SNORA71B promotes breast cancer cells across blood-brain barrier by inducing epithelial-mesenchymal transition

Why does HER2-positive breast cancer metastasize to the brain and what can we do about it?

Breast cancer is the most frequent malignancy in women. However, in the middle and late stages, some people develop distant metastases, which considerably lower the quality of life and life expectancy. The brain is one of the sites where metastasis frequently happens. According to epidemiological research, brain metastases occur at a late stage in 30-50% of patients with HER2-positive breast cancer, resulting in a poor prognosis. Additionally, few treatments are available for HER2-positive brain metastatic breast cancer, and the mortality rate is remarkable owing to the complexity of the brain's anatomical structure and physiological function. In this review, we described the stages of the brain metastasis of breast cancer, the relationship between the microenvironment and metastatic cancer cells, and the unique molecular and cellular mechanisms. It involves cancer cells migrating, invading, and adhering to the brain; penetrating the blood-brain barrier; interacting with brain cells; and activating signal pathways once inside the brain. Finally, we reviewed current clinically used treatment approaches for brain metastasis in HER2-positive breast cancer; summarized the traditional treatment, targeted treatment, immunotherapy, and other treatment modalities; compared the benefits and drawbacks of each approach; discussed treatment challenges; and emphasized the importance of identifying potential targets to improve patient survival rates and comprehend brain metastasis in breast cancer.

Small Nucleolar RNAs as Diagnostic and Prognostic Biomarkers in Cancer: A Systematic Review and Meta-Analysis

OBJECTIVES

Small nucleolar RNAs (snoRNAs) form clusters within the genome, representing a mysterious category of small non-coding RNAs. Research has demonstrated that aberrant snoRNAs can contribute to the development of various types of cancers. Recent studies have identified snoRNAs as potentially valuable biomarkers for the diagnosis or/and prognosis of cancers. However, there has been a lack of comprehensive reviews on prognostic and diagnostic snoRNAs across different types of cancers.

METHODS

We conducted a systematic search of various databases including Google Scholar, Medline, Cochrane, Scopus, PubMed, Embase, ScienceDirect, Ovid-Medline, Chinese National Knowledge Infrastructure, WanFang, and SinoMed with a time frame reception to December 30, 2022. A total of 49 relevant articles were included in our analysis, consisting of 21 articles focusing on diagnostic aspects and 41 articles focusing on prognostic aspects. Pooled odds ratio, 95% confidence intervals (CIs), and hazard ratio (HR) were utilized to evaluate clinical parameters and overall survival (OS), respectively.

RESULT

The findings indicated that area under the curve, sensitivity, and specificity were 0.85, 75%, and 80% in cancer, respectively. There was a possibility that snoRNAs had a positive impact on the diagnosis (risk ratio, RR = 2.95, 95% CI: 2.75-3.16, P = 0.000) and OS (HR = 1) in cancer. Additionally, abnormally expressed snoRNAs were associated with a positive impact on OS time for chronic lymphocytic leukemia (HR: 0.88, 95%Cl: 0.69-1.11, P < 0.00001), colon adenocarcinoma (HR: 0.97, 95%Cl: 0.91-1.03, P < 0.0001), and ovarian cancer (HR: 0.98, 95%Cl: 0.98-0.99, P < 0.00001). However, dysregulated snoRNAs of colon cancer and colorectal cancer had a negative impact on OS time (HR = 3.01 and 1.01 respectively, P < 0.0001).

CONCLUSION

The results strongly suggested that snoRNAs could serve as potential novel indicators for prognosis and diagnosis in cancers. This systematic review followed the guidelines of the Transparent Reporting of Systematic Review and Meta-Analyses (PROSPERO register: CRD42020209096).

RNA-seq analysis identifies transcriptomic profiles associated with anal cancer recurrence among people living with HIV

BACKGROUND

Chemoradiation therapy (CRT) is the standard of care for squamous cell carcinoma of the anus (SCCA), the most common type of anal cancer. However, approximately one fourth of patients still relapse after CRT.

METHODS

We used RNA-sequencing technology to characterize coding and non-coding transcripts in tumor tissues from CRT-treated SCCA patients and compare them between 9 non-recurrent and 3 recurrent cases. RNA was extracted from FFPE tissues. Library preparations for RNA-sequencing were created using SMARTer Stranded Total RNA-Seq Kit. All libraries were pooled and sequenced on a NovaSeq 6000. Function and pathway enrichment analysis was performed with Metascape and enrichment of gene ontology (GO) was performed with Gene Set Enrichment Analysis (GSEA).

RESULTS

There were 449 differentially expressed genes (DEGs) observed (390 mRNA, 12 miRNA, 17 lincRNA and 18 snRNA) between the two groups. We identified a core of upregulated genes (IL4, CD40LG, ICAM2, HLA-I (HLA-A, HLA-C) and HLA-II (HLA-DQA1, HLA-DRB5) in the non-recurrent SCCA tissue enriching to the gene ontology term 'allograft rejection', which suggests a CD4+ T cell driven immune response. Conversely, in the recurrent tissues, keratin (KRT1, 10, 12, 20) and hedgehog signaling pathway (PTCH2) genes involved in 'Epidermis Development,', were significantly upregulated. We identified miR-4316, that inhibit tumor proliferation and migration by repressing vascular endothelial growth factors, as being upregulated in non-recurrent SCCA. On the contrary, lncRNA-SOX21-AS1, implicated in the progression of many other cancers, was also found to be more common in our recurrent compared to non-recurrent SCCA.

Our study identified key host factors which may drive the recurrence of SCCA and warrants further studies to understand the mechanism and evaluate their potential use in personalized treatment.Key MessageOur study used RNA sequencing (RNA-seq) to identify pivotal factors in coding and non-coding transcripts which differentiate between patients at risk for recurrent anal cancer after treatment. There were 449 differentially expressed genes (390 mRNA, 12 miRNA, 17 lincRNA and 18 snRNA) between 9 non-recurrent and 3 recurrent squamous cell carcinoma of anus (SCCA) tissues. The enrichment of genes related to allograft rejection was observed in the non-recurrent SCCA tissues, while the enrichment of genes related to epidermis development was positively linked with recurrent SCCA tissues.

The Journey of Cancer Cells to the Brain: Challenges and Opportunities

Cancer metastases into the brain constitute one of the most severe, but not uncommon, manifestations of cancer progression. Several factors control how cancer cells interact with the brain to establish metastasis. These factors include mediators of signaling pathways participating in migration, infiltration of the blood-brain barrier, interaction with host cells (e.g., neurons, astrocytes), and the immune system. Development of novel therapies offers a glimpse of hope for increasing the diminutive life expectancy currently forecasted for patients suffering from brain metastasis. However, applying these treatment strategies has not been sufficiently effective. Therefore, there is a need for a better understanding of the metastasis process to uncover novel therapeutic targets. In this review, we follow the journey of various cancer cells from their primary location through the diverse processes that they undergo to colonize the brain. These processes include EMT, intravasation, extravasation, and infiltration of the blood-brain barrier, ending up with colonization and angiogenesis. In each phase, we focus on the pathways engaging molecules that potentially could be drug target candidates.

LincRNAs and snoRNAs in Breast Cancer Cell Metastasis: The Unknown Players

Recent advances in research have led to earlier diagnosis and targeted therapies against breast cancer, which has resulted in reduced breast cancer-related mortality. However, the majority of breast cancer-related deaths are due to metastasis of cancer cells to other organs, a process that has not been fully elucidated. Among the factors and genes implicated in the metastatic process regulation, non-coding RNAs have emerged as crucial players. This review focuses on the role of long intergenic noncoding RNAs (lincRNAs) and small nucleolar RNAs (snoRNAs) in breast cancer cell metastasis. LincRNAs are transcribed between two protein-coding genes and are longer than 200 nucleotides, they do not code for a specific protein but function as regulatory molecules in processes such as cell proliferation, apoptosis, epithelial-to-mesenchymal transition, migration, and invasion while most of them are highly elevated in breast cancer tissues and seem to function as competing endogenous RNAs (ceRNAs) inhibiting relevant miRNAs that specifically target vital metastasis-related genes. Similarly, snoRNAs are 60-300 nucleotides long and are found in the nucleolus being responsible for the post-transcriptional modification of ribosomal and spliceosomal RNAs. Most snoRNAs are hosted inside intron sequences of protein-coding and non-protein-coding genes, and they also regulate metastasis-related genes affecting related cellular properties.

snoRNAs: functions and mechanisms in biological processes, and roles in tumor pathophysiology

Small nucleolar RNAs (snoRNAs), a type of non-coding RNA, are widely present in the nucleoli of eukaryotic cells and play an important role in rRNA modification. With the recent increase in research on snoRNAs, new evidence has emerged indicating that snoRNAs also participate in tRNA and mRNA modification. Studies suggest that numerous snoRNAs, including tumor-promoting and tumor-suppressing snoRNAs, are not only dysregulated in tumors but also show associations with clinical prognosis. In this review, we summarize the reported functions of snoRNAs and the possible mechanisms underlying their role in tumorigenesis and cancer development to guide the snoRNA-based clinical diagnosis and treatment of cancer in the future.

Disorders and roles of tsRNA, snoRNA, snRNA and piRNA in cancer

Most small non-coding RNAs (sncRNAs) with regulatory functions are encoded by majority sequences in the human genome, and the emergence of high-throughput sequencing technology has greatly expanded our understanding of sncRNAs. sncRNAs are composed of a variety of RNAs, including tRNA-derived small RNA (tsRNA), small nucleolar RNA (snoRNA), small nuclear RNA (snRNA), PIWI-interacting RNA (piRNA), etc. While for some, sncRNAs' implication in several pathologies is now well established, the potential involvement of tsRNA, snoRNA, snRNA and piRNA in human diseases is only beginning to emerge. Recently, accumulating pieces of evidence demonstrate that tsRNA, snoRNA, snRNA and piRNA play an important role in many biological processes, and their dysregulation is closely related to the progression of cancer. Abnormal expression of tsRNA, snoRNA, snRNA and piRNA participates in the occurrence and development of tumours through different mechanisms, such as transcriptional inhibition and post-transcriptional regulation. In this review, we describe the research progress in the classification, biogenesis and biological function of tsRNA, snoRNA, snRNA and piRNA. Moreover, we emphasised their dysregulation and mechanism of action in cancer and discussed their potential as diagnostic and prognostic biomarkers or therapeutic targets.

Tumor Nonimmune-Microenvironment-Related Gene Expression Signature Predicts Brain Metastasis in Lung Adenocarcinoma Patients after Surgery: A Machine Learning Approach Using Gene Expression Profiling

Simple Summary

It is important to be able to predict brain metastasis in lung adenocarcinoma patients; however, research in this area is still lacking. Much of the previous work on tumor microenvironments in lung adenocarcinoma with brain metastasis concerns the tumor immune microenvironment. The importance of the tumor nonimmune microenvironment (extracellular matrix (ECM), epithelial–mesenchymal transition (EMT) feature, and angiogenesis) has been overlooked with regard to brain metastasis. We evaluated tumor nonimmune-microenvironment-related gene expression signatures that could predict brain metastasis after the surgical resection of lung adenocarcinoma using a machine learning approach. We identified a tumor nonimmune-microenvironment-related 17-gene expression signature, and this signature showed high brain metastasis predictive power in four machine learning classifiers. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. Our tumor nonimmune-microenvironment-related gene expression signatures are important biological markers that can predict brain metastasis and provide patient-specific treatment options.

Abstract

Using a machine learning approach with a gene expression profile, we discovered a tumor nonimmune-microenvironment-related gene expression signature, including extracellular matrix (ECM) remodeling, epithelial–mesenchymal transition (EMT), and angiogenesis, that could predict brain metastasis (BM) after the surgical resection of 64 lung adenocarcinomas (LUAD). Gene expression profiling identified a tumor nonimmune-microenvironment-related 17-gene expression signature that significantly correlated with BM. Of the 17 genes, 11 were ECM-remodeling-related genes. The 17-gene expression signature showed high BM predictive power in four machine learning classifiers (areas under the receiver operating characteristic curve = 0.845 for naïve Bayes, 0.849 for support vector machine, 0.858 for random forest, and 0.839 for neural network). Subgroup analysis revealed that the BM predictive power of the 17-gene signature was higher in the early-stage LUAD than in the late-stage LUAD. Pathway enrichment analysis showed that the upregulated differentially expressed genes were mainly enriched in the ECM–receptor interaction pathway. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. The tumor nonimmune-microenvironment-related gene expression signatures found in this study are important biological markers that can predict BM and provide patient-specific treatment options.

SnoRNA copy regulation affects family size, genomic location and family abundance levels

Background

Small nucleolar RNAs (snoRNAs) are an abundant class of noncoding RNAs present in all eukaryotes and best known for their involvement in ribosome biogenesis. In mammalian genomes, many snoRNAs exist in multiple copies, resulting from recombination and retrotransposition from an ancestral snoRNA. To gain insight into snoRNA copy regulation, we used Rfam classification and normal human tissue expression datasets generated using low structure bias RNA-seq to characterize snoRNA families.

Results

We found that although box H/ACA families are on average larger than box C/D families, the number of expressed members is similar for both types. Family members can cover a wide range of average abundance values, but importantly, expression variability of individual members of a family is preferred over the total variability of the family, especially for box H/ACA snoRNAs, suggesting that while members are likely differentially regulated, mechanisms exist to ensure uniformity of the total family abundance across tissues. Box C/D snoRNA family members are mostly embedded in the same host gene while box H/ACA family members tend to be encoded in more than one different host, supporting a model in which box C/D snoRNA duplication occurred mostly by cis recombination while box H/ACA snoRNA families have gained copy members through retrotransposition. And unexpectedly, snoRNAs encoded in the same host gene can be regulated independently, as some snoRNAs within the same family vary in abundance in a divergent way between tissues.

Conclusions

SnoRNA copy regulation affects family sizes, genomic location of the members and controls simultaneously member and total family abundance to respond to the needs of individual tissues.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07757-1.