the suprachiasmatic nucleus and liver. USP2 variants influence energy metabolism by controlling hepatic gluconeogenesis, hepatic cholesterol uptake, adipose tissue inflammation, and subsequent systemic insulin sensitivity. USP2 also has the potential to promote surface expression of ion channels in renal and intestinal epithelial cells. In addition to modifying the production of cytokines in immune cells, USP2 also modulates the signaling molecules that are involved in cytokine signaling in the target cells. Usp2 knockout mice exhibit changes in locomotion and male fertility, which suggest roles for USP2 in the central nervous system and male genital tract, respectively. In this review, we summarize the cellular events with USP2 contributions and list the signaling molecules that are upstream or downstream of USP2. Additionally, we describe phenotypic differences found in the in vitro and in vivo experimental models.

Cancer, Retrogenes, and Evolution

This review summarizes the knowledge about retrogenes in the context of cancer and evolution. The retroposition, in which the processed mRNA from parental genes undergoes reverse transcription and the resulting cDNA is integrated back into the genome, results in additional copies of existing genes. Despite the initial misconception, retroposition-derived copies can become functional, and due to their role in the molecular evolution of genomes, they have been named the “seeds of evolution”. It is convincing that retrogenes, as important elements involved in the evolution of species, also take part in the evolution of neoplastic tumors at the cell and species levels. The occurrence of specific “resistance mechanisms” to neoplastic transformation in some species has been noted. This phenomenon has been related to additional gene copies, including retrogenes. In addition, the role of retrogenes in the evolution of tumors has been described. Retrogene expression correlates with the occurrence of specific cancer subtypes, their stages, and their response to therapy. Phylogenetic insights into retrogenes show that most cancer-related retrocopies arose in the lineage of primates, and the number of identified cancer-related retrogenes demonstrates that these duplicates are quite important players in human carcinogenesis.

Circulating Non-Coding RNAs in Head and Neck Cancer: Roles in Diagnosis, Prognosis, and Therapy Monitoring

Head and neck cancer (HNC), the seventh most common form of cancer worldwide, is a group of epithelial malignancies affecting sites in the upper aerodigestive tract. The 5-year overall survival for patients with HNC has stayed around 40–50% for decades, with mortality being attributable mainly to late diagnosis and recurrence. Recently, non-coding RNAs, including tRNA halves, YRNA fragments, microRNAs (miRNAs), and long non-coding RNAs (lncRNAs), have been identified in the blood and saliva of patients diagnosed with HNC. These observations have recently fueled the study of their potential use in early detection, diagnosis, and risk assessment. The present review focuses on recent insights and the potential impact that circulating non-coding RNA evaluation may have on clinical decision-making in the management of HNC.

RAS-mediated suppression of PAR3 and its effects on SCC initiation and tissue architecture occur independently of hyperplasia

Proper epithelial development and homeostasis depends on strict control of oriented cell division. Current evidence shows that this process is regulated by intrinsic polarity factors and external spatial cues. Owing to the lack of an appropriate model system that can recapitulate the architecture of the skin, deregulation of spindle orientation in human epithelial carcinoma has never been investigated. Here, using an inducible model of human squamous cell carcinoma (SCC), we demonstrate that RAS-dependent suppression of PAR3 (encoded by PARD3) accelerates epithelial disorganization during early tumorigenesis. Diminished PAR3 led to loss of E-cadherin-mediated cell adhesion, which in turn contributed to misoriented cell division. Pharmacological inhibition of the MAPK pathway downstream of RAS activation reversed the defects in PAR3 expression, E-cadherin-mediated cell adhesion and mitotic spindle orientation. Thus, temporal analysis of human neoplasia provides a powerful approach to study cellular and molecular transformations during early oncogenesis, which allowed identification of PAR3 as a critical regulator of tissue architecture during initial human SCC development.

Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

BACKGROUND

Lung cancer is the number one cancer killer in the world with more than 142,670 deaths estimated in the United States alone in the year 2019. Consequently, there is an overreaching need to identify the key biomarkers for lung cancer. The aim of this study is to computationally identify biomarker genes for lung cancer that can aid in its diagnosis and treatment. The gene expression profiles of two different types of studies, namely non-treatment and treatment, are considered for discovering biomarker genes. In non-treatment studies healthy samples are control and cancer samples are cases. Whereas, in treatment studies, controls are cancer cell lines without treatment and cases are cancer cell lines with treatment.

RESULTS

The Differentially Expressed Genes (DEGs) for lung cancer were isolated from Gene Expression Omnibus (GEO) database using R software tool GEO2R. A total of 407 DEGs (254 upregulated and 153 downregulated) from non-treatment studies and 547 DEGs (133 upregulated and 414 downregulated) from treatment studies were isolated. Two Cytoscape apps, namely, CytoHubba and MCODE, were used for identifying biomarker genes from functional networks developed using DEG genes. This study discovered two distinct sets of biomarker genes - one from non-treatment studies and the other from treatment studies, each set containing 16 genes. Survival analysis results show that most non-treatment biomarker genes have prognostic capability by indicating low-expression groups have higher chance of survival compare to high-expression groups. Whereas, most treatment biomarkers have prognostic capability by indicating high-expression groups have higher chance of survival compare to low-expression groups.

CONCLUSION

A computational framework is developed to identify biomarker genes for lung cancer using gene expression profiles. Two different types of studies - non-treatment and treatment - are considered for experiment. Most of the biomarker genes from non-treatment studies are part of mitosis and play vital role in DNA repair and cell-cycle regulation. Whereas, most of the biomarker genes from treatment studies are associated to ubiquitination and cellular response to stress. This study discovered a list of biomarkers, which would help experimental scientists to design a lab experiment for further exploration of detail dynamics of lung cancer development.

Drug discovery and chemical probing in Drosophila

Flies are increasingly utilized in drug discovery and chemical probing in vivo, which are novel technologies complementary to genetic probing in fundamental biological studies. Excellent genetic conservation, small size, short generation time, and over one hundred years of genetics make Drosophila an attractive model for rapid assay readout and use of analytical amounts of compound, enabling the experimental iterations needed in early drug development at a fraction of time and costs. Here, we describe an effective drug-testing pipeline using adult flies that can be easily implemented to study several disease models and different genotypes to discover novel molecular insight, probes, quality lead compounds, and develop novel prototype drugs.

Modeling Basins of Attraction for Breast Cancer Using Hopfield Networks

Cancer is a genetic disease for which traditional treatments cause harmful side effects. After two decades of genomics technological breakthroughs, personalized medicine is being used to improve treatment outcomes and mitigate side effects. In mathematical modeling, it has been proposed that cancer matches an attractor in Waddington's epigenetic landscape. The use of Hopfield networks is an attractive modeling approach because it requires neither previous biological knowledge about protein-protein interactions nor kinetic parameters. In this report, Hopfield network modeling was used to analyze bulk RNA-Seq data of paired breast tumor and control samples from 70 patients. We characterized the control and tumor attractors with respect to their size and potential energy and correlated the Euclidean distances between the tumor samples and the control attractor with their corresponding clinical data. In addition, we developed a protocol that outlines the key genes involved in tumor state stability. We found that the tumor basin of attraction is larger than that of the control and that tumor samples are associated with a more substantial negative energy than control samples, which is in agreement with previous reports. Moreover, we found a negative correlation between the Euclidean distances from tumor samples to the control attractor and patient overall survival. The ascending order of each node's density in the weight matrix and the descending order of the number of patients that have the target active only in the tumor sample were the parameters that withdrew more tumor samples from the tumor basin of attraction with fewer gene inhibitions. The combinations of therapeutic targets were specific to each patient. We performed an initial validation through simulation of trastuzumab treatment effects in HER2+ breast cancer samples. For that, we built an energy landscape composed of single-cell and bulk RNA-Seq data from trastuzumab-treated and non-treated HER2+ samples. The trajectory from the non-treated bulk sample toward the treated bulk sample was inferred through the perturbation of differentially expressed genes between these samples. Among them, we characterized key genes involved in the trastuzumab response according to the literature.

Cytochrome P450 4B1 (CYP4B1) as a target in cancer treatment

Cytochrome P450 4B1 (CYP4B1) plays crucial roles in biotransforming of xenobiotics. Its predominant extrahepatic expression has been associated with certain tissue-specific toxicities. However, the expressions of CYP4B1 in various cancers and hence their potential roles in cancer development were inclusive. In this work, existing knowledge on expression and regulation of CYP4B1 gene and protein, catalysis of CYP4B1, association of CYP4B1 with cancers, contradicting findings about human CYP4B1 activities as well as the employing CYP4B1 in suicide gene approach for cancer treatment were reviewed. To date, it appears that there is a wide spectrum of tissue distribution of CYP4B1 with lungs as the predominant sites. Several nuclear receptors are possibly responsible for regulating its gene expression. The involvement of CYP4B1 in cancer was considered via activation of procarcinogens and neovascularization. However, human CYP4B1 was found to be inactive due to a substitution of proline with serine at position 427. Suicide gene approach combining reengineered CYP4B1 and prodrug 4-ipomeanol (4-IPO) has shown a promising potential for targeted cancer therapy. Further studies should focus on the verification of human CYP4B1 catalytic activities. More compounds with similar structure as 4-IPO should be tested to identify more alternative agents for the suicide gene approach in cancer treatment.

More Than One HMG-CoA Lyase: The Classical Mitochondrial Enzyme Plus the Peroxisomal and the Cytosolic Ones

There are three human enzymes with HMG-CoA lyase activity that are able to synthesize ketone bodies in different subcellular compartments. The mitochondrial HMG-CoA lyase was the first to be described, and catalyzes the cleavage of 3-hydroxy-3-methylglutaryl CoA to acetoacetate and acetyl-CoA, the common final step in ketogenesis and leucine catabolism. This protein is mainly expressed in the liver and its function is metabolic, since it produces ketone bodies as energetic fuels when glucose levels are low. Another isoform is encoded by the same gene for the mitochondrial HMG-CoA lyase (HMGCL), but it is located in peroxisomes. The last HMG-CoA lyase to be described is encoded by a different gene, HMGCLL1, and is located in the cytosolic side of the endoplasmic reticulum membrane. Some activity assays and tissue distribution of this enzyme have shown the brain and lung as key tissues for studying its function. Although the roles of the peroxisomal and cytosolic HMG-CoA lyases remain unknown, recent studies highlight the role of ketone bodies in metabolic remodeling, homeostasis, and signaling, providing new insights into the molecular and cellular function of these enzymes.

The Role of Single-Nucleotide Polymorphisms in the Function of Candidate Tumor Suppressor ALDH1L1

Folate (vitamin B9) is a common name for a group of coenzymes that function as carriers of chemical moieties called one-carbon groups in numerous biochemical reactions. The combination of these folate-dependent reactions constitutes one-carbon metabolism, the name synonymous to folate metabolism. Folate coenzymes and associated metabolic pathways are vital for cellular homeostasis due to their key roles in nucleic acid biosynthesis, DNA repair, methylation processes, amino acid biogenesis, and energy balance. Folate is an essential nutrient because humans are unable to synthesize this coenzyme and must obtain it from the diet. Insufficient folate intake can ultimately increase risk of certain diseases, most notably neural tube defects. More than 20 enzymes are known to participate in folate metabolism. Single-nucleotide polymorphisms (SNPs) in genes encoding for folate enzymes are associated with altered metabolism, changes in DNA methylation and modified risk for the development of human pathologies including cardiovascular diseases, birth defects, and cancer. ALDH1L1, one of the folate-metabolizing enzymes, serves a regulatory function in folate metabolism restricting the flux of one-carbon groups through biosynthetic processes. Numerous studies have established that ALDH1L1 is often silenced or strongly down-regulated in cancers. The loss of ALDH1L1 protein positively correlates with the occurrence of malignant tumors and tumor aggressiveness, hence the enzyme is viewed as a candidate tumor suppressor. ALDH1L1 has much higher frequency of non-synonymous exonic SNPs than most other genes for folate enzymes. Common SNPs at the polymorphic loci rs3796191, rs2886059, rs9282691, rs2276724, rs1127717, and rs4646750 in ALDH1L1 exons characterize more than 97% of Europeans while additional common variants are found in other ethnic populations. The effects of these SNPs on the enzyme is not clear but studies indicate that some coding and non-coding ALDH1L1 SNPs are associated with altered risk of certain cancer types and it is also likely that specific haplotypes define the metabolic response to dietary folate. This review discusses the role of ALDH1L1 in folate metabolism and etiology of diseases with the focus on non-synonymous coding ALDH1L1 SNPs and their effects on the enzyme structure/function, metabolic role and association with cancer.

The role of PEG3 in the occurrence and prognosis of colon cancer

Purpose

Imprinted genes are often identified as key players in the etiology and prognosis of many tumors; however, the role they play in colon cancer remains unclear. Along with the development of big data analysis came the discovery of a wealth of genetic prognostic factors, like microsatellite instability for colon cancer, which need to be taken into consideration when evaluating new biomarkers for the disease.

Methods

We systematically mined public databases to find recurrence free survival (RFS)-related imprinted genes for colon cancer patients on the mRNA level by univariate and multivariate survival analyses. We then investigated the association of methylation status and microRNA expression of the targeted imprinted genes with survival rate of colon cancer patients. Lastly, in a clinical study we used qRT-PCR and immunohistochemistry to quantify mRNA and protein expression of the imprinted genes that related to RFS in our bioinformatics screening, respectively, in 20 tumor tissues compared to paired adjacent tissues.

Results

The results show that paternally expressed gene 3 (PEG3) is the only imprinted gene related to colon cancer patient prognosis on the mRNA level in our datasets, and high mRNA expression of PEG3 is associated with a poor prognosis. Furthermore, the methylation beta value of cg13960339, as well as the expression of 4 microRNAs, negatively correlated with PEG3 mRNA level and were correlated with the prognosis of colon cancer patients. Moreover, the expression of PEG3 mRNA in colon cancer is significantly lower, but PEG3 protein expression is significantly higher compared to that in normal tissues.

Conclusion

PEG3 is likely associated with the progression and prognosis of colon cancer.

Genetic interaction analysis among oncogenesis-related genes revealed novel genes and networks in lung cancer development

The development of cancer is driven by the accumulation of many oncogenesis-related genetic alterations and tumorigenesis is triggered by complex networks of involved genes rather than independent actions. To explore the epistasis existing among oncogenesis-related genes in lung cancer development, we conducted pairwise genetic interaction analyses among 35,031 SNPs from 2027 oncogenesis-related genes. The genotypes from three independent genome-wide association studies including a total of 24,037 lung cancer patients and 20,401 healthy controls with Caucasian ancestry were analyzed in the study. Using a two-stage study design including discovery and replication studies, and stringent Bonferroni correction for multiple statistical analysis, we identified significant genetic interactions between SNPs in RGL1:RAD51B (OR=0.44, p value=3.27x10-11 in overall lung cancer and OR=0.41, p value=9.71x10-11 in non-small cell lung cancer), SYNE1:RNF43 (OR=0.73, p value=1.01x10-12 in adenocarcinoma) and FHIT:TSPAN8 (OR=1.82, p value=7.62x10-11 in squamous cell carcinoma) in our analysis. None of these genes have been identified from previous main effect association studies in lung cancer. Further eQTL gene expression analysis in lung tissues provided information supporting the functional role of the identified epistasis in lung tumorigenesis. Gene set enrichment analysis revealed potential pathways and gene networks underlying molecular mechanisms in overall lung cancer as well as histology subtypes development. Our results provide evidence that genetic interactions between oncogenesis-related genes play an important role in lung tumorigenesis and epistasis analysis, combined with functional annotation, provides a valuable tool for uncovering functional novel susceptibility genes that contribute to lung cancer development by interacting with other modifier genes.

Pan-Cancer Analyses Reveal Genomic Features of FOXM1 Overexpression in Cancer

FOXM1 is frequently overexpressed in cancer, but this has not been studied in a comprehensive manner. We utilized genotype-tissue expression (GTEx) normal and The Cancer Genome Atlas (TCGA) tumor data to define FOXM1 expression, including its isoforms, and to determine the genetic alterations that promote FOXM1 expression in cancer. Additionally, we used human fallopian tube epithelial (FTE) cells to dissect the role of Retinoblastoma (Rb)-E2F and Cyclin E1 in FOXM1 regulation, and a novel human embryonic kidney cell (HEK293T) CRISPR FOXM1 knockout model to define isoform-specific transcriptional programs. FOXM1 expression, at the mRNA and protein level, was significantly elevated in tumors with FOXM1 amplification, p53 inactivation, and Rb-E2F deregulation. FOXM1 expression was remarkably high in testicular germ cell tumors (TGCT), high-grade serous ovarian cancer (HGSC), and basal breast cancer (BBC). FOXM1 expression in cancer was associated with genomic instability, as measured using aneuploidy signatures. FTE models confirmed a role for Rb-E2F signaling in FOXM1 regulation and in particular identified Cyclin E1 as a novel inducer of FOXM1 expression. Among the three FOXM1 isoforms, FOXM1c showed the highest expression in normal and tumor tissues and cancer cell lines. The CRISPR knockout model demonstrated that FOXM1b and FOXM1c are transcriptionally active, while FOXM1a is not. Finally, we were unable to confirm the existence of a FOXM1 auto-regulatory loop. This study provides significant and novel information regarding the frequency, causes, and consequences of elevated FOXM1 expression in human cancer.

Loss of ALDH1L1 folate enzyme confers a selective metabolic advantage for tumor progression

ALDH1L1 (cytosolic 10-formyltetrahydrofolate dehydrogenase) is the enzyme in folate metabolism commonly downregulated in human cancers. One of the mechanisms of the enzyme downregulation is methylation of the promoter of the ALDH1L1 gene. Recent studies underscored ALDH1L1 as a candidate tumor suppressor and potential marker of aggressive cancers. In agreement with the ALDH1L1 loss in cancer, its re-expression leads to inhibition of proliferation and to apoptosis, but also affects migration and invasion of cancer cells through a specific folate-dependent mechanism involved in invasive phenotype. A growing body of literature evaluated the prognostic value of ALDH1L1 expression for cancer disease, the regulatory role of the enzyme in cellular proliferation, and associated metabolic and signaling cellular responses. Overall, there is a strong indication that the ALDH1L1 silencing provides metabolic advantage for tumor progression at a later stage when unlimited proliferation and enhanced motility become critical processes for the tumor expansion. Whether the ALDH1L1 loss is involved in tumor initiation is still an open question.

Onco-Multi-OMICS Approach: A New Frontier in Cancer Research

The acquisition of cancer hallmarks requires molecular alterations at multiple levels including genome, epigenome, transcriptome, proteome, and metabolome. In the past decade, numerous attempts have been made to untangle the molecular mechanisms of carcinogenesis involving single OMICS approaches such as scanning the genome for cancer-specific mutations and identifying altered epigenetic-landscapes within cancer cells or by exploring the differential expression of mRNA and protein through transcriptomics and proteomics techniques, respectively. While these single-level OMICS approaches have contributed towards the identification of cancer-specific mutations, epigenetic alterations, and molecular subtyping of tumors based on gene/protein-expression, they lack the resolving-power to establish the casual relationship between molecular signatures and the phenotypic manifestation of cancer hallmarks. In contrast, the multi-OMICS approaches involving the interrogation of the cancer cells/tissues in multiple dimensions have the potential to uncover the intricate molecular mechanism underlying different phenotypic manifestations of cancer hallmarks such as metastasis and angiogenesis. Moreover, multi-OMICS approaches can be used to dissect the cellular response to chemo- or immunotherapy as well as discover molecular candidates with diagnostic/prognostic value. In this review, we focused on the applications of different multi-OMICS approaches in the field of cancer research and discussed how these approaches are shaping the field of personalized oncomedicine. We have highlighted pioneering studies from “The Cancer Genome Atlas (TCGA)” consortium encompassing integrated OMICS analysis of over 11,000 tumors from 33 most prevalent forms of cancer. Accumulation of huge cancer-specific multi-OMICS data in repositories like TCGA provides a unique opportunity for the systems biology approach to tackle the complexity of cancer cells through the unification of experimental data and computational/mathematical models. In future, systems biology based approach is likely to predict the phenotypic changes of cancer cells upon chemo-/immunotherapy treatment. This review is sought to encourage investigators to bring these different approaches together for interrogating cancer at molecular, cellular, and systems levels.