Expression scoring of a small-nucleolar-RNA signature identified by machine learning serves as a prognostic predictor for head and neck cancer

Disulfidptosis features and prognosis in head and neck squamous cell carcinoma patients: unveiling and validating the prognostic signature across cohorts

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a significant health concern with a variable global incidence and is linked to regional lifestyle factors and HPV infections. Despite treatment advances, patient prognosis remains variable, necessitating an understanding of its molecular mechanisms and the identification of reliable prognostic biomarkers.

METHODS

We analyzed 959 HNSCC samples and employed batch correction to obtain consistent transcriptomic data across cohorts. We examined 79 disulfidptosis-related genes to determine consensus clusters and utilized high-throughput sequencing to identify genetic heterogeneity within tumors. We established a disulfidptosis prognostic signature (DSPS) using least absolute shrinkage and selection operator (LASSO) regression and developed a prognostic nomogram integrating the DSPS with clinical factors. Personalized chemotherapy prediction was performed using the "pRRophetic" R package.

RESULTS

Batch corrections were used to harmonize gene expression data, revealing two distinct disulfidptosis subtypes, C1 and C2, with differential gene expression and survival outcomes. Subtype C1, characterized by increased expression of the MYH family genes ACTB, ACTN2, and FLNC, had a mortality rate of 48.4%, while subtype C2 had a mortality rate of 38.7% (HR = 0.77, 95% CI: 0.633-0.934, P = 0.008). LASSO regression identified 15 genes that composed the DSPS prognostic model, which independently predicted survival (HR = 2.055, 95% CI: 1.420-2.975, P < 0.001). The prognostic nomogram, which included the DSPS, age, and tumor stage, predicted survival with AUC values of 0.686, 0.704, and 0.789 at 3, 5, and 8 years, respectively, indicating strong predictive capability. In the external validation cohort (cohort B), the DSPS successfully identified patients at greater risk, with worse overall survival outcomes in the high-DSPS subgroup (HR = 1.54, 95% CI: 1.17-2.023, P = 0.002) and AUC values of 0.601, 0.644, 0.636, and 0.748 at 3, 5, 8, and 10 years, respectively, confirming the model's robustness.

CONCLUSION

The DSPS provides a robust prognostic tool for HNSCC, underscoring the complexity of this disease and the potential for tailored treatment strategies. This study highlights the importance of molecular signatures in oncology, offering a step toward personalized medicine and improved patient outcomes in HNSCC management.

USP39 Promotes the Viability and Migration of Head and Neck Squamous Cell Carcinoma Cell by Regulating STAT1

Objective: Ubiquitin-specific peptidase 39 (USP39) plays a carcinogenic role in many cancers, but little research has been conducted examining whether it is involved in head and neck squamous cell carcinoma (HNSCC). Therefore, this study explored the functional role of USP39 in HNSCC. Method: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to identify differentially expressed proteins (DEPs) between the HNSCC tumor and adjacent healthy tissues. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to assess the functional enrichment of DEPs. Immunohistochemistry was used to detect protein expression. The viability and migration of two HNSCC cell lines, namely CAL27 and SCC25, were detected using the cell counting kit-8 assay and a wound healing assay, respectively. Quantitative real-time PCR was used to detect the expression level of signal transducer and activator of transcription 1 (STAT1) mRNA. Results: LC-MS/MS results identified 590 DEPs between HNSCC and adjacent tissues collected from 4 patients. Through GO and KEGG pathway analyses, 34 different proteins were found to be enriched in the spliceosome pathway. The expression levels of USP39 and STAT1 were significantly higher in HNSCC tumor tissue than in adjacent healthy tissue as assessed by LC-MS/MS analysis, and the increased expression of USP39 and STAT1 protein was confirmed by immunohistochemistry in clinical samples collected from 7 additional patients with HNSCC. Knockdown of USP39 or STAT1 inhibited the viability and migration of CAL27 and SCC25 cells. In addition, USP39 knockdown inhibited the expression of STAT1 mRNA in these cells. Conclusion: Our findings indicated that USP39 knockdown may inhibit HNSCC viability and migration by suppressing STAT1 expression. The results of this study suggest that USP39 may be a potential new target for HNSCC clinical therapy or a new biomarker for HNSCC.

Polo-like kinase 1 as a biomarker predicts the prognosis and immunotherapy of breast invasive carcinoma patients

Background

Invasive breast carcinoma (BRCA) is associated with poor prognosis and high risk of mortality. Therefore, it is critical to identify novel biomarkers for the prognostic assessment of BRCA.

Methods

The expression data of polo-like kinase 1 (PLK1) in BRCA and the corresponding clinical information were extracted from TCGA and GEO databases. PLK1 expression was validated in diverse breast cancer cell lines by quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting. Single sample gene set enrichment analysis (ssGSEA) was performed to evaluate immune infiltration in the BRCA microenvironment, and the random forest (RF) and support vector machine (SVM) algorithms were used to screen for the hub infiltrating cells and calculate the immunophenoscore (IPS). The RF algorithm and COX regression model were applied to calculate survival risk scores based on the PLK1 expression and immune cell infiltration. Finally, a prognostic nomogram was constructed with the risk score and pathological stage, and its clinical potential was evaluated by plotting calibration charts and DCA curves. The application of the nomogram was further validated in an immunotherapy cohort.

Results

PLK1 expression was significantly higher in the tumor samples in TCGA-BRCA cohort. Furthermore, PLK1 expression level, age and stage were identified as independent prognostic factors of BRCA. While the IPS was unaffected by PLK1 expression, the TMB and MATH scores were higher in the PLK1-high group, and the TIDE scores were higher for the PLK1-low patients. We also identified 6 immune cell types with high infiltration, along with 11 immune cell types with low infiltration in the PLK1-high tumors. A risk score was devised using PLK1 expression and hub immune cells, which predicted the prognosis of BRCA patients. In addition, a nomogram was constructed based on the risk score and pathological staging, and showed good predictive performance.

Conclusions

PLK1 expression and immune cell infiltration can predict post-immunotherapy prognosis of BRCA patients.

A prognostic signature based on snoRNA predicts the overall survival of lower-grade glioma patients

Introduction

Small nucleolar RNAs (snoRNAs) are a group of non-coding RNAs enriched in the nucleus which direct post-transcriptional modifications of rRNAs, snRNAs and other molecules. Recent studies have suggested that snoRNAs have a significant role in tumor oncogenesis and can be served as prognostic markers for predicting the overall survival of tumor patients.

Methods

We screened 122 survival-related snoRNAs from public databases and eventually selected 7 snoRNAs that were most relevant to the prognosis of lower-grade glioma (LGG) patients for the establishment of the 7-snoRNA prognostic signature. Further, we combined clinical characteristics related to the prognosis of glioma patients and the 7-snoRNA prognostic signature to construct a nomogram.

Results

The prognostic model displayed greater predictive power in both validation set and stratification analysis. Results of enrichment analysis revealed that these snoRNAs mainly participated in the post-transcriptional process such as RNA splicing, metabolism and modifications. In addition, 7-snoRNA prognostic signature were positively correlated with immune scores and expression levels of multiple immune checkpoint molecules, which can be used as potential biomarkers for immunotherapy prediction. From the results of bioinformatics analysis, we inferred that SNORD88C has a major role in the development of glioma, and then performed in vitro experiments to validate it. The results revealed that SNORD88C could promote the proliferation, invasion and migration of glioma cells.

Discussion

We established a 7-snoRNA prognostic signature and nomogram that can be applied to evaluate the survival of LGG patients with good sensitivity and specificity. In addition, SNORD88C could promote the proliferation, migration and invasion of glioma cells and is involved in a variety of biological processes related to DNA and RNA.

Imprinted small nucleolar RNAs: Missing link in development and disease?

The 14q32.2 (DLK1-DIO3) and 15q11-q13 (SNURF-SNRPN) imprinted gene loci harbor the largest known small nucleolar RNA clusters expressed from the respective maternal and paternal alleles. Recent studies have demonstrated significant roles for the 15q11-q13 located SNORD115-SNORD116 C/D box snoRNAs in Prader-Willi syndrome (PWS), a neurodevelopmental disorder. Even though the effect of SNORD116 deletion is apparent in the PWS phenotype, similar effects of a SNORD113-SNORD114 cluster deletion from the 14q32.2 locus in Kagami-Ogata syndrome (KOS14) and upregulation in Temple syndrome (TS14) remain to be explored. Moreover, apart from their probable involvement in neurodevelopmental disorders, snoRNAs from the SNORD113-SNORD114 cluster have been implicated in multiple biological processes, including pluripotency, development, cancers, and RNA modifications. Here we summarize the current understanding of the system to explore the possibility of a link between developmental disorders and C/D box snoRNA expression from the imprinted 14q32.2 locus. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA in Disease and Development > RNA in Development RNA Processing > Processing of Small RNAs.

Identification of a new anoikis-related gene signature for prognostic significance in head and neck squamous carcinomas

Anoikis, a mode of programmed cell death, is essential for normal development and homeostasis in the organism and plays an important role in the onset and progression of cancers. The authors of this research sought to establish a gene signature associated with anoikis to predict therapy outcomes and patient prognosis for individuals with head and neck squamous cell carcinoma (HNSCC). Transcriptome data of anoikis-related genes (ARGs) in individuals with HNSCC were retrieved from public databases to aid in the formulation of the gene signature. A novel ARG signature was then created using a combination of the Least Absolute Shrinkage and Selection Operator regression and Cox regression analysis. The relationship between ARGs and tumor immune microenvironment in HNSCC was explored using single-cell analysis. HNSCC individuals were classified into high-risk and low-risk groups as per the median value of risk score. The study also investigated the variations in the infiltration status of immune cells, tumor microenvironment, sensitivity to immunotherapy and chemotherapeutics, as well as functional enrichment between the low-risk and high-risk categories. A total of 18 ARGs were incorporated in the formulation of the signature. Our signature's validity as a standalone predictive predictor was validated by multivariate Cox regression analysis and Kaplan-Meier survival analysis. Generally, the prognosis was worse for high-risk individuals. Subjects in the low-risk groups had a better prognosis and responded in a better way to combination immunotherapy, had higher immunological ratings and activity levels, and had more immune cell infiltration. In addition, gene set enrichment analysis findings showed that the low-risk subjects exhibited heightened activity in several immune-related pathways. However, the high-risk patients responded better to chemotherapy. The aim of this research was to develop a new ARG signature to predict the prognosis and sensitivity to immunotherapeutic and chemotherapeutic schemes for HNSCC patient. As a result, this could help spur the creation of new chemotherapeutics and immunotherapeutic approaches for patients with HNSCC.

Non-Coding RNAs in Oral Cancer: Emerging Roles and Clinical Applications

Oral cancer (OC) is among the most prevalent cancers in the world. Certain geographical areas are disproportionately affected by OC cases due to the regional differences in dietary habits, tobacco and alcohol consumption. However, conventional therapeutic methods do not yield satisfying treatment outcomes. Thus, there is an urgent need to understand the disease process and to develop diagnostic and therapeutic strategies for OC. In this review, we discuss the role of various types of ncRNAs in OC, and their promising clinical implications as prognostic or diagnostic markers and therapeutic targets. MicroRNA (miRNA), long ncRNA (lncRNA), circular RNA (circRNA), PIWI-interacting RNA (piRNA), and small nucleolar RNA (snoRNA) are the major ncRNA types whose involvement in OC are emerging. Dysregulated expression of ncRNAs, particularly miRNAs, lncRNAs, and circRNAs, are linked with the initiation, progression, as well as therapy resistance of OC via modulation in a series of cellular pathways through epigenetic, transcriptional, post-transcriptional, and translational modifications. Differential expressions of miRNAs and lncRNAs in blood, saliva or extracellular vesicles have indicated potential diagnostic and prognostic importance. In this review, we have summarized all the promising aspects of ncRNAs in the management of OC.

RNA-Based Liquid Biopsy in Head and Neck Cancer

Head and neck cancer (HNC) is a prevalent and diverse group of malignancies with substantial morbidity and mortality rates. Early detection and monitoring of HNC are crucial for improving patient outcomes. Liquid biopsy, a non-invasive diagnostic approach, has emerged as a promising tool for cancer detection and monitoring. In this article, we review the application of RNA-based liquid biopsy in HNC. Various types of RNA, including messenger RNA (mRNA), microRNA (miRNA), long non-coding RNA (lncRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), circular RNA (circRNA) and PIWI-interacting RNA (piRNA), are explored as potential biomarkers in HNC liquid-based diagnostics. The roles of RNAs in HNC diagnosis, metastasis, tumor resistance to radio and chemotherapy, and overall prognosis are discussed. RNA-based liquid biopsy holds great promise for the early detection, prognosis, and personalized treatment of HNC. Further research and validation are necessary to translate these findings into clinical practice and improve patient outcomes.

Underexplored reciprocity between genome-wide methylation status and long non-coding RNA expression reflected in breast cancer research: potential impacts for the disease management in the framework of 3P medicine

Breast cancer (BC) is the most common female malignancy reaching a pandemic scale worldwide. A comprehensive interplay between genetic alterations and shifted epigenetic regions synergistically leads to disease development and progression into metastatic BC. DNA and histones methylations, as the most studied epigenetic modifications, represent frequent and early events in the process of carcinogenesis. To this end, long non-coding RNAs (lncRNAs) are recognized as potent epigenetic modulators in pathomechanisms of BC by contributing to the regulation of DNA, RNA, and histones' methylation. In turn, the methylation status of DNA, RNA, and histones can affect the level of lncRNAs expression demonstrating the reciprocity of mechanisms involved. Furthermore, lncRNAs might undergo methylation in response to actual medical conditions such as tumor development and treated malignancies. The reciprocity between genome-wide methylation status and long non-coding RNA expression levels in BC remains largely unexplored. Since the bio/medical research in the area is, per evidence, strongly fragmented, the relevance of this reciprocity for BC development and progression has not yet been systematically analyzed. Contextually, the article aims at:consolidating the accumulated knowledge on both-the genome-wide methylation status and corresponding lncRNA expression patterns in BC andhighlighting the potential benefits of this consolidated multi-professional approach for advanced BC management. Based on a big data analysis and machine learning for individualized data interpretation, the proposed approach demonstrates a great potential to promote predictive diagnostics and targeted prevention in the cost-effective primary healthcare (sub-optimal health conditions and protection against the health-to-disease transition) as well as advanced treatment algorithms tailored to the individualized patient profiles in secondary BC care (effective protection against metastatic disease). Clinically relevant examples are provided, including mitochondrial health control and epigenetic regulatory mechanisms involved.

Expression and molecular regulation of non-coding RNAs in HPV-positive head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is the sixth most prevalent malignancy worldwide. Accumulating evidence suggests that persistent HPV infection is closely related to a subset of HNSCC types, and the incidence of human papillomavirus (HPV)-positive HNSCC has been annually increasing in recent decades. Although the carcinogenesis of HPV-positive HNSCC has not been completely elucidated, it has been well confirmed that E6 and E7, the main viral oncoproteins are responsible for the maintenance of malignant transformation, promotion of cell proliferation, and increase in tumor invasion. Moreover, compared with HPV-negative HNSCC, HPV-positive HNSCC shows some special clinical-pathological features, which are possibly related to HPV infection and their specific regulatory mechanisms. Non-coding RNA (ncRNA) is a class of RNA lacking the protein-coding function and playing a critical regulatory role via multiple complex molecular mechanisms. NcRNA is an important regulatory pattern of epigenetic modification, which can exert significant effects on HPV-induced tumorigenesis and progression by deregulating downstream genes. However, the knowledge of ncRNAs is still limited, hence, a better understanding of ncRNAs could provide some insights for exploring the carcinogenesis mechanism and identifying valuable biomarkers in HPV-positive HNSCC. Therefore, in this review, we mainly focused on the expression profile of ncRNAs (including lncRNA, miRNA, and circRNA) and explored their regulatory role in HPV-positive HNSCC, aiming to clarify the regulatory mechanism of ncRNAs and identify valuable biomarkers for HPV-positive HNSCC.

Machine learning and BP neural network revealed abnormal B cell infiltration predicts the survival of lung cancer patients

FAM83A gene is related to the invasion and metastasis of various tumors. However, the abnormal immune cell infiltration associated with the gene is poorly understood in the pathogenesis and prognosis of NSCLC. Based on the TCGA and GEO databases, we used COX regression and machine learning algorithms (CIBERSORT, random forest, and back propagation neural network) to study the prognostic value of FAM83A and immune infiltration characteristics in NSCLC. High FAM83A expression was significantly associated with poor prognosis of NSCLC patients (p = 0.00016), and had excellent prognostic independence. At the same time, the expression level of FAM83A is significantly related to the T, N, and Stage. Subsequently, based on machine learing strategies, we found that the infiltration level of naive B cells was negatively correlated with the expression of FAM83A. The low infiltration of naive B cells was significantly related to the poor overall survival rate of NSCLC (p = 0.0072). In addition, Cox regression confirmed that FAM83A and naive B cells are risk factors for the prognosis of NSCLC patients. The nomogram combining FAM83A and naive B cells (C-index = 0.748) has a more accurate prognostic ability than the Stage (C-index = 0.651) system. Our analysis shows that abnormal infiltration of naive B cells associated with FAM83A is a key factor in the prognostic prediction of NSCLC patients.

Identification of key snoRNAs serves as biomarkers for hepatocellular carcinoma by bioinformatics methods

BACKGROUND

Hepatocellular carcinoma (HCC) is a common malignancy with high mortality and poor prognosis due to a lack of predictive markers. However, research on small nuclear RNAs (snoRNAs) in HCC were very little. This study aimed to identify a potential diagnostic and prognostic snoRNA signature for HCC.

METHODS

HCC datasets from the cancer genome atlas (TCGA) and international cancer genome consortium (ICGC) cohorts were used. Differentially expressed snoRNA (DEs) were identified using the limma package. Based on the DEs, diagnostic and prognostic models were established by the least absolute shrinkage and selection operator (LASSO) regression and COX analysis, and Kaplan-Meier (K-M) survival analysis and receiver operating characteristic (ROC) curve analysis were conducted to evaluate the efficiency of signatures. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to analyze the risk score and further explore the potential correlation between the risk groups and tumor immune status in TCGA. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to determine the functions of key snoRNAs.

RESULTS

We constructed a 6-snoRNAs signature which could classify patients into high- or low-risk groups and found that patients in the high-risk group had a worse prognosis than those in the low-risk group and were significantly involved in p53 processes. Tumor immune status analysis revealed that CTLA4 and PDCD1 (PD1) were highly expressed in the high-risk group, which responded to PD1 inhibitor therapy. Additionally, a 25-snoRNAs diagnostic signature was constructed with an area under the curve (AUC) of 0.933 for distinguishing HCCs from normal controls. Finally, 3 key snoRNAs (SNORA11, SNORD124, and SNORD46) were identified with both diagnostic and prognostic efficacy, some of which were closely related to the spliceosome and Notch signaling pathways.

CONCLUSIONS

Our study identified 6 snoRNAs that may serve as novel prognostic models and 3 key snoRNAs with both diagnostic and prognostic efficacy for HCC.

Spectrum-Effect Relationship-Based Strategy Combined with Molecular Docking to Explore Bioactive Flavonoids from Sceptridium ternatum

Sceptridium ternatum is a herbaceous plant with significant potential for pharmaceutical and cosmetic applications. In this study, we established a spectrum-effect relationship-based strategy to investigate the bioactive basis and tissue distribution in S. ternatum. First, a phytochemical analysis on the ethanol extracts from roots, stems, and leaves of S. ternatum was performed using the colorimetric method, high-performance liquid chromatography-ultraviolet (HPLC-UV), and high-performance liquid chromatography-electrospray ionization quadrupole time-of-flight mass spectrometry (HPLC-ESI-Q-TOF-MS/MS). Then, radical scavenging assays and the lipopolysaccharide-stimulated RAW 264.7 cell model were used to estimate the antioxidant and anti-inflammatory activities, respectively. Spectrum-effect relationship analysis and molecular docking were further employed to evaluate the correlation between the phytochemical profile and anti-inflammatory activity. Our results demonstrate that S. ternatum leaves contained the most abundant flavonoids and exerted the best biological activities. Their IC₅₀ values for scavenging 2,2'-azino-bis (3-ethylbenzthiazoline-6-sulfonic acid) and 1,1-diphenyl-2-picrylhydrazyl radicals were 2.43 ± 0.13 and 5.36 ± 0.54 mg/mL, respectively. In lipopolysaccharide-stimulated RAW 264.7 cells, the leaf extract caused the greatest reduction in nitric oxide production (38.15%) and interleukin-6 release (110.86%). Spectrum-effect relationship analysis and molecular docking indicated that quercetin 3-O-rhamnoside-7-O-glucoside possessed high anti-inflammatory activity by binding with interleukin-6. In conclusion, S. ternatum is a rich source of bioactive flavonoids with potential for exploitation in the prevention and treatment of oxidative stress and inflammation-related pathologies.

Identification of three small nucleolar RNAs (snoRNAs) as potential prognostic markers in diffuse large B-cell lymphoma

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is a non-Hodgkin lymphoma with high mortality rates. Small nucleolar RNAs (snoRNAs) are tumor-specific biological markers, but there are few studies on the role of snoRNAs in DLBCL.

MATERIALS AND METHODS

Survival-related snoRNAs were selected to construct a specific snoRNA-based signature via computational analyses (Cox regression and independent prognostic analyses) to predict the prognosis of DLBCL patients. To assist in clinical applications, a nomogram was built by combining the risk model and other independent prognostic factors. Pathway analysis, gene ontology analysis, transcription factor enrichment, protein-protein interactions, and single nucleotide variant analysis were used to explore the potential biological mechanisms of co-expressed genes.

RESULTS

Twelve prognosis-correlated snoRNAs were selected from the DLBCL patient cohort of microarray profiles, and a three-snoRNA signature consisting of SNORD1A, SNORA60, and SNORA66 was constructed. DLBCL patients could be divided into high-risk and low-risk cohorts using the risk model, and the high-risk group and activated B cell-like (ABC) type DLBCL were linked with disappointing survival. In addition, SNORD1A co-expressed genes were inseparably linked to the biological functions of the ribosome and mitochondria. Potential transcriptional regulatory networks have also been identified. MYC and RPL10A were the most mutated SNORD1A co-expressed genes in DLBCL.

CONCLUSION

Put together, our findings explored the potential biological effects of snoRNAs in DLBCL, and provided a new predictor for DLBCL prediction.

The Potential Role of Small Nucleolar RNAs in Cancers – An Evidence Map

Purpose

Cancer seriously endangers human health in every country of the world. New evidence shows that small nucleolar RNAs play important roles in tumorigenesis. Herein, we created this evidence map to systematically assess the impact of dysregulated snoRNAs on cancers.

Methods

We searched four databases to February 2022 using the keywords, “carcinoma”, “neoplasms”, “tumor”, “cancer”, “snoRNA”, and “small nucleolar rna”. The research data were independently screened by two reviewers. Bubble plot, mind map, heatmap were used to depict the relationship between snoRNAs and cancers.

Results

In total, 102 studies met the inclusion criteria and were analyzed in this evidence map. In this study, we found that dysregulated snoRNAs were statistically associated with the clinicopathological characteristics of cancer patients, and affected tumor cell phenotypes. Abnormally expressed snoRNAs were associated with poor survival in cancer patients. Current research confirmed that snoRNAs have good diagnostic efficiency for cancers. snoRNAs could modulate biological processes and signaling pathways of different cancer cells by altering rRNA, regulating mRNA, and recruiting protein factors.

Conclusion

Taken all together, ectopic snoRNAs may serve as new biomarkers for clinical assessment, diagnostic, prognostic prediction of cancer patients, and provide a potential therapeutic strategy for cancer treatment. This article provided a visual analysis of existing evidence on snoRNAs and cancers, which can offer useful information for different researchers interested in snoRNAs.

Integrative genomic analysis of a novel small nucleolar RNAs prognostic signature in patients with acute myelocytic leukemia

This study mainly used The Cancer Genome Atlas (TCGA) RNA sequencing dataset to screen prognostic snoRNAs of acute myeloid leukemia (AML), and used for the construction of prognostic snoRNAs signature for AML. A total of 130 AML patients with RNA sequencing dataset were used for prognostic snoRNAs screenning. SnoRNAs co-expressed genes and differentially expressed genes (DEGs) were used for functional annotation, as well as gene set enrichment analysis (GSEA). Connectivity Map (CMap) also used for potential targeted drugs screening. Through genome-wide screening, we identified 30 snoRNAs that were significantly associated with the prognosis of AML. Then we used the step function to screen a prognostic signature composed of 14 snoRNAs (SNORD72, SNORD38, U3, SNORA73B, SNORD79, SNORA73, SNORD12B, SNORA74, SNORD116-12, SNORA65, SNORA14, snoU13, SNORA75, SNORA31), which can significantly divide AML patients into high- and low-risk groups. Through GSEA, snoRNAs co-expressed genes and DEGs functional enrichment analysis, we screened a large number of potential functional mechanisms of this prognostic signature in AML, such as phosphatidylinositol 3-kinase-Akt, Wnt, epithelial to mesenchymal transition, T cell receptors, NF-kappa B, mTOR and other classic cancer-related signaling pathways. In the subsequent targeted drug screening using CMap, we also identified six drugs that can be used for AML targeted therapy, they were alimemazine, MG-262, fluoxetine, quipazine, naltrexone and oxybenzone. In conclusion, our current study was constructed an AML prognostic signature based on the 14 prognostic snoRNAs, which may serve as a novel prognostic biomarker for AML.

The role of OncoSnoRNAs and Ribosomal RNA 2'-O-methylation in Cancer

Ribosomes are essential nanomachines responsible for all protein production in cells. Ribosome biogenesis and function are energy costly processes, they are tightly regulated to match cellular needs. In cancer, major pathways that control ribosome biogenesis and function are often deregulated to ensure cell survival and to accommodate the continuous proliferation of tumour cells. Ribosomal RNAs (rRNAs) are abundantly modified with 2'-O-methylation (Nm, ribomethylation) being one of the most common modifications. In eukaryotic ribosomes, ribomethylation is performed by the methyltransferase Fibrillarin guided by box C/D small nucleolar RNAs (snoRNAs). Accumulating evidences indicate that snoRNA expression and ribosome methylation profiles are altered in cancer. Here we review our current knowledge on differential snoRNA expression and rRNA 2ʹ-O methylation in the context of human malignancies, and discuss the consequences and opportunities for cancer diagnostics, prognostics, and therapeutics.

Identification of Immune Subtypes for Predicting the Prognosis of Patients in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a common malignancy with poor prognosis and immune response, which plays an important role in tumor progression. Recently, immunotherapies have revolutionized the therapeutic means of malignancies including HNSCC. However, the relationship between immunophenotypes of HNSCC and its clinical response to immune-checkpoint inhibitors remains unclear. We aim to identify molecular subtyping related to distinct immunophenotypes in HNSCC. Consensus clustering algorithm was conducted for subtyping. Immunophenotypes between subtypes were compared according to infiltrating immunocytes, immune reactions, major histocompatibility complex (MHC) family, immunoinhibitory, immunostimulatory and immune scores. The relationship between immunophenotype and genotype was investigated from gene mutation and tumor mutation burden. The potential response of Immune-checkpoint blockade (ICB) therapy was estimated with TIDE and ImmuCellAI algorithms, and immune-checkpoint genes. The immune characteristics were also investigated. Biological functions were annotated by the gene-set enrichment analysis (GSEA) algorithm. Two distinct immune subtypes of HNSCC with different survival outcomes, biological characteristics, immunophenotype, and ICB response were identified. The subtype-1 was featured with better prognosis, more infiltrated immunocytes, stronger immune reaction, higher immune-related gene expression, higher immune-checkpoint gene expression (PD-1, PD-L1, and CTLA-4), and better ICB response. A higher immune response in subtype-1 was also revealed by GSEA. Subtype-1 possessed a higher immune response and more sensitivity to ICB therapy leading to a better prognosis. These findings may shed promising light on the immunotherapy strategy in HNSCC

TP53-Associated Ion Channel Genes Serve as Prognostic Predictor and Therapeutic Targets in Head and Neck Squamous Cell Carcinoma

TP53 mutations are the most occurred mutation in HNSCC which might affect the ion channel genes. We aim to investigate the ion channel gene alteration under TP53 mutation and their prognostic implication. The overall mutation status of HNSCC were explored. By screening the TP53-associated ion channel genes (TICGs), an ion channel prognostic signature (ICPS) was established through a series of machine learning algorithms. The ICPS was then evaluated and its clinical significance was explored. 82 TICGs differentially expressed between TP53WT and TP53MUT were screened. Using univariate regression analysis and LASSO regression analysis and multivariate regression analysis, an ICPS containing 7 ion channel genes was established. A series of evaluation was carried out which proved the predictive ability of ICPS. Functional analysis of ICPS revealed that cancer-related pathways were enriched in high-risk group. Next, for clinical application, a nomogram was constructed based on ICPS and other independent clinicopathological factors. TP53 mutation status strongly affects the expression of ion channel genes. The ICPM we have identified is a strong indicator for HNSCC prognosis and could help with patient stratification as well as identification of novel drug targets.

Identification of potential prognostic small nucleolar RNA biomarkers for predicting overall survival in patients with sarcoma

Objective

The main purpose of the present study is to screen prognostic small nucleolar RNA (snoRNA) markers using the RNA‐sequencing (RNA‐seq) dataset of The Cancer Genome Atlas (TCGA) sarcoma cohort.

Methods

The sarcoma RNA‐seq dataset comes from the TCGA cohort. A total of 257 sarcoma patients were included into the prognostic analysis. Multiple bioinformatics analysis methods for functional annotation of snoRNAs and screening of targeted drugs, including biological network gene ontology tool, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and connectivity map (CMap) are used.

Results

We had identified 15 snoRNAs that were significantly related to the prognosis of sarcoma and constructed a prognostic signature based on four prognostic snoRNA (U3, SNORA73B, SNORD46, and SNORA26) expression values. Functional annotation of these four snoRNAs by their co‐expression genes suggests that some of them were closely related to cell cycle‐related biological processes and tumor‐related signaling pathways, such as Wnt, mitogen‐activated protein kinase, target of rapamycin, and nuclear factor‐kappa B signaling pathway. GSEA of the risk score suggests that high risk score phenotype was significantly enriched in cell cycle‐related biological processes, protein SUMOylation, DNA replication, p53 binding, regulation of DNA repair, and DNA methylation, as well as Myc, Wnt, RB1, E2F, and TEL pathways. Then we also used the CMap online tool to screen five targeted drugs (rilmenidine, pizotifen, amiprilose, quipazine, and cinchonidine) for this risk score model in sarcoma.

Conclusion

Our study have identified 15 snoRNAs that may be serve as novel prognostic biomarkers for sarcoma, and constructed a prognostic signature based on four prognostic snoRNA expression values.

Application of Machine Learning in Developing a Novelty Five-Pseudogene Signature to Predict Prognosis of Head and Neck Squamous Cell Carcinoma: A New Aspect of "Junk Genes" in Biomedical Practice

Head and neck squamous cell carcinoma (HNSCC) is the sixth malignancy, which is characterized by poor prognosis or high mortality because of the lack of predicting markers. Aberrant cancer pseudogenes have been found predictive for prognosis. We aim to identify a pseudogene-based prognosis signature for HNSCC by machine learning. RNA-seq data were downloaded from The Cancer Genome Atlas, and 700 differentially-expressed pseudogenes were identified. The survival-related pseudogenes were screened through COX-regression analysis, which includes univariate regression, least absolute shrinkage and selection operator regression, and multivariate regression, and a five-pseudogene signature was constructed. The value of prediction for the signature was validated in multiple subgroups in terms of survival. Gene set enrichment analysis (GSEA) and coexpression analysis were used to determine the underlying biological functions. Seven hundred dysregulated pseudogenes were identified, and the five-pseudogene signature can distinguish the low-risk and high-risk patients for both training and testing sets and predicted prognosis with high sensitivity and specificity. Furthermore, the signature was applicable to patients of different genders, ages, stages, and grades. Coexpression analysis revealed that the five-pseudogene is associated with immune system. GSEA showed cancer-related biological process and pathways the five-pseudogene involved in. The five-pseudogene signature is not only a novel marker for prognosis but also a promising signature for monitoring therapeutic schedule. Therefore, our findings may have potential clinical significance.

Expression scoring of a small-nucleolar-RNA signature identified by machine learning serves as a prognostic predictor for head and neck cancer

Head and neck squamous cell carcinoma (HNSCC) is a common malignancy with high mortality and poor prognosis due to a lack of predictive markers. Increasing evidence has demonstrated small nucleolar RNAs (snoRNAs) play an important role in tumorigenesis. The aim of this study was to identify a prognostic snoRNA signature of HNSCC. Survival-related snoRNAs were screened by Cox regression analysis (univariate, least absolute shrinkage and selection operator, and multivariate). The predictive value was validated in different subgroups. The biological functions were explored by coexpression analysis and gene set enrichment analysis (GSEA). One hundred and thirteen survival-related snoRNAs were identified, and a five-snoRNA signature predicted prognosis with high sensitivity and specificity. Furthermore, the signature was applicable to patients of different sexes, ages, stages, grades, and anatomic subdivisions. Coexpression analysis and GSEA revealed the five-snoRNA are involved in regulating malignant phenotype and DNA/RNA editing. This five-snoRNA signature is not only a promising predictor of prognosis and survival but also a potential biomarker for patient stratification management.