A metabolism-related gene signature for predicting the prognosis and therapeutic responses in patients with hepatocellular carcinoma

Structural basis of tRNA recognition by the m3C RNA methyltransferase METTL6 in complex with SerRS seryl-tRNA synthetase

Methylation of cytosine 32 in the anticodon loop of tRNAs to 3-methylcytosine (m3C) is crucial for cellular translation fidelity. Misregulation of the RNA methyltransferases setting this modification can cause aggressive cancers and metabolic disturbances. Here, we report the cryo-electron microscopy structure of the human m3C tRNA methyltransferase METTL6 in complex with seryl-tRNA synthetase (SerRS) and their common substrate tRNASer. Through the complex structure, we identify the tRNA-binding domain of METTL6. We show that SerRS acts as the tRNASer substrate selection factor for METTL6. We demonstrate that SerRS augments the methylation activity of METTL6 and that direct contacts between METTL6 and SerRS are necessary for efficient tRNASer methylation. Finally, on the basis of the structure of METTL6 in complex with SerRS and tRNASer, we postulate a universal tRNA-binding mode for m3C RNA methyltransferases, including METTL2 and METTL8, suggesting that these mammalian paralogs use similar ways to engage their respective tRNA substrates and cofactors.

Construction and validation of a prognostic model for overall survival time of patients with ovarian cancer by metabolism-related genes

BACKGROUND

Ovarian cancer is a female-specific malignancy with high morbidity and mortality. The metabolic reprogramming of tumor cells is closely related to the biological behavior of tumors.

METHODS

The prognostic signature of the metabolism-related gene (MRGs) was established by LASSO-Cox regression analysis. The prognostic signature of MRGs was also prognosticated in each clinical subgroup. These genes were subjected to functional enrichment analysis and tissue expression exploration. Analysis of the MRG prognostic signature in terms of immune cell infiltration and antitumor drug susceptibility was also performed.

RESULTS

A MRG prognostic signature including 21 genes was established and validated. Most of the 21 MRGs were expressed at different levels in ovarian cancer than in normal ovarian tissue. The enrichment analysis suggested that MRGs were involved in lipid metabolism, membrane organization, and molecular binding. The MRG prognostic signature demonstrated the predictive value of overall survival time in various clinical subgroups. The monocyte, NKT, Tgd and Tex cell scores showed differences between the groups with high- and low-risk score. The antineoplastic drug analysis we performed provided information on ovarian cancer drug therapy and drug resistance. In vitro experiments verified that PLCH1 in 21 MRGs can regulate the apoptosis and proliferation of ovarian cancer cells.

CONCLUSION

This metabolism-related prognostic signature was a potential prognostic factor in patients with ovarian cancer, demonstrating high stability and accuracy.

A pyroptosis-related lncRNA-based prognostic index for hepatocellular carcinoma by relative expression orderings

Background

Hepatocellular carcinoma (HCC) is an invasive malignant tumor, and pyroptosis makes an important contribution to the pathology and progression of liver cancer. Many prognostic models have been proposed for HCC based on the quantitative expression level of candidate genes, which are unsuitable for clinical application due to their vulnerability against experimental batch effects. The aim of this study was to develop a novel pyroptosis-related long non-coding RNA (lncRNA)-based prognostic index (PLPI) for HCC based on relative expression orderings (REOs).

Methods

Firstly, the pyroptosis-related lncRNAs were identified through the Wilcoxon rank-sum test and gene co-expression analyses. Then, the novel prognostic model PLPI was constructed by pyroptosis-related lncRNA pairs, which were identified by multiple machine learning algorithms. Gene set enrichment, somatic mutation, and drug sensitivity analyses were conducted to measure the differences between high- and low-risk patients. Multiple immune analyses were used to explore the association between PLPI and the immunological microenvironment.

Results

In this study, a novel prognostic model PLPI based on 10 pyroptosis-related lncRNA pairs was constructed, which was proven to be an independent prognostic risk factor. The receiver operating characteristic (ROC) curves showed that the model had a good prognostic ability in the training, testing, and external set, respectively [5-year area under the curve (AUC) =0.73, 5-year AUC =0.81, 4-year AUC =0.79]. The results of survival, somatic mutation, and immune analyses showed that the patients in the low-risk group had a better prognosis, lower rates of somatic mutation, and better immune cell infiltration. Personalized chemotherapeutic drugs were also identified for the patients with HCC.

Conclusions

The novel PLPI not only greatly predicted the prognosis of patients with HCC but could also offer novel ideas and approaches for the therapeutic management of HCC.

Prognostic and metabolic characteristics of a novel cuproptosis-related signature in patients with hepatocellular carcinoma

Cuproptosis is a novel discovered mode of programmed cell death. To identify the molecular regulatory patterns related to cuproptosis, this study was designed for exploring the correlation between cuproptosis-related genes (CRGs) and the prognosis, metabolism, and treatment of hepatocellular carcinoma (HCC). Cancer Genome Atlas (TCGA) database was used to screen 363 HCC samples, which were categorized into 2 clusters based on the expression of CRGs. Survival analysis demonstrated that overall survival (OS) was better in Cluster 1 than Cluster 2 which might to be relevant to differences in metabolic based on functional analysis. With LASSO regression analysis and univariate COX regression, 8 prognosis-related genes were screened, a differently expressed genes (DEGs) were then constructed (HCC patients' DEGs)-based signature. The signature's stability was also validated in the 2 independent cohorts and test cohorts (GSE14520, HCC dataset in PCAWG). The 1-year, 3-year, and 5-year area under the curve (AUC) were 0.756, 0.706, and 0.722, respectively. The signature could also well predict the response to chemotherapy, targeted and transcatheter arterial chemoembolization (TACE) by providing a risk score. Moreover, the correlation was uncovered by the research between the metabolism and risk score. In conclusion, a unique cuproptosis-related signature that be capable of predicting patients' prognosis with HCC, and offered valuable insights into chemotherapy, TACE and targeted therapies for these patients has been developed.

Unleashing the potential of gene signatures as prognostic and predictive tools: A step closer to personalized medicine in hepatocellular carcinoma (HCC)

Hepatocellular carcinoma (HCC) is one of the growing malignancies globally, affecting a myriad of people and causing numerous cancer-related deaths. Despite therapeutic improvements in treatment strategies over the past decades, HCC still remains one of the leading causes of person-years of life lost. Numerous studies have been conducted to assess the characteristics of HCC with the aim of predicting its prognosis and responsiveness to treatment. However, the identified biomarkers have shown limited sensitivity, and the translation of these findings into clinical practice has faced challenges. The development of sequencing techniques has facilitated the exploration of a wide range of genes, leading to the emergence of gene signatures. Although several studies assessed differentially expressed genes in normal and HCC tissues to find the unique gene signature with prognostic value, to date, no study has reviewed the task, and to the best of our knowledge, this review represents the first comprehensive analysis of relevant studies in HCC. Most gene signatures focused on immune-related genes, while others investigated genes related to metabolism, autophagy, and apoptosis. Even though no identical gene signatures were found, NDRG1, SPP1, BIRC5, and NR0B1 were the most extensively studied genes with prognostic value. Finally, despite challenges such as the lack of consistent patterns in gene signatures, we believe that comprehensive analysis of pertinent gene signatures will bring us a step closer to personalized medicine in HCC, where treatment strategies can be tailored to individual patients based on their unique molecular profiles.

POLR3G promotes EMT via PI3K/AKT signaling pathway in bladder cancer

RNA Polymerase III Subunit G (POLR3G) promotes tumorigenesis, metastasis, cancer stemness, and chemoresistance of breast cancer and lung cancer; however, its biological function in bladder cancer (BLCA) remains unclear. Through bioinformatic analyses, we found that POLR3G expression was significantly elevated in BLCA tumor tissues and was associated with decreased survival. Multivariate Cox analysis indicated that POLR3G could serve as an independent prognostic risk factor. Our functional investigations revealed that POLR3G deficiency resulted in reduced migration and invasion of BLCA cells both in vitro and in vivo. Additionally, the expressions of EMT-related mesenchymal markers were also downregulated in POLR3G knockdown cells. Mechanistically, we showed that POLR3G could activate the PI3K/AKT signaling pathway. Inhibition of this pathway with LY294002 reduced the enhanced migration and invasion of BLCA cells induced by POLR3G overexpression, whereas the activation of this pathway using 740Y-P restored the abilities that were inhibited by POLR3G knockdown. Taken together, our findings suggested that POLR3G is a prognostic predictor for BLCA and promotes EMT of BLCA through activation of the PI3K/AKT signaling pathway.

A Combinatorial Regulatory Platform Determines Expression of RNA Polymerase III Subunit RPC7α (POLR3G) in Cancer

RNA polymerase III (Pol III) subunit RPC7α, which is encoded by POLR3G in humans, has been linked to both tumor growth and metastasis. Accordantly, high POLR3G expression is a negative prognostic factor in multiple cancer subtypes. To date, the mechanisms underlying POLR3G upregulation have remained poorly defined. We performed a large-scale genomic survey of mRNA and chromatin signatures to predict drivers of POLR3G expression in cancer. Our survey uncovers positive determinants of POLR3G expression, including a gene-internal super-enhancer bound with multiple transcription factors (TFs) that promote POLR3G expression, as well as negative determinants that include gene-internal DNA methylation, retinoic-acid induced differentiation, and MXD4-mediated disruption of POLR3G expression. We show that novel TFs identified in our survey, including ZNF131 and ZNF207, functionally enhance POLR3G expression, whereas MXD4 likely obstructs MYC-driven expression of POLR3G and other growth-related genes. Integration of chromatin architecture and gene regulatory signatures identifies additional factors, including histone demethylase KDM5B, as likely influencers of POLR3G gene activity. Taken together, our findings support a model in which POLR3G expression is determined with multiple factors and dynamic regulatory programs, expanding our understanding of the circuitry underlying POLR3G upregulation and downstream consequences in cancer.

Metabolic Reprogramming of HCC: A New Microenvironment for Immune Responses

Hepatocellular carcinoma is the most common primary liver cancer, ranking third among the leading causes of cancer-related mortality worldwide and whose incidence varies according to geographical area and ethnicity. Metabolic rewiring was recently introduced as an emerging hallmark able to affect tumor progression by modulating cancer cell behavior and immune responses. This review focuses on the recent studies examining HCC's metabolic traits, with particular reference to the alterations of glucose, fatty acid and amino acid metabolism, the three major metabolic changes that have gained attention in the field of HCC. After delivering a panoramic picture of the peculiar immune landscape of HCC, this review will also discuss how the metabolic reprogramming of liver cancer cells can affect, directly or indirectly, the microenvironment and the function of the different immune cell populations, eventually favoring the tumor escape from immunosurveillance.

RNA polymerase III transcription and cancer: A tale of two RPC7 subunits

RNA polymerase III composition is shaped by the mutually exclusive incorporation of two paralogous subunits, RPC7α and RPC7β, encoded by genes POLR3G and POLR3GL in vertebrates. The expression of POLR3G and POLR3GL is spatiotemporally regulated during development, and multiple reports point to RPC7α-enhanced Pol III activity patterns, indicating that Pol III identity may underly dynamic Pol III transcription patterns observed in higher eukaryotes. In cancer, upregulation of POLR3G, but not POLR3GL, is associated with poor survival outcomes among patients, suggesting differences between RPC7α and RPC7β further influence disease progression and may translate into future biomarkers and therapeutic strategies. Here, we outline our current understanding of Pol III identity and transcription and reexamine the distinct protein characteristics of Pol III subunits RPC7α and RPC7β. Drawing on both structural and genomic studies, we discuss differences between RPC7α and RPC7β and the potential mechanisms by which Pol III identity may establish differential activities during development and disease.

Identification and Validation of Novel Immunogenic Cell Death- and DNA Damage Response-Related Molecular Patterns Correlated with Immune Status and Prognosis in Hepatocellular Carcinoma

Immunogenic cell death (ICD) and DNA damage response (DDR) are involved in cancer progression and prognosis. Currently, chemotherapy is the first-line treatment for intermediate or advanced hepatocellular carcinoma (HCC), which is mostly based on platinum and anthracyclines that induce DNA damage and ICD. With the treatment of HCC with immune checkpoint inhibitors (ICIs), it is important to understand the molecular characteristics and prognostic values of ICD and DDR-related genes (IDRGs). We aimed to explore the characteristics of ICD and DDR-related molecular patterns, immune status, and the association of immunotherapy and prognosis with IDRGs in HCC. We identified IDRGs in HCC and evaluated their differential expression, biological behaviors, molecular characteristics, immune cell infiltration, and prognostic value. Prognostic IDRGs and subtypes were identified and validated. FFAR3, DDX1, POLR3G, FANCL, ADA, PI3KR1, DHX58, TPT1, MGMT, SLAMF6, and EIF2AK4 were determined as risk factors for HCC, and the biological experiments indicated that high FANCL expression is harmful to the treatment and prognosis. HCC was classified into high- and low-risk groups based on the median values of the risk factors to construct a predictive nomogram. These findings provide novel insights into the treatment and prognosis of HCC and provide a new research direction for HCC.

The POLR3G Subunit of Human RNA Polymerase III Regulates Tumorigenesis and Metastasis in Triple-Negative Breast Cancer

RNA polymerase (Pol) III transcribes short untranslated RNAs that contribute to the regulation of gene expression. Two isoforms of human Pol III have been described that differ by the presence of the POLR3G/RPC32α or POLR3GL/RPC32β subunits. POLR3G was found to be expressed in embryonic stem cells and at least a subset of transformed cells, whereas POLR3GL shows a ubiquitous expression pattern. Here, we demonstrate that POLR3G is specifically overexpressed in clinical samples of triple-negative breast cancer (TNBC) but not in other molecular subtypes of breast cancer. POLR3G KO in the MDA-MB231 TNBC cell line dramatically reduces anchorage-independent growth and invasive capabilities in vitro. In addition, the POLR3G KO impairs tumor growth and metastasis formation of orthotopic xenografts in mice. Moreover, KO of POLR3G induces expression of the pioneer transcription factor FOXA1 and androgen receptor. In contrast, the POLR3G KO neither alters proliferation nor the expression of epithelial-mesenchymal transition marker genes. These data demonstrate that POLR3G expression is required for TNBC tumor growth, invasiveness and dissemination and that its deletion affects triple-negative breast cancer-specific gene expression.

Identification and validation of a tyrosine metabolism-related prognostic prediction model and characterization of the tumor microenvironment infiltration in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is an aggressive and heterogeneous disease characterized by high morbidity and mortality. The liver is the vital organ that participates in tyrosine catabolism, and abnormal tyrosine metabolism could cause various diseases, including HCC. Besides, the tumor immune microenvironment is involved in carcinogenesis and can influence the patients' clinical outcomes. However, the potential role of tyrosine metabolism pattern and immune molecular signature is poorly understood in HCC.

METHODS

Gene expression, somatic mutations, copy number variation data, and clinicopathological information of HCC were downloaded from The Cancer Genome Atlas (TCGA) database. GSE14520 from the Gene Expression Omnibus (GEO) databases was used as a validation dataset. We performed unsupervised consensus clustering of tyrosine metabolism-related genes (TRGs) and classified patients into distinct molecular subtypes. We used ESTIMATE algorithms to evaluate the immune infiltration. We then applied LASSO Cox regression to establish the TRGs risk model and validated its predictive performance.

RESULTS

In this study, we first described the alterations of 42 TRGs in HCC cohorts and characterized the clinicopathological characteristics and tumor microenvironmental landscape of the two distinct subtypes. We then established a tyrosine metabolism-related scoring system and identified five TRGs, which were highly correlated with prognosis and representative of this gene set, namely METTL6, GSTZ1, ADH4, ADH1A, and LCMT1. Patients in the high-risk group had an inferior prognosis. Univariate and multivariate Cox proportional hazards regression analysis also showed that the tyrosine metabolism-related signature was an independent prognostic indicator. Besides, receiver operating characteristic curve (ROC) analysis demonstrated the predictive accuracy of the TRGs signature that could reliably predict 1-, 3-, and 5-year survival in both TCGA and GEO cohorts. We also got consistent results by performing clone formation and invasion analysis, and immunohistochemical (IHC) assays. Moreover, we also discovered that the TRGs signature was significantly associated with the different immune landscapes and therapeutic drug sensitivity.

CONCLUSION

Our comprehensive analysis revealed the potential molecular signature and clinical utilities of TRGs in HCC. The model based on five TRGs can accurately predict the survival outcomes of HCC, improving our knowledge of TRGs in HCC and paving a new path for guiding risk stratification and treatment strategy development for HCC patients.

Exploration of the Potential Link, Hub Genes, and Potential Drugs for Coronavirus Disease 2019 and Lung Cancer Based on Bioinformatics Analysis

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has a huge influence on global public health and the economy. Lung cancer is one of the high-risk factors of COVID-19, but the molecular mechanism of lung cancer and COVID-19 is still unclear, and further research is needed. Therefore, we used the transcriptome information of the public database and adopted bioinformatics methods to identify the common pathways and molecular biomarkers of lung cancer and COVID-19 to further understand the connection between them. The two RNA-seq data sets in this study—GSE147507 (COVID-19) and GSE33532 (lung cancer)—were both derived from the Gene Expression Omnibus (GEO) database and identified differentially expressed genes (DEGs) for lung cancer and COVID-19 patients. We conducted Gene Ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis and found some common features between lung cancer and COVID-19. We also performed TFs-gene, miRNAs-gene, and gene-drug analyses. In total, 32 DEGs were found. A protein-protein interaction (PPI) network was constructed by DEGs, and 10 hub genes were screened. Finally, the identified drugs may be helpful for COVID-19 treatment.

M2-like tumor-associated macrophage-related biomarkers to construct a novel prognostic signature, reveal the immune landscape, and screen drugs in hepatocellular carcinoma

Background

M2-like tumor-associated macrophages (M2-like TAMs) have important roles in the progression and therapeutics of cancers. We aimed to detect novel M2-like TAM-related biomarkers in hepatocellular carcinoma (HCC) via integrative analysis of single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data to construct a novel prognostic signature, reveal the “immune landscape”, and screen drugs in HCC.

Methods

M2-like TAM-related genes were obtained by overlapping the marker genes of TAM identified from scRNA-seq data and M2 macrophage modular genes identified by weighted gene co-expression network analysis (WGCNA) using bulk RNA-seq data. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were carried out to screen prognostic genes from M2-like TAM-related genes, followed by a construction of a prognostic signature, delineation of risk groups, and external validation of the prognostic signature. Analyses of immune cells, immune function, immune evasion scores, and immune-checkpoint genes between high- and low-risk groups were done to further reveal the immune landscape of HCC patients. To screen potential HCC therapeutic agents, analyses of gene–drug correlation and sensitivity to anti-cancer drugs were conducted.

Results

A total of 127 M2-like TAM-related genes were identified by integrative analysis of scRNA-seq and bulk-seq data. PDLIM3, PAM, PDLIM7, FSCN1, DPYSL2, ARID5B, LGALS3, and KLF2 were screened as prognostic genes in HCC by univariate Cox regression and LASSO regression analyses. Then, a prognostic signature was constructed and validated based on those genes for predicting the survival of HCC patients. In terms of drug screening, expression of PAM and LGALS3 was correlated positively with sensitivity to simvastatin and ARRY-162, respectively. Based on risk grouping, we predicted 10 anticancer drugs with high sensitivity in the high-risk group, with epothilone B having the lowest half-maximal inhibitory concentration among all drugs tested.

Conclusions

Our findings enhance understanding of the M2-like TAM-related molecular mechanisms involved in HCC, reveal the immune landscape of HCC, and provide potential targets for HCC treatment.

Downregulation of METTL6 mitigates cell progression, migration, invasion and adhesion in hepatocellular carcinoma by inhibiting cell adhesion molecules

RNA modifications have attracted increasing interest in recent years because they have been frequently implicated in various human diseases, including cancer, highlighting the importance of dynamic post‑transcriptional modifications. Methyltransferase‑like 6 (METTL6) is a member of the RNA methyltransferase family that has been identified in many cancers; however, little is known about its specific role or mechanism of action. In the present study, we aimed to study the expression levels and functional role of METTL6 in hepatocellular carcinoma (HCC), and further investigate the relevant pathways. To this end, we systematically conducted bioinformatics analysis of METTL6 in HCC using gene expression data and clinical information from a publicly available dataset. The mRNA expression levels of METTL6 were significantly upregulated in HCC tumor tissues compared to that in adjacent non‑tumor tissues and strongly associated with poorer survival outcomes in patients with HCC. CRISPR/Cas9‑mediated knockout of METTL6 in HCC cell lines remarkably inhibited colony formation, cell proliferation, cell migration, cell invasion and cell attachment ability. RNA sequencing analysis demonstrated that knockout of METTL6 significantly suppressed the expression of cell adhesion‑related genes. However, chromatin immunoprecipitation sequencing results revealed no significant differences in enhancer activities between cells, which suggests that METTL6 may regulate genes of interest post‑transcriptionally. In addition, it was demonstrated for the first time that METTL6 was localized in the cytosol as detected by immunofluorescence analysis, which indicates the plausible location of RNA modification mediated by METTL6. Our findings provide further insight into the function of RNA modifications in cancer and suggest a possible role of METTL6 as a therapeutic target in HCC.

Identification of m6A methyltransferase-related genes predicts prognosis and immune infiltrates in head and neck squamous cell carcinoma

Background

Head and neck squamous cell carcinoma (HNSCC) accounts for 90% of head and neck malignant tumors. As the early symptoms of HNSCC are not obvious, and it is prone to recurrence and metastasis, making the overall survival (OS) rate of patients very low. Existing studies have shown m6A methylation plays a crucial role in various cancers, but it is rarely studied in HNSCC. This study aimed to explore the expression of m6A methylation-related genes in HNSCC and its correlation with prognosis, and to explore its relationship with immune infiltration.

Methods

The gene expression data of HNSCC patient tumor samples (tumor =510) and adjacent normal tissue samples (normal =50) were extracted from The Cancer Genome Atlas (TCGA) database, and the expression characteristics of m6A regulatory factors were described. Kaplan-Meier survival analysis was used to analyze the relationship between m6A regulatory factors and OS and disease-specific survival (DSS). Least absolute shrinkage and selection operator (LASSO) regression was used to construct the m6A regulatory factor-HNSCC risk prediction model. In addition, the relationship between m6A methylation-related genes and tumor immune infiltration were discussed.

Results

The differential expression of 20 genes were identified by TCGA, and 18 genes (IGF2BP2, IGF2BP1, IGF2BP3, VIRMA, YTHDF1, YTHDF2, YTHDF3, ZC3H13, METTL14, ALKBH5, METTL3, RBMX, WTAP, YTHDC1, FTO, HNRNPC, HNRNPA2B1, and RBM15) were overexpressed in HNSCC. The survival rate of different gene expression levels was different. The high expression of YTHDC1 and YTHDC2 indicated better OS. Furthermore, for DSS, increased expression of YTHDC2 was also correlated with better clinical outcomes (P<0.05). At the same time, we drew a 3-gene risk score model in the TCGA-HNSCC cohort, and the survival curve showed compared with low-risk patients, high-risk patients had significantly worse OS (P<0.05). Gene enrichment analysis showed EPITHELIAL_MESENCHYMAL_TRANSITIO, MTORC1_SIGNALING, MYC_TARGETS_V1, MYC_TARGETS_V2, MYOGENESIS pathways, high TP53 mutations, and suppressive immunity were related to the high-risk group. The low-risk group was related to ALLOGRAFT_REJECTION, COMPLEMENT, IL6_JAK_STAT3_SIGNALING, INTERFERON_ALPHA_RESPONSE, INTERFERON_GAMMA_RESPONSE pathways, low TP53 mutations, and active immunity.

Conclusions

The m6A methyltransferase-related genes can predict the prognosis of HNSCC and are related to immune infiltration.

Role of Nitric Oxide in Gene Expression Regulation during Cancer: Epigenetic Modifications and Non-Coding RNAs

Nitric oxide (NO) has been identified and described as a dual mediator in cancer according to dose-, time- and compartment-dependent NO generation. The present review addresses the different epigenetic mechanisms, such as histone modifications and non-coding RNAs (ncRNAs), miRNA and lncRNA, which regulate directly or indirectly nitric oxide synthase (NOS) expression and NO production, impacting all hallmarks of the oncogenic process. Among lncRNA, HEIH and UCA1 develop their oncogenic functions by inhibiting their target miRNAs and consequently reversing the inhibition of NOS and promoting tumor proliferation. The connection between miRNAs and NO is also involved in two important features in cancer, such as the tumor microenvironment that includes key cellular components such as tumor-associated macrophages (TAMs), cancer associated fibroblasts (CAFs) and cancer stem cells (CSCs).