Potential biomarkers and therapeutic targets in cervical cancer: Insights from the meta-analysis of transcriptomics data within network biomedicine perspective

Diagnostic value for methylation in cervical cancer based on a small-molecule fluorescent probe targeting DNMT1

BACKGROUND

Value analysis of a small-molecule fluorescent probe for methylation detection in different cervical lesions.

MATERIALS AND METHODS

(1) The grayscale values of distinct lesion tissues were remarkably distinct among the four groups (p < 0.05). The comparison of the grayscale value between the two groups showed that the CA group noticeably exceeded the LSIL and cervicitis groups, and the HSIL group was apparently higher than the LSIL and cervicitis groups (p < 0.05); (2) The mean grayscale values of the enrolled subjects were calculated with 55.21 as the midline, with >55.21 as positive and ≤55.21 as negative.

RESULTS

The results showed that the positive rate of the cervicitis group was 0.00%, the LSIL group 67.74%, the HSIL group 83.33%, and the CA group 100.00%. The results among the four groups were notably distinct (p < 0.05); (3) The comparison among DAPI, probe, bright, and merged images of cervicitis, LSIL, HSIL, and CA indicated that different cervical lesions were with quite various stains.

CONCLUSION

The grayscale value, positive rate, and stained picture of distinct cervical lesions were remarkably different. The small-molecule fluorescent probe has a good value in differentiating cervical lesions and can be considered for popularization and application.

Unraveling bacterial stress responses: implications for next-generation antimicrobial solutions

The accelerated spread of antimicrobial-resistant bacteria has caused a serious health problem and rendered antimicrobial treatments ineffective. Innovative approaches are crucial to overcome the health threat posed by resistant pathogens and prevent the emergence of untreatable infections. Triggering stress responses in bacteria can diminish susceptibility to various antimicrobials by inducing resistance mechanisms. Therefore, a thorough understanding of stress response control, especially in relation to antimicrobial resistance, offers valuable perspectives for innovative and efficient therapeutic approaches to combat antimicrobial resistance. The aim of this study was to evaluate the stress responses of 8 different bacteria by analyzing reporter metabolites, around which significant alterations were observed, using a pathway-driven computational approach. For this purpose, the transcriptomic data that the bacterial pathogens were grown under 11 different stress conditions mimicking the human host environments were integrated with the genome-scale metabolic models of 8 pathogenic species (Enterococcus faecalis OG1R, Escherichia coli EPEC O127:H6 E2348/69, Escherichia coli ETEC H10407, Escherichia coli UPEC 536, Klebsiella pneumoniae MGH 78578, Pseudomonas aeruginosa PAO1, Staphylococcus aureus MRSA252, and Staphylococcus aureus MSSA476). The resulting reporter metabolites were enriched in multiple metabolic pathways, with cofactor biosynthesis being the most important. The results of this study will serve as a guide for the development of antimicrobial agents as they provide a first insight into potential drug targets.

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models

The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.

Microbiota changes: the unseen players in cervical cancer progression

Cervical cancer ranks among the most prevalent cancers globally with high-risk human papillomaviruses implicated in nearly 99% of cases. However, hidden players such as changes in the microbiota are now being examined as potential markers in the progression of this disease. Researchers suggest that changes in the vaginal microbiota might correlate with cervical cancer. This review provides a comprehensive look at the microbiota changes linked with the advancement of cervical cancer. It also scrutinizes the databases from past studies on the microbiota during healthy and cancerous stages, drawing connections between prior findings concerning the role of the microbiota in the progression of cervical cancer. Preliminary findings identify Fusobacterium spp., Peptostreptococcus spp., Campylobacter spp., and Haemophilus spp., as potential biomarkers for cervical cancer progression. Alloscardovia spp., Eubacterium spp., and Mycoplasma spp. were identified as potential biomarkers for HPVs (+), while Methylobacterium spp. may be indicative of HPV (-). However, the study's limitations, including potential biases and methodological constraints, underscore the need for further research to validate these findings and delve deeper into the microbiota's role in HPV development. Despite these limitations, the review provides valuable insights into microbiota trends during cervical cancer progression, offering direction for future research. The review summarizes key findings from previous studies on microbiota during healthy and cancerous stages, as well as other conditions such as CIN, SIL, HPV (+), and HPV (-), indicating a promising area for further investigation. The consistent presence of HPV across all reported cervical abnormalities, along with the identification of distinct bacterial genera between cancerous and control samples, suggests a potential link that merits further exploration. In conclusion, a more profound understanding of the microbial landscape could elucidate the pathogenesis of cervical diseases and inform future strategies for diagnosis, prevention, and treatment.

Non-apoptotic cell death programs in cervical cancer with an emphasis on ferroptosis

BACKGROUND

Cervical cancer, a pernicious gynecological malignancy, causes the mortality of hundreds of thousands of females worldwide. Despite a considerable decline in mortality, the surging incidence rate among younger women has raised serious concerns. Immortality is the most important characteristic of tumor cells, hence the carcinogenesis of cervical cancer cells pivotally requires compromising with cell death mechanisms.

METHODS

The current study comprehensively reviewed the mechanisms of non-apoptotic cell death programs to provide possible disease management strategies.

RESULTS

Comprehensive evidence has stated that focusing on necroptosis, pyroptosis, and autophagy for disease management is associated with significant limitations such as insufficient understanding, contradictory functions, dependence on disease stage, and complexity of intracellular pathways. However, ferroptosis represents a predictable role in cervix carcinogenesis, and ferroptosis-related genes demonstrate a remarkable correlation with patient survival and clinical outcomes.

CONCLUSION

Ferroptosis may be an appropriate option for disease management strategies from predicting prognosis to treatment.

A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine

Ovarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, "SOV-module" was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant (p-value = 1.36 × 10-4) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.

Exosomes in Cancer: Diagnostic and Therapeutic Applications

Small extracellular vesicles called exosomes are produced by cells and contain a range of biomolecules, including proteins, lipids, and nucleic acids. Exosomes have been implicated in the development and spread of cancer, and recent studies have shown that their contents may be exploited as biomarkers for early detection and ongoing surveillance of the disease. In this review article, we summarize the current knowledge on exosomes as biomarkers of cancer. We discuss the various methods used for exosome isolation and characterization, as well as the different types of biomolecules found within exosomes that are relevant for cancer diagnosis and prognosis. We also highlight recent studies that have demonstrated the utility of exosomal biomarkers in different types of cancer, such as lung cancer, breast cancer, and pancreatic cancer. Overall, exosomes show great promise as noninvasive biomarkers for cancer detection and monitoring. Exosomes have the ability to transform cancer diagnostic and therapeutic paradigms, providing promise for more efficient and individualized. This review seeks to serve as an inspiration for new ideas and research in the never-ending fight against cancer. Moreover, further studies are needed to validate their clinical utility and establish standardized protocols for their isolation and analysis. With continued research and development, exosomal biomarkers have the potential to revolutionize cancer diagnosis and treatment.

Integrated Network Pharmacology and Metabolomics to Dissect the Mechanisms of Naringin for Treating Cervical Cancer

INTRODUCTION

Cervical cancer is one of the malignant cancers with high mortality among women worldwide. Although vaccines and early detection have reduced cervical cancer mortality, it remains a malignancy with a high mortality rate in women.

OBJECTIVES

We aimed to develop a novel integrated strategy that combines metabolomics with network pharmacology to explore the therapeutic mechanisms of naringin in cervical cancer. The mechanism of naringin intervention in cervical cancer was initially clarified by metabolomics and network pharmacology.

METHODS

The method of LC-MS and network pharmacology for the detection and identification of potential biomarkers and the mechanisms of action of naringin was used. The metabolites were detected and identified based on ultra-high-performance liquid chromatography coupled with Quadrupole- Exactive Orbitrap MS (UHPLC-Q-Exactive Orbitrap MS) and followed by the network pharmacology analysis.

RESULTS

In network pharmacology, naringin played a synergetic role through regulatory shared pathways, such as steroid hormone biosynthesis, sphingolipid signaling pathway and arachidonic acid metabolism, etc. Besides, the metabolomics analysis showed that 20 differential metabolites and 10 metabolic pathways were mainly involved in the therapeutic effect of naringin on cervical cancer. The result showed that naringin treatment for cervical cancer mainly occurs through the following metabolic pathways: amino acid metabolism and arachidonic acid metabolism.

CONCLUSION

This work provided valuable information and a scientific basis for further studies of naringin in the treatment of cervical cancer.

Application of Protein-Protein Interaction Network Analysis in Order to Identify Cervical Cancer miRNA and mRNA Biomarkers

Cervical cancer (CC) is one of the world's most common and severe cancers. This cancer includes two histological types: squamous cell carcinoma (SCC) and adenocarcinoma (ADC). The current study aims at identifying novel potential candidate mRNA and miRNA biomarkers for SCC based on a protein-protein interaction (PPI) and miRNA-mRNA network analysis. The current project utilized a transcriptome profile for normal and SCC samples. First, the PPI network was constructed for the 1335 DEGs, and then, a significant gene module was extracted from the PPI network. Next, a list of miRNAs targeting module's genes was collected from the experimentally validated databases, and a miRNA-mRNA regulatory network was formed. After network analysis, four driver genes were selected from the module's genes including MCM2, MCM10, POLA1, and TONSL and introduced as potential candidate biomarkers for SCC. In addition, two hub miRNAs, including miR-193b-3p and miR-615-3p, were selected from the miRNA-mRNA regulatory network and reported as possible candidate biomarkers. In summary, six potential candidate RNA-based biomarkers consist of four genes containing MCM2, MCM10, POLA1, and TONSL, and two miRNAs containing miR-193b-3p and miR-615-3p are opposed as potential candidate biomarkers for CC.

Transcriptomic analyses of ovarian clear-cell carcinoma with concurrent endometriosis

Introduction

Endometriosis, a benign inflammatory disease whereby endometrial-like tissue grows outside the uterus, is a risk factor for endometriosis-associated ovarian cancers. In particular, ovarian endometriomas, cystic lesions of deeply invasive endometriosis, are considered the precursor lesion for ovarian clear-cell carcinoma (OCCC).

Methods

To explore this transcriptomic landscape, OCCC from women with pathology-proven concurrent endometriosis (n = 4) were compared to benign endometriomas (n = 4) by bulk RNA and small-RNA sequencing.

Results

Analysis of protein-coding genes identified 2449 upregulated and 3131 downregulated protein-coding genes (DESeq2, P< 0.05, log2 fold-change > |1|) in OCCC with concurrent endometriosis compared to endometriomas. Gene set enrichment analysis showed upregulation of pathways involved in cell cycle regulation and DNA replication and downregulation of pathways involved in cytokine receptor signaling and matrisome. Comparison of pathway activation scores between the clinical samples and publicly-available datasets for OCCC cell lines revealed significant molecular similarities between OCCC with concurrent endometriosis and OVTOKO, OVISE, RMG1, OVMANA, TOV21G, IGROV1, and JHOC5 cell lines. Analysis of miRNAs revealed 64 upregulated and 61 downregulated mature miRNA molecules (DESeq2, P< 0.05, log2 fold-change > |1|). MiR-10a-5p represented over 21% of the miRNA molecules in OCCC with endometriosis and was significantly upregulated (NGS: log2fold change = 4.37, P = 2.43e-18; QPCR: 8.1-fold change, P< 0.05). Correlation between miR-10a expression level in OCCC cell lines and IC50 (50% inhibitory concentration) of carboplatin in vitro revealed a positive correlation (R2 = 0.93). MiR-10a overexpression in vitro resulted in a significant decrease in proliferation (n = 6; P< 0.05) compared to transfection with a non-targeting control miRNA. Similarly, the cell-cycle analysis revealed a significant shift in cells from S and G2 to G1 (n = 6; P< 0.0001). Bioinformatic analysis predicted that miR-10a-5p target genes that were downregulated in OCCC with endometriosis were involved in receptor signaling pathways, proliferation, and cell cycle progression. MiR-10a overexpression in vitro was correlated with decreased expression of predicted miR-10a target genes critical for proliferation, cell-cycle regulation, and cell survival including [SERPINE1 (3-fold downregulated; P< 0.05), CDK6 (2.4-fold downregulated; P< 0.05), and RAP2A (2-3-fold downregulated; P< 0.05)].

Discussion

These studies in OCCC suggest that miR-10a-5p is an impactful, potentially oncogenic molecule, which warrants further studies.

A systematic review and meta-analysis of miRNAs for the detection of cervical cancer

Aim: This study aimed to critically appraise the evidence of the diagnostic effectiveness of miRNAs for the detection of cervical cancer. Methods & materials: A systematic review and meta-analysis was performed, searching PubMed, EMBASE and Web of Science. An umbrella meta-analysis of meta-analyses of individual biomarkers was performed. A Grading of Recommendations, Assessment, Development and Evaluations (GRADE) assessment of evidence was also performed. Results: A total of 52 miRNAs were included. Umbrella meta-analysis revealed significant heterogeneity in terms of sensitivity, specificity, receiver operating characteristic (ROC), positive predictive value and/or negative predictive value. Umbrella effects were 0.76 (95% CI: 0.73-0.78), 0.78 (95% CI: 0.75-0.81), 0.77 (95% CI: 0.75-0.80), 0.75 (95% CI: 0.71-0.79) and 0.76 (95% CI: 0.74-0.79), respectively. Conclusion: Moderate quality evidence suggested miR199a-5p, miR21-5p and miR-141a had excellent diagnostic performance.

Systematic approach to identify therapeutic targets and functional pathways for the cervical cancer

BACKGROUND

In today's society, cancer has become a big concern. The most common cancers in women are breast cancer (BC), endometrial cancer (EC), ovarian cancer (OC), and cervical cancer (CC). CC is a type of cervix cancer that is the fourth most common cancer in women and the fourth major cause of death.

RESULTS

This research uses a network approach to discover genetic connections, functional enrichment, pathways analysis, microRNAs transcription factors (miRNA-TF) co-regulatory network, gene-disease associations, and therapeutic targets for CC. Three datasets from the NCBI's GEO collection were considered for this investigation. Then, using a comparison approach between the datasets, 315 common DEGs were discovered. The PPI network was built using a variety of combinatorial statistical approaches and bioinformatics tools, and the PPI network was then utilized to identify hub genes and critical modules.

CONCLUSION

Furthermore, we discovered that CC has specific similar links with the progression of different tumors using Gene Ontology terminology and pathway analysis. Transcription factors-gene linkages, gene-disease correlations, and the miRNA-TF co-regulatory network were revealed to have functional enrichments. We believe the candidate drugs identified in this study could be effective for advanced CC treatment.

Drug repositioning via host-pathogen protein-protein interactions for the treatment of cervical cancer

Introduction

Integrating interaction data with biological knowledge can be a critical approach for drug development or drug repurposing. In this context, host-pathogen-protein-protein interaction (HP-PPI) networks are useful instrument to uncover the phenomena underlying therapeutic effects in infectious diseases, including cervical cancer, which is almost exclusively due to human papillomavirus (HPV) infections. Cervical cancer is one of the second leading causes of death, and HPV16 and HPV18 are the most common subtypes worldwide. Given the limitations of traditionally used virus-directed drug therapies for infectious diseases and, at the same time, recent cancer statistics for cervical cancer cases, the need for innovative treatments becomes clear.

Methods

Accordingly, in this study, we emphasize the potential of host proteins as drug targets and identify promising host protein candidates for cervical cancer by considering potential differences between HPV subtypes (i.e., HPV16 and HPV18) within a novel bioinformatics framework that we have developed. Subsequently, subtype-specific HP-PPI networks were constructed to obtain host proteins. Using this framework, we next selected biologically significant host proteins. Using these prominent host proteins, we performed drug repurposing analysis. Finally, by following our framework we identify the most promising host-oriented drug candidates for cervical cancer.

Results

As a result of this framework, we discovered both previously associated and novel drug candidates, including interferon alfacon-1, pimecrolimus, and hyaluronan specifically for HPV16 and HPV18 subtypes, respectively.

Discussion

Consequently, with this study, we have provided valuable data for further experimental and clinical efforts and presented a novel bioinformatics framework that can be applied to any infectious disease.

An integrative approach toward identification and analysis of therapeutic targets involved in HPV pathogenesis with a focus on carcinomas

BACKGROUND

Persistent infection of high-risk HPVs is known to cause diverse carcinomas, mainly cervical, oropharyngeal, penile, etc. However, efficient treatment is still lacking.

OBJECTIVE

Identify and analyze potential therapeutic targets involved in HPV oncogenesis and repurposing drug candidates.

METHODS

Integrative analyses were performed on the compendium of 1887 HPV infection-associated or integration-driven disrupted genes cataloged from the Open Targets Platform and HPVbase resource. Potential target genes are prioritized using STRING, Cytoscape, cytoHubba, and MCODE. Gene ontology and KEGG pathway enrichment analysis are performed. Further, TCGA cancer genomic data of CESC and HNSCC is analyzed. Moreover, regulatory networks are also deduced by employing NetworkAnalyst.

RESULTS

We have implemented a unique approach for identifying and prioritizing druggable targets and repurposing drug candidates against HPV oncogenesis. Overall, hundred key genes with 44 core targets were prioritized with transcription factors (TFs) and microRNAs (miRNAs) regulators pertinent to HPV pathogenesis. Genomic alteration profiling further substantiated our findings. Among identified druggable targets, TP53, NOTCH1, PIK3CA, EP300, CREBBP, EGFR, ERBB2, PTEN, and FN1 are frequently mutated in CESC and HNSCC. Furthermore, PIK3CA, CCND1, RFC4, KAT5, MYC, PTK2, EGFR, and ERBB2 show significant copy number gain, and FN1, CHEK1, CUL1, EZH2, NRAS, and H2AFX was marked for the substantial copy number loss in both carcinomas. Likewise, under-explored relevant regulators, i.e., TFs (HINFP, ARID3A, NFATC2, NKX3-2, EN1) and miRNAs (has-mir-98-5p, has-mir-24-3p, has-mir-192-5p, has-mir-519d-3p) is also identified.

CONCLUSIONS

We have identified potential therapeutic targets, transcriptional and post-transcriptional regulators to explicate HPV pathogenesis as well as potential repurposing drug candidates. This study would aid in biomarker and drug discovery against HPV-mediated carcinoma.

A survey on multi-omics-based cancer diagnosis using machine learning with the potential application in gastrointestinal cancer

Gastrointestinal cancer is becoming increasingly common, which leads to over 3 million deaths every year. No typical symptoms appear in the early stage of gastrointestinal cancer, posing a significant challenge in the diagnosis and treatment of patients with gastrointestinal cancer. Many patients are in the middle and late stages of gastrointestinal cancer when they feel uncomfortable, unfortunately, most of them will die of gastrointestinal cancer. Recently, various artificial intelligence techniques like machine learning based on multi-omics have been presented for cancer diagnosis and treatment in the era of precision medicine. This paper provides a survey on multi-omics-based cancer diagnosis using machine learning with potential application in gastrointestinal cancer. Particularly, we make a comprehensive summary and analysis from the perspective of multi-omics datasets, task types, and multi-omics-based integration methods. Furthermore, this paper points out the remaining challenges of multi-omics-based cancer diagnosis using machine learning and discusses future topics.

Cervicovaginal-Microbiome Analysis by 16S Sequencing and Real-Time PCR in Patients from Novosibirsk (Russia) with Cervical Lesions and Several Years after Cancer Treatment

Disturbed cervicovaginal-microbiome (CVM) structure promotes human papillomavirus (HPV) persistence and reflects risks of cervical lesions and cancer onset and recurrence. Therefore, microbiomic biomarkers may be useful for cervical disease screening and patient management. Here, by 16S rRNA gene sequencing and commercial PCR-based diagnostic kits, we profiled CVM in cytological preparations from 140 HPV-tested women (from Novosibirsk, Russia) with normal cytological findings, cervical lesions, or cancer and from 101 women who had recently received different cancer therapies. An increase in lesion severity was accompanied by higher HPV prevalence and elevated CVM biodiversity. Post-treatment CVM was found to be enriched with well-known microbial biomarkers of dysbiosis, just as in cervical disease. Nonetheless, concentrations of some skin-borne and environmental species (which gradually increased with increasing lesion severity)-especially Cutibacterium spp., Achromobacter spp., and Ralstonia pickettii-was low in post-treatment patients and depended on treatment types. Frequency of Lactobacillus iners dominance was high in all groups and depended on treatment types in post-treatment patients. Microbiome analysis via PCR-based kits revealed statistically significant differences among all groups of patients. Thus, microbiome profiling may help to find diagnostic and prognostic markers for management of cervical lesions; quantitative PCR-based kits may be suitable for these purposes.

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data

Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.

Alterations in the expression levels of long intergenic non-coding RNA APOC1P1-3 in cervical cancer tissue samples

lncRNAs play a crucial role in the carcinogenesis process. Thus, they have been recognized as the potential therapeutic and diagnostic biomarkers of cancers. This study assessed the alteration in the expression of APOC1P1-3 lncRNA in cancerous tissues compared to their adjacent non-tumorous tissues sampled from cervical cancer patients. one hundred fifteen pairs of cancerous and adjacent non-cancerous biopsy of cervical cancer specimens were collected. RNA isolation and cDNA synthesis were carried out. The qRT-PCR was used to assess the changes in the expression of APOC1P1-3 lncRNA. Moreover, the biomarker function of the lncRNA and the correlations between APOC1P1-3 and clinicopathological parameters were measured. The APOC1P1-3 expression was significantly increased in cervical cancer specimens as compared to adjacent non-tumorous specimens (p < 0.0001). A significant association was also observed between APOC1P1-3 expression and lymph node involvement (p = 0.031). Additionally, APOC1P1-3 expression demonstrated a significant association with the depth of tumor invasion (p = 0.035), and squamous type of cervical cancer (p = 0.019). The overexpression of APOC1P1-3 was significantly observed in patients younger than 50 years old as compared to another age group (p = 0.033). The results of ROC curve exhibited that APOC1P1-3 with area under the curve (AUC), specificity, and sensitivity of 0.96, 93.91%, and 78.26%, respectively can be considered as a potential biomarker. Regarding overexpression of APOC1P1-3 in human cervical cancer samples, this lncRNA may be considered as an oncogenic factor in cervical cancer patients. Besides, APOC1P1-3 may be a possible biomarker for the diagnosis and treatment of cervical cancer patients.

Bioinformatics Prediction and Machine Learning on Gene Expression Data Identifies Novel Gene Candidates in Gastric Cancer

Gastric cancer (GC) is one of the five most common cancers in the world and unfortunately has a high mortality rate. To date, the pathogenesis and disease genes of GC are unclear, so the need for new diagnostic and prognostic strategies for GC is undeniable. Despite particular findings in this regard, a holistic approach encompassing molecular data from different biological levels for GC has been lacking. To translate Big Data into system-level biomarkers, in this study, we integrated three different GC gene expression data with three different biological networks for the first time and captured biologically significant (i.e., reporter) transcripts, hub proteins, transcription factors, and receptor molecules of GC. We analyzed the revealed biomolecules with independent RNA-seq data for their diagnostic and prognostic capabilities. While this holistic approach uncovered biomolecules already associated with GC, it also revealed novel system biomarker candidates for GC. Classification performances of novel candidate biomarkers with machine learning approaches were investigated. With this study, AES, CEBPZ, GRK6, HPGDS, SKIL, and SP3 were identified for the first time as diagnostic and/or prognostic biomarker candidates for GC. Consequently, we have provided valuable data for further experimental and clinical efforts that may be useful for the diagnosis and/or prognosis of GC.

Learn from antibody–drug conjugates: consideration in the future construction of peptide-drug conjugates for cancer therapy

Cancer is one of the leading causes of death worldwide due to high heterogeneity. Although chemotherapy remains the mainstay of cancer therapy, non-selective toxicity and drug resistance of mono-chemotherapy incur broad criticisms. Subsequently, various combination strategies have been developed to improve clinical efficacy, also known as cocktail therapy. However, conventional “cocktail administration” is just passable, due to the potential toxicities to normal tissues and unsatisfactory synergistic effects, especially for the combined drugs with different pharmacokinetic properties. The drug conjugates through coupling the conventional chemotherapeutics to a carrier (such as antibody and peptide) provide an alternative strategy to improve therapeutic efficacy and simultaneously reduce the unspecific toxicities, by virtue of the advantages of highly specific targeting ability and potent killing effect. Although 14 antibody–drug conjugates (ADCs) have been approved worldwide and more are being investigated in clinical trials so far, several limitations have been disclosed during clinical application. Compared with ADCs, peptide-drug conjugates (PDCs) possess several advantages, including easy industrial synthesis, low cost, high tissue penetration and fast clearance. So far, only a handful of PDCs have been approved, highlighting tremendous development potential. Herein, we discuss the progress and pitfalls in the development of ADCs and underline what can learn from ADCs for the better construction of PDCs in the future.

Disturbing NLRP3 acetylation and inflammasome assembly inhibits androgen receptor-promoted inflammatory responses and prostate cancer progression

Chronic inflammation is one of the definite factors leading to the occurrence and development of tumors, including prostate cancer (PCa). The androgen receptor (AR) pathway is essential for PCa tumorigenesis and inflammatory response. However, little is known about the AR-regulated NACHT, LRR, and PYD domain-containing protein 3 (NLRP3) inflammasome pathway in human PCa. In this study, we explored the expression of inflammatory cytokine and AR in high-grade PCa and observed that NLRP3 inflammasome-associated genes were upregulated in high-grade PCa compared with that in low-grade PCa and benign prostatic hyperplasia and were associated with AR expression. In addition, we identified circAR-3-a circRNA derived from the AR gene-which is involved in the AR-regulated inflammatory response and cell proliferation by activating the NLRP3 inflammatory pathway. While circAR-3 overexpression promoted cell proliferation and the inflammatory response, its depletion induced opposite effects. Mechanistically, we noted that circAR-3 mediated the acetylation modification of NLRP3 by KAT2B and then promoted NLRP3 inflammasome complex subcellular distribution and assembly. Disturbing NLRP3 acetylation or blocking inflammasome assembly with an inhibitor suppressed the progression of PCa xenograft tumors. Our findings provide the first evidence that targeting NLRP3 acetylation or inflammasome assembly may be effective in inhibiting PCa progression.

Human oncogenic viruses: an overview of protein biomarkers in viral cancers and their potential use in clinics

INTRODUCTION

Although the idea that carcinogenesis might be caused by viruses was first voiced about 100 years ago, today's data disappointingly show that we have not made much progress in preventing and/or treating viral cancers in a century. According to recent studies, infections are responsible for approximately 13% of cancer development in the world. Today, it is accepted and proven by many authorities that Epstein-Barr virus (EBV), Hepatitis B virus (HBV), Hepatitis C virus (HCV), Human Herpesvirus 8 (HHV8), Human T-cell Lymphotropic virus 1 (HTLV1) and highly oncogenic Human Papillomaviruses (HPVs) cause or/and contribute to cancer development in humans.

AREAS COVERED

Considering the insufficient prevention and/or treatment strategies for viral cancers, in this review we present the current knowledge on protein biomarkers of oncogenic viruses. In addition, we aimed to decipher their potential for clinical use by evaluating whether the proposed biomarkers are expressed in body fluids, are druggable, and act as tumor suppressors or oncoproteins.

EXPERT OPINION

Consequently, we believe that this review will shed light on researchers and provide a guide to find remarkable solutions for the prevention and/or treatment of viral cancers.

Integration of artificial intelligence and precision oncology in Latin America

Next-generation medicine encompasses different concepts related to healthcare models and technological developments. In Latin America and the Caribbean, healthcare systems are quite different between countries, and cancer control is known to be insufficient and inefficient considering socioeconomically discrepancies. Despite advancements in knowledge about the biology of different oncological diseases, the disease remains a challenge in terms of diagnosis, treatment, and prognosis for clinicians and researchers. With the development of molecular biology, better diagnosis methods, and therapeutic tools in the last years, artificial intelligence (AI) has become important, because it could improve different clinical scenarios: predicting clinically relevant parameters, cancer diagnosis, cancer research, and accelerating the growth of personalized medicine. The incorporation of AI represents an important challenge in terms of diagnosis, treatment, and prognosis for clinicians and researchers in cancer care. Therefore, some studies about AI in Latin America and the Caribbean are being conducted with the aim to improve the performance of AI in those countries. This review introduces AI in cancer care in Latin America and the Caribbean, and the advantages and promising results that it has shown in this socio-demographic context.

Exploration of biomarkers for the diagnosis, treatment and prognosis of cervical cancer: a review

As the fourth most diagnosed cancer, cervical cancer (CC) is one of the major causes of cancer-related mortality affecting females globally, particularly when diagnosed at advanced stage. Discoveries of CC biomarkers pave the road to precision medicine for better patient outcomes. High throughput omics technologies, characterized by big data production further accelerate the process. To date, various CC biomarkers have been discovered through the advancement in technologies. Despite, very few have successfully translated into clinical practice due to the paucity of validation through large scale clinical studies. While vast amounts of data are generated by the omics technologies, challenges arise in identifying the clinically relevant data for translational research as analyses of single-level omics approaches rarely provide causal relations. Integrative multi-omics approaches across different levels of cellular function enable better comprehension of the fundamental biology of CC by highlighting the interrelationships of the involved biomolecules and their function, aiding in identification of novel integrated biomarker profile for precision medicine. Establishment of a worldwide Early Detection Research Network (EDRN) system helps accelerating the pace of biomarker translation. To fill the research gap, we review the recent research progress on CC biomarker development from the application of high throughput omics technologies with sections covering genomics, transcriptomics, proteomics, and metabolomics.

Recent developments and future directions in meta-analysis of differential gene expression in livestock RNA-Seq

Decreases in the costs of high-throughput sequencing technologies have led to continually increasing numbers of livestock RNA-Seq studies in the last decade. Although the number of studies has increased dramatically, most livestock RNA-Seq experiments are limited by cost to a small number of biological replicates. Meta-analysis procedures can be used to integrate and jointly analyze data from multiple independent studies. Meta-analyses increase the sample size, which in turn increase both statistical power and robustness of the results. In this work, we discuss cutting edge approaches to combining results from multiple independent RNA-Seq studies to improve livestock transcriptomics research. We review currently published RNA-Seq meta-analyses in livestock, describe many of the key issues specific to RNA-Seq meta-analysis in livestock species, and discuss future perspectives.

Metadata analysis to explore hub of the hub-genes highlighting their functions, pathways and regulators for cervical cancer diagnosis and therapies

Cervical cancer (CC) is considered as the fourth most common women cancer globally.that shows malignant features of local infiltration and invasion into adjacent organs and tissues. There are several individual studies in the literature that explored CC-causing hub-genes (HubGs), however, we observed that their results are not so consistent. Therefore, the main objective of this study was to explore hub of the HubGs (hHubGs) that might be more representative CC-causing HubGs compare to the single study based HubGs. We reviewed 52 published articles and found 255 HubGs/studied-genes in total. Among them, we selected 10 HubGs (CDK1, CDK2, CHEK1, MKI67, TOP2A, BRCA1, PLK1, CCNA2, CCNB1, TYMS) as the hHubGs by the protein-protein interaction (PPI) network analysis. Then, we validated their differential expression patterns between CC and control samples through the GPEA database. The enrichment analysis of HubGs revealed some crucial CC-causing biological processes (BPs), molecular functions (MFs) and cellular components (CCs) by involving hHubGs. The gene regulatory network (GRN) analysis identified four TFs proteins and three miRNAs as the key transcriptional and post-transcriptional regulators of hHubGs. Then, we identified hHubGs-guided top-ranked FDA-approved 10 candidate drugs and validated them against the state-of-the-arts independent receptors by molecular docking analysis. Finally, we investigated the binding stability of the top-ranked three candidate drugs (Docetaxel, Temsirolimus, Paclitaxel) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore the finding of this study might be the useful resources for CC diagnosis and therapies.

Acute Myeloid Leukemia: New Multiomics Molecular Signatures and Implications for Systems Medicine Diagnostics and Therapeutics Innovation

Acute myeloid leukemia (AML) is a common, complex, and multifactorial malignancy of the hematopoietic system. AML diagnosis and treatment outcomes display marked heterogeneity and patient-to-patient variations. To date, AML-related biomarker discovery research has employed single omics inquiries. Multiomics analyses that reconcile and integrate the data streams from multiple levels of the cellular hierarchy, from genes to proteins to metabolites, offer much promise for innovation in AML diagnostics and therapeutics. We report, in this study, a systems medicine and multiomics approach to integrate the AML transcriptome data and reporter biomolecules at the RNA, protein, and metabolite levels using genome-scale biological networks. We utilized two independent transcriptome datasets (GSE5122, GSE8970) in the Gene Expression Omnibus database. We identified new multiomics molecular signatures of relevance to AML: miRNAs (e.g., mir-484 and miR-519d-3p), receptors (ACVR1 and PTPRG), transcription factors (PRDM14 and GATA3), and metabolites (in particular, amino acid derivatives). The differential expression profiles of all reporter biomolecules were crossvalidated in independent RNA-Seq and miRNA-Seq datasets. Notably, we found that PTPRG holds important prognostication potential as evaluated by Kaplan-Meier survival analyses. The multiomics relationships unraveled in this analysis point toward the genomic pathogenesis of AML. These multiomics molecular leads warrant further research and development as potential diagnostic and therapeutic targets.

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

Multiomics Data Integration Identifies New Molecular Signatures for Abdominal Aortic Aneurysm and Aortic Occlusive Disease: Implications for Early Diagnosis, Prognosis, and Therapeutic Targets

Cardiovascular disease (CVD) is the leading cause of death among adults in developed countries. Among CVDs, abdominal aortic aneurysm (AAA) and aortic occlusive disease (AOD) are of great public health importance because of the high mortality rate in the elderly population. Despite significant molecular insights into AAA and AOD, the molecular mechanisms of these diseases remain unclear, and the current lack of robust diagnostic and prognostic biomarkers requires novel approaches to biomarker discovery and molecular targeting. In this study, we performed a comparative analysis of genome-wide expression data from patients with large AAA (n = 29), small AAA (n = 20), AOD (n = 9), and controls (n = 10). Specifically, we identified the differentially expressed genes and associated molecular pathways and biological processes (BPs) in each disease. Using a systems science approach, these data were linked to comprehensive human biological networks (i.e., protein-protein interaction, transcriptional regulatory, and metabolic networks) to identify molecular signatures of the salient mechanisms of AAA and AOD. Significant alterations in lipid metabolism and valine, leucine, and isoleucine metabolism, as well as neurodegenerative diseases and sex differences in the pathogenesis of AAA and AOD were identified. In the presence of aneurysm, size-dependent changes in lipid metabolism were observed. In addition, molecules and signaling pathways related to immunity, inflammation, infectious disease, and oxidative phosphorylation were identified in common. The results of the comparative and integrative analyzes revealed important clues to disease mechanisms and reporter molecules at various levels that warrant future development as potential prognostic biomarkers and putative therapeutic targets.

E2F4 may be a core transcription factor in the lncRNA-TF regulatory network in cervical cancer

Background

Cervical cancer is the most common gynecological cancer worldwide and is associated with high morbidity and mortality. Despite improvements in therapeutic strategies, the network regulation mechanism remains unclear and the treatment effect is not satisfactory. Therefore, there is a need to continue studying the mechanism of cervical cancer to explore effective gene targets and precise targeted therapy drugs.

Methods

First, three paired tissues (cancer tissues and noncancerous tissues) from patients with cervical squamous cell carcinoma were collected, grouped, and analyzed by microarray. Second, differentially expressed mRNAs (DEMs) and differentially expressed lncRNAs (DELs) (|fold change| ≥ 2 and p < 0.05) between the two groups were screened. For DEMs, functional annotation and pathway analysis were performed using DAVID. Functional prediction of DELs was then performed and their cis‐regulatory and trans‐regulatory networks were explored.

Results

Function prediction of DELs (both up‐regulated and down‐regulated) shows that the highest frequency Cellular Component (CC) item is cytosol, the highest frequency Molecular function (MF) item is mitotic cell cycle and the highest frequency Biological Process (BP) item is protein binding. Through cis‐regulation analysis of DELs, the cis‐regulatory relationship of 96 DELs was predicted. The lncRNA‐trans‐regulation network analysis suggested that E2F4 may be the core transcription factor in the lncRNA‐TF regulatory network in cervical cancer.

Conclusions

The lncRNA‐TF regulatory network plays an important role in the occurrence and progression of cervical cancer, and E2F4 may be a critical transcription factor in the regulatory network.

Systems biomarkers for papillary thyroid cancer prognosis and treatment through multi-omics networks

The identification of biomolecules associated with papillary thyroid cancer (PTC) has upmost importance for the elucidation of the disease mechanism and the development of effective diagnostic and treatment strategies. Despite particular findings in this regard, a holistic analysis encompassing molecular data from different biological levels has been lacking. In the present study, a meta-analysis of four transcriptome datasets was performed to identify gene expression signatures in PTC, and reporter molecules were determined by mapping gene expression data onto three major cellular networks, i.e., transcriptional regulatory, protein-protein interaction, and metabolic networks. We identified 282 common genes that were differentially expressed in all PTC datasets. In addition, six proteins (FYN, JUN, LYN, PML, SIN3A, and RARA), two Erb-B2 receptors (ERBB2 and ERBB4), two cyclin-dependent receptors (CDK1 and CDK2), and three histone deacetylase receptors (HDAC1, HDAC2, and HDAC3) came into prominence as proteomic signatures in addition to several metabolites including lactaldehyde and proline at the metabolome level. Significant associations with calcium and MAPK signaling pathways and transcriptional and post-transcriptional activities of 12 TFs and 110 miRNAs were also observed at the regulatory level. Among them, six miRNAs (miR-30b-3p, miR-15b-5p, let-7a-5p, miR-130b-3p, miR-424-5p, and miR-193b-3p) were associated with PTC for the first time in the literature, and the expression levels of miR-30b-3p, miR-15b-5p, and let-7a-5p were found to be predictive of disease prognosis. Drug repositioning and molecular docking simulations revealed that 5 drugs (prochlorperazine, meclizine, rottlerin, cephaeline, and tretinoin) may be useful in the treatment of PTC. Consequently, we report here biomolecule candidates that may be considered as prognostic biomarkers or potential therapeutic targets for further experimental and clinical trials for PTC.

System and network biology-based computational approaches for drug repositioning

Recent advances in computational biology have not only fastened the drug discovery process but have also proven to be a powerful tool for the search of existing molecules of therapeutic value for drug repurposing. The system biology-based drug repurposing approaches shorten the time and reduced the cost of the whole process when compared to de novo drug discovery. In the present pandemic situation, these computational approaches have emerged as a boon to tackle the COVID-19 associated morbidities and mortalities. In this chapter, we present the overview of system biology-based network system approaches which can be exploited for the drug repurposing of disease. Besides, we have included information on relevant repurposed drugs which are currently used for the treatment of COVID-19.

Long non-coding RNA TP73-AS1 predicts poor prognosis and regulates cell proliferation and migration in cervical cancer

INTRODUCTION

Cervical cancer is one of the most common malignant tumors in women, which seriously affects women's health, especially in developing countries. This study aims to investigate novel molecular markers for poor prognosis of cervical cancer to achieve correct guidance of clinical treatment, accurate assessment of prognosis, and provide a new basis for the choice of reasonable treatment options for cervical cancer patients.

MATERIAL AND METHODS

QRT-PCR was employed to investigate the expression of lncRNA TP73-AS1 in cervical cancer tissues and cell lines. COX multivariate analysis showed the relationship between TP73-AS1 expression and clinicopathological features of patients with cervical cancer. Colony formation and MTT assay detected the effect of TP73-AS1 on proliferation of cervical cancer cells. The effect of TP73-AS1 on migration and invasion of cervical cancer cells was determined by the wound-healing assay and transwell assay. Western blot was performed to assess the expression of EMT markers.

RESULTS

This study showed that lncRNA TP73-AS1 was up-regulated in cervical cancer tissues and cell lines (p < 0.001), and high expression of TP73-AS1 could be considered as an independent prognostic factor (p < 0.05). Moreover, lncRNA TP73-AS1 promotes cervical cancer cell migration and invasion, and knockdown of TP73-AS1 inhibits the growth of cervical cancer cells (p < 0.001).

CONCLUSIONS

Our results indicated that lncRNA TP73-AS1 was up-regulated in cervical cancer tissues and cell lines, predicting poor prognosis of cervical cancer and regulating cell proliferation and migration.

MicroRNA-Mediated Drug Repurposing Unveiled Potential Candidate Drugs for Prolactinoma Treatment

INTRODUCTION

Prolactinomas, also called lactotroph adenomas, are the most encountered type of hormone-secreting pituitary neuroendocrine tumors in the clinic. The preferred first-line therapy is a medical treatment with dopamine agonists (DAs), mainly cabergoline, to reduce serum prolactin levels, tumor volume, and mass effect. However, in some cases, patients have displayed DA resistance with aggressive tumor behavior or are faced with recurrence after drug withdrawal. Also, currently used therapeutics have notorious side effects and impair the life quality of the patients.

METHODS

Since the amalgamation of clinical and laboratory data besides tumor histopathogenesis and transcriptional regulatory features of the tumor emerges to exhibit essential roles in the behavior and progression of prolactinomas; in this work, we integrated mRNA- and microRNA (miRNA)-level transcriptome data that exploit disease-specific signatures in addition to biological and pharmacological data to elucidate a rational prioritization of pathways and drugs in prolactinoma.

RESULTS

We identified 8 drug candidates through drug repurposing based on mRNA-miRNA-level data integration and evaluated their potential through in vitro assays in the MMQ cell line. Seven repurposed drugs including 5-fluorocytosine, nortriptyline, neratinib, puromycin, taxifolin, vorinostat, and zileuton were proposed as potential drug candidates for the treatment of prolactinoma. We further hypothesized possible mechanisms of drug action on MMQ cell viability through analyzing the PI3K/Akt signaling pathway and cell cycle arrest via flow cytometry and Western blotting.

DISCUSSION

We presented the transcriptomic landscape of prolactinoma through miRNA and mRNA-level data integration and proposed repurposed drug candidates based on this integration. We validated our findings through testing cell viability, cell cycle phases, and PI3K/Akt protein expressions. Effects of the drugs on cell cycle phases and inhibition of the PI3K/Akt pathway by all drugs gave us promising output for further studies using these drugs in the treatment of prolactinoma. This is the first study that reports miRNA-mediated repurposed drugs for prolactinoma treatment via in vitro experiments.

Biliary Strictures and Cholangiocarcinoma - Untangling a Diagnostic Conundrum

Cholangiocarcinoma is an uncommon and highly aggressive biliary tract malignancy with few manifestations until late disease stages. Diagnosis is currently achieved through a combination of clinical, biochemical, radiological and histological techniques. A number of reported cancer biomarkers have the potential to be incorporated into diagnostic pathways, but all lack sufficient sensitivity and specificity limiting their possible use in screening and early diagnosis. The limitations of standard serum markers such as CA19-9, CA125 and CEA have driven researchers to identify multiple novel biomarkers, yet their clinical translation has been slow with a general requirement for further validation in larger patient cohorts. We review recent advances in the diagnostic pathway for suspected CCA as well as emerging diagnostic biomarkers for early detection, with a particular focus on non-invasive approaches.

Small Extracellular Vesicles: Functions and Potential Clinical Applications as Cancer Biomarkers

Cancer, as the second leading cause of death worldwide, is a major public health concern that imposes a heavy social and economic burden. Effective approaches for either diagnosis or therapy of most cancers are still lacking. Dynamic monitoring and personalized therapy are the main directions for cancer research. Cancer-derived extracellular vesicles (EVs) are potential disease biomarkers. Cancer EVs, including small EVs (sEVs), contain unique biomolecules (protein, nucleic acid, and lipids) at various stages of carcinogenesis. In this review, we discuss the biogenesis of sEVs, and their functions in cancer, revealing the potential applications of sEVs as cancer biomarkers.

Determination of a six-gene prognostic model for cervical cancer based on WGCNA combined with LASSO and Cox-PH analysis

AIM

This study aimed to establish a risk model of hub genes to evaluate the prognosis of patients with cervical cancer.

METHODS

Based on TCGA and GTEx databases, the differentially expressed genes (DEGs) were screened and then analyzed using GO and KEGG analyses. The weighted gene co-expression network (WGCNA) was then used to perform modular analysis of DEGs. Univariate Cox regression analysis combined with LASSO and Cox-pH was used to select the prognostic genes. Then, multivariate Cox regression analysis was used to screen the hub genes. The risk model was established based on hub genes and evaluated by risk curve, survival state, Kaplan-Meier curve, and receiver operating characteristic (ROC) curve.

RESULTS

We screened 1265 DEGs between cervical cancer and normal samples, of which 620 were downregulated and 645 were upregulated. GO and KEGG analyses revealed that most of the upregulated genes were related to the metastasis of cancer cells, while the downregulated genes mostly acted on the cell cycle. Then, WGCNA mined six modules (red, blue, green, brown, yellow, and gray), and the brown module with the most DEGs and related to multiple cancers was selected for the follow-up study. Eight genes were identified by univariate Cox regression analysis combined with the LASSO Cox-pH model. Then, six hub genes (SLC25A5, ENO1, ANLN, RIBC2, PTTG1, and MCM5) were screened by multivariate Cox regression analysis, and SLC25A5, ANLN, RIBC2, and PTTG1 could be used as independent prognostic factors. Finally, we determined that the risk model established by the six hub genes was effective and stable.

CONCLUSIONS

This study supplies the prognostic value of the risk model and the new promising targets for the cervical cancer treatment, and their biological functions need to be further explored.

Differential Interactome Based Drug Repositioning Unraveled Abacavir, Exemestane, Nortriptyline Hydrochloride, and Tolcapone as Potential Therapeutics for Colorectal Cancers

There is a critical requirement for alternative strategies to provide the better treatment in colorectal cancer (CRC). Hence, our goal was to propose novel biomarkers as well as drug candidates for its treatment through differential interactome based drug repositioning. Differentially interacting proteins and their modules were identified, and their prognostic power were estimated through survival analyses. Drug repositioning was carried out for significant target proteins, and candidate drugs were analyzed via in silico molecular docking prior to in vitro cell viability assays in CRC cell lines. Six modules (mAPEX1, mCCT7, mHSD17B10, mMYC, mPSMB5, mRAN) were highlighted considering their prognostic performance. Drug repositioning resulted in eight drugs (abacavir, ribociclib, exemestane, voriconazole, nortriptyline hydrochloride, theophylline, bromocriptine mesylate, and tolcapone). Moreover, significant in vitro inhibition profiles were obtained in abacavir, nortriptyline hydrochloride, exemestane, tolcapone, and theophylline (positive control). Our findings may provide new and complementary strategies for the treatment of CRC.

Knockdown of LINC01123 inhibits cell viability, migration and invasion via miR-361-3p/TSPAN1 targeting in cervical cancer

Cervical cancer (CC) is a type of gynecological malignancy that poses a significant threat to females. The aim of the present study was to examine the role of long intergenic non-protein coding RNA 1123 (LINC01123) and its underlying molecular mechanism in the development of CC. mRNA expression levels of LINC01123 and microRNA (miR)-361-3p in CC tissue samples and cell lines were evaluated using reverse transcription-quantitative PCR. Cell viability, migration and invasion were detected using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, wound healing and Transwell assays. Moreover, a xenograft tumor model was established for elucidating the influence of LINC01123 knockdown on tumor growth in vivo. A dual-luciferase reporter assay was used to confirm the association between LINC01123 and miR-361-3p, and miR-361-3p and tetraspanin 1 (TSPAN1). Western blot analysis was used to determine TSPAN1 protein expression. LINC01123 expression was upregulated and miR-361-3p expression was reduced in CC tissue samples and cell lines. Knockdown of LINC01123 inhibited cell viability, migration and invasion in vitro, and suppressed tumor growth in vivo. Furthermore, LINC01123 targeted miR-361-3p and negatively regulated miR-361-3p expression. Overexpression of miR-361-3p inhibited cell viability, migration and invasion in HeLa and CaSki cells. Additionally, miR-361-3p targeted TSPAN1 and negatively regulated TSPAN1 expression. Inhibition of miR-361-3p and overexpression of TSPAN1 reversed the effect of LINC01123 knockdown on cell proliferation, migration and invasion in HeLa cells. Knockdown of LINC01123 inhibited cell proliferation, migration and invasion via miR-361-3p/TSPAN1 regulation in CC, which may present an effective target for treatment of CC.

Kallikrein 5 overexpression is associated with poor prognosis in uterine cervical cancer

Objective

Kallikrein 5 (KLK5), which is frequently observed in normal cervico-vaginal fluid, is known to be related to prognosis in several solid tumors. We investigated the prognostic significance of KLK5 in uterine cervical cancer using tumor tissue microarray and immunohistochemistry staining.

Methods

We analyzed samples of 165 patients with uterine cervical cancer who received definitive radiation therapy between 2004 and 2012. We divided patients into two groups stratified by their KLK5 activity by immunohistochemistry staining: negative/weak (0–1+) (n=120 patients) and moderate/strong (2–3+) group (n=45 patients). Patient and tumor characteristics, patterns of failure, and survival outcomes were compared. Univariable and multivariable analyses were performed to identify prognostic factors.

Results

Patients with KLK5 2–3+ were younger (median: 52 vs. 60 years) and had frequent paraaortic lymph node involvement (40.0% vs. 18.3%) than those with KLK5 0–1+. With a median follow-up of 60.8 (interquartile range, 47.5–77.9) months, patients with KLK5 2–3+ had inferior 5-year locoregional recurrence-free survival and distant metastasis-free survival of 61.7% (vs. 77.5% in KLK5 0–1+ group) and 59.4% (vs. 72.8% in the KLK5 0–1+ group), respectively (all p<0.05). KLK5 2–3+ expression retained its significance after adjusting for other well-known prognostic factors of tumor size and stage in multivariable analysis.

Conclusions

KLK5 overexpression is associated with the aggressiveness of cervical cancer and may underlie the diminished response to conventional treatments. Therefore, KLK5 could be a reliable prognostic factor in cervical cancer.

Preprocessing of Public RNA-Sequencing Datasets to Facilitate Downstream Analyses of Human Diseases

Publicly available RNA-sequencing (RNA-seq) data are a rich resource for elucidating the mechanisms of human disease; however, preprocessing these data requires considerable bioinformatic expertise and computational infrastructure. Analyzing multiple datasets with a consistent computational workflow increases the accuracy of downstream meta-analyses. This collection of datasets represents the human intracellular transcriptional response to disorders and diseases such as acute lymphoblastic leukemia (ALL), B-cell lymphomas, chronic obstructive pulmonary disease (COPD), colorectal cancer, lupus erythematosus; as well as infection with pathogens including Borrelia burgdorferi, hantavirus, influenza A virus, Middle East respiratory syndrome coronavirus (MERS-CoV), Streptococcus pneumoniae, respiratory syncytial virus (RSV), severe acute respiratory syndrome coronavirus (SARS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We calculated the statistically significant differentially expressed genes and Gene Ontology terms for all datasets. In addition, a subset of the datasets also includes results from splice variant analyses, intracellular signaling pathway enrichments as well as read mapping and quantification. All analyses were performed using well-established algorithms and are provided to facilitate future data mining activities, wet lab studies, and to accelerate collaboration and discovery.

The effect of psychological nursing on the short- and long-term negative emotions and quality of life of cervical cancer patients undergoing postoperative chemotherapy

OBJECTIVE

The purpose of this study was to analyze the effect of psychological nursing intervention on the short- and long-term negative emotions and changes in the quality of life in patients with cervical cancer who underwent postoperative chemotherapy.

METHODS

141 patients with cervical cancer who received postoperative chemotherapy in our hospital were recruited as the study cohort. They were divided into the study group (80 cases) and the control group (61 cases) according to the different nursing methods each underwent. The patients in the control group underwent routine nursing, and the study group also underwent psychological nursing. The changes in the quality of life and the negative emotions of the patients in the two groups before and after the intervention were compared, and the correlation between the quality of life and the negative emotions were explored.

RESULTS

The patients' Quality of Life Questionnaire (EROTC-QLQ-C30) and Self-rating Anxiety Scale (SAS) scores in the two groups before the intervention were not significantly different (P > 0.05). A re-evaluation at the end of the 90 day-intervention showed that the EROTC-QLQ-C30 scores in the study group were significantly higher than they were in the control group (P < 0.05). A dynamic evaluation showed that the proportion of patients with mild anxiety in the study group was higher than it was in the control group at 30, 60, and 90 days of intervention (P < 0.05). A Spearman correlation analysis showed that the SAS scale and EROTC-QLQ-C30 scores were negatively correlated (r=-0.4438, P < 0.05).

CONCLUSION

The implementation of psychological intervention can help alleviate the short- and long-term negative emotions of cervical cancer patients who underwent postoperative chemotherapy, and it is feasible and conducive to the patients' quality of life. We recommend carrying out the clinical promotion and application of this psychological intervention.

Methylation silencing CDH23 is a poor prognostic marker in diffuse large B-cell lymphoma

Cadherin-23(CDH23) mediates homotypic and heterotypic cell-cell adhesions in cancer cells. However, the epigenetic regulation, the biological functions, the mechanisms and the prognostic value of CDH23 in diffuse large B-cell lymphoma (DLBCL) are still unclear. The Gene Expression Profiling Interactive Analysis (GEPIA) and the Gene Expression Omnibus (GEO) database were employed to analyze the CDH23 expression level in DLBCL. The correlation of CDH23 expression and methylation was analyzed by LinkedOmics database. The prognostic value was analyzed via GEPIA. Correlated genes, target kinase, target miRNA, target transcription factor and biological functions were identified by LinkedOmics and GeneMANIA database. The relationship between CDH23 and the immune cell infiltration was explored by the Tumor Immune Estimation Resource (TIMER). The expression of CDH23 was reduced by DNA methylation significantly in DLBCL tissue. Reduction of CDH23 represented poor outcome of DLBCL patients. Functional enrichment analysis showed that CDH23 mainly enriched in cancer cell growth, cell metastasis, cell adhesion, cell cycle, drug catabolic process, leukocyte mediated immunity and DNA repair by some cancer related kinases, miRNAs and transcription factors. These results indicated that methylated reduction of CDH23 represented poor outcome of DLBCL. CDH23 is associated with essential biological functions and key molecules in DLBCL. CDH23 may play crucial roles in DLBCL tumorigenesis. Our results lay a foundation for further investigation of the role of CDH23 in DLBCL tumorigenesis.

Tumor suppressor miR-192-5p targets TRPM7 and inhibits proliferation and invasion in cervical cancer

Cervical cancer is the fourth highest mortality cancer among women worldwide. Many researchers have discovered the major anticancer role of miR-192-5p. However, no study has revealed the effect of miR-192-5p on cervical cancer and its molecular mechanism. Therefore, in this study, we aimed to explore the role of miR-192-5p in proliferation, invasion of cervical cancer, and its regulatory mechanism. Firstly, the expression level of miR-192-5p was examined by real-time quantitative polymerase chain reaction. Cell counting kit-8 analysis was applied to detect the proliferation of transfected Caski and SiHa cells. Flow cytometry assay was applied to detect the apoptosis of transfected Caski and SiHa cells. Our result showed that miR-192-5p restrained cervical cancer cell proliferation and induced apoptosis. Then we employed wound healing and transwell assays to analyze the migration and invasion abilities of Caski and SiHa cells in vitro. The results showed that miR-192-5p had an inhibitory effect on cervical cancer migration and invasion. The results of in vivo experiment demonstrated that miR-192-5p also inhibited tumor development in nude mice. We further detected that the binding of transient receptor potential melastatin-subfamily member 7 (TRPM7) to miR-192-5p using bioinformatic methods and dual-luciferase reporter assay. Finally, we found that TRPM7 overexpression reversed the inhibitory effects of miR-192-5p on proliferation, migration, and invasion on cervical cancer cells. In conclusion, the findings of the present study revealed that miR-192-5p performs an inhibitory role in cervical cancer proliferation and invasion by targeting TRPM7.

Carbon Based Nanodots in Early Diagnosis of Cancer

Detection of cancer at an early stage is one of the principal factors associated with successful treatment outcome. However, current diagnostic methods are not capable of making sensitive and robust cancer diagnosis. Nanotechnology based products exhibit unique physical, optical and electrical properties that can be useful in diagnosis. These nanotech-enabled diagnostic representatives have proved to be generally more capable and consistent; as they selectively accumulated in the tumor site due to their miniscule size. This article rotates around the conventional imaging techniques, the use of carbon based nanodots viz Carbon Quantum Dots (CQDs), Graphene Quantum Dots (GQDs), Nanodiamonds, Fullerene, and Carbon Nanotubes that have been synthesized in recent years, along with the discovery of a wide range of biomarkers to identify cancer at early stage. Early detection of cancer using nanoconstructs is anticipated to be a distinct reality in the coming years.

The Clinical Significance and Biological Function of PCDH7 in Cervical Cancer

Purpose

Cervical cancer is a common cancerous tumor in women that is prone to recurrence and metastasis. Recently, many people have explored the role of protocadherin 7 (PCDH7) in cancer and found that PCDH7 is abnormally expressed in many cancers. The purpose of this study is to explore the expression and mechanism of PCDH7 in cervical cancer and evaluate its clinical prognostic significance.

Materials and Methods

The expression of PCDH7 in cervical cancer and cells was measured by qRT-PCR. The relationship between PCDH7 expression and the clinical prognosis was calculated using the Kaplan–Meier method and Cox regression analyses. Effects of PCDH7 on cancer cell proliferation, migration, and invasion were studied by MTT assay and transwell assays.

Results

The expression of PCDH7 in cervical cancer tissues and cell lines was notably downregulated compared with the corresponding control. Low PCDH7 expression was associated with a low survival rate. PCDH7 expression was correlated with lymph node metastasis, cell differentiation, and FIGO staging. PCDH7 can be used as an independent prognostic factor for cervical cancer. Up-regulation of PCDH7 significantly inhibited the proliferation ability, migration potential, and invasion capacity of cancer cells.

Conclusion

PCDH7 may be used as a prognostic biomarker for cervical cancer patients.

Cervical Cancer Diagnosis: Insights into Biochemical Biomarkers and Imaging Techniques

Cervical malignancy is known as one of the important cancers which is originated from cervix. This malignancy has been observed in women infected with papillomavirus who had regular oral contraceptives, multiple pregnancies, and sexual relations. Early and fast cervical cancer diagnosis is known as two important aspects of cervical cancer therapy. Several investigations indicated that early and fast detection of cervical cancer could be associated with better treatment process and increasing survival rate of patients with this malignancy. Imaging techniques are very important diagnosis tools that could be employed for diagnosis and following responses to therapy in various cervical cancer stages. Multiple lines of evidence indicated that utilization of imaging techniques is related to some limitations (i.e. high cost, and invasive effects). Hence, it seems that along with using imaging techniques, finding and developing new biomarkers could be useful in the diagnosis and treatment of subjects with cervical cancer. Taken together, many studies showed that a variety of biomarkers including, several proteins, mRNAs, microRNAs, exosomes and polymorphisms might be introduced as prognostic, diagnostic and therapeutic biomarkers in cervical cancer therapy. In this review article, we highlighted imaging techniques as well as novel biomarkers for the diagnosis of cervical cancer.

Expression Analysis of MIST1 and EMT Markers in Primary Tumor Samples Points to MIST1 as a Biomarker of Cervical Cancer

Background

Mist1 is a basic transcription factor, which plays an important role in the development of multiple organs, and may also regulate tumor progression by mediating epithelial-mesenchymal transformation. However, there is lack of research on its role of squamous cell carcinoma, especially in cervical squamous cell carcinoma.

Methods

Bioinformatic methods were used to analyze gene expression, correlation, and patient survival according to the TCGA database. Thirty pairs of cancer tissues and distal cancer tissues from cervical cancer patients who received radical surgery were enrolled in the study. The expression of Mist1 was analyzed using Western blot. Furthermore, the potential associations among Mist1 expression, EMT biomarkers and various clinicopathological characteristics were investigated. All statistical tests employed in this study were two-sided, and P values <0.05 were deemed statistically significant.

Results

Overall survival data were obtained from TCGA-CESC dataset, containing 3 control samples and 305 tumor samples. The expression of Mist1 was significantly higher in primary tumor than in normal tissues (P<0.001). The samples were divided into a low Mist1 expression group (n=144) and a high Mist1 expression group (n=146) according to the median expression level. Kaplan–Meier survival analysis revealed that high expression of Mist1 was significantly correlated with poor overall survival (P=0.032). We further explored the relationships between Mist1 and EMT. Among the 30 primary cervical cancer specimens investigated, the difference in Mist1 expressed statuses between cervical cancer tissues and distal noncancerous cervical tissues was significant (P=0.001). And the epithelial cell marker E-cadherin was downregulated in Mist1 overexpressed cervical cancer cells; however, the mesenchymal marker N-Cadherin and Twist was upregulated.

Conclusion

Our study found that Mist1 seemed to play the role of oncogene in cervical squamous cell carcinoma and could be a potential biomarker.