Senescence-Associated Secretory Phenotype Determines Survival and Therapeutic Response in Cervical Cancer

Targeting treatment resistance in cervical cancer: A new avenue for senolytic therapies

Cervical cancer poses a significant global health challenge, particularly impacting women in economically developing nations. This disparity stems from a combination of factors, including inadequate screening infrastructure and resource limitations. However, the foremost contributor is the widespread lack of awareness and limited accessibility to Human Papillomavirus (HPV) vaccination, which is a key preventative measure against cervical cancer development. Despite advancements in cervical cancer prevention, treatment resistance remains a major hurdle in achieving improved patient outcomes. Cellular senescence, specifically the senescence-associated secretory phenotype (SASP) and its bidirectional relationship with the immune system, has been implicated in resistance to conventional cervical cancer chemotherapy treatments. The exact mechanisms by which this state of growth arrest and the associated changes in immune regulation contribute to cervical cancer progression and the associated drug resistance are not entirely understood. This underscores the necessity for innovative strategies to address the prevalence of treatment-resistant cervical cancer, with one promising avenue being the utilisation of senolytics. Senolytics are agents that have promising efficacy in clearing senescent cells from tumour tissues, however neither the utilisation of senolytics for addressing senescence-induced treatment resistance nor the potential integration of immunotherapy as senolytic agents in cervical cancer treatment has been explored to date. This review provides a concise overview of the mechanisms underlying senescence induction and the pivotal role of the immune system in this process. Additionally, it explores various senolytic approaches that hold significant potential for advancing cervical cancer research.

Prognostic Significance of SASP-Related Gene Signature of Radiation Therapy in Head and Neck Squamous Cell Carcinoma

In this study, we developed and validated the clinical significance of senescence-associated secretory phenotype (SASP)-related gene signature and explored its association with radiation therapy (RT) in patients with head and neck squamous cell carcinoma (HNSCC). First, we searched the three published review literature associated with SASP and selected all 81 genes to develop SASP-related gene signature. Then, 81 SASP-related genes were adapted to gene expression dataset from The Cancer Genome Atlas (TCGA). Patients with HNSCC of TCGA were classified into clusters 1 and 2 via unsupervised clustering according to SASP-related gene signature. Kaplan-Meier plot survival analysis showed that cluster 1 had a poorer prognosis than cluster 2 in 5-year overall survival and recurrence-free survival. Similarly, cluster 1 showed a worse prognosis than cluster 2 in three validation cohorts (E-MTAB-8588, FHCRC, and KHU). Cox proportional hazards regression observed that the SASP-related signature was an independent prognostic factor for patients with HNSCC. We also established a nomogram using a relevant clinical parameter and a risk score. Time-dependent receiver operating characteristic analysis was carried out to assess the accuracy of the prognostic risk model and nomogram. Senescence SASP-related gene signature was associated with the response to RT. Therefore, subsequent, in vitro experiments further validated the association between SASP-related gene signature and RT in HNSCC. In conclusion, we developed a SASP-related gene signature, which could predict survival of patients with HNSCC, and this gene signature provides new clinical evidence for the accurate diagnosis and targeted RT of HNSCC.

Serum Proteomic Signatures in Cervical Cancer: Current Status and Future Directions

In 2020, the World Health Organization (WHO) reported 604,000 new diagnoses of cervical cancer (CC) worldwide, and over 300,000 CC-related fatalities. The vast majority of CC cases are caused by persistent human papillomavirus (HPV) infections. HPV-related CC incidence and mortality rates have declined worldwide because of increased HPV vaccination and CC screening with the Papanicolaou test (PAP test). Despite these significant improvements, developing countries face difficulty implementing these programs, while developed nations are challenged with identifying HPV-independent cases. Molecular and proteomic information obtained from blood or tumor samples have a strong potential to provide information on malignancy progression and response to therapy in CC. There is a large amount of published biomarker data related to CC available but the extensive validation required by the FDA approval for clinical use is lacking. The ability of researchers to use the big data obtained from clinical studies and to draw meaningful relationships from these data are two obstacles that must be overcome for implementation into clinical practice. We report on identified multimarker panels of serum proteomic studies in CC for the past 5 years, the potential for modern computational biology efforts, and the utilization of nationwide biobanks to bridge the gap between multivariate protein signature development and the prediction of clinically relevant CC patient outcomes.

The role of NF-κB in carcinogenesis of cervical cancer: opportunities and challenges

The nuclear factor-κB (NF-κB) family, consisting of several transcription factors, has been implicated in the regulation of cell proliferation and invasion, as well as inflammatory reactions and tumor development. Cervical cancer (CC) results from long-term interactions of multiple factors, among which persistent high-risk human papillomavirus (hrHPV) infection is necessary. During different stages from early to late after HPV infection, the activity of NF-κB varies and plays various roles in carcinogenesis and progress of CC. As the center of the cell signaling transduction network, NF-κB can be activated through classical and non-classical pathways, and regulate the expression of downstream target genes involved in regulating the tumor microenvironment and acquiring hallmark traits of CC cells. Targeting NF-κB may help treat CC and overcome the resistance to radiation and chemotherapy. Even though NF-κB inhibitors have not been applied in clinical treatment as yet, due to limitations such as dose-restrictive toxicity and poor tumor-specificity, it is still considered to have significant therapeutic potential and application prospects. In this review, we focus on the role of NF-κB in the process of CC occurrence and hallmark capabilities acquisition. Finally, we summarize relevant NF-κB-targeted treatments, providing ideas for the prevention and treatment of CC.

Yearning for machine learning: applications for the classification and characterisation of senescence

Senescence is a widely appreciated tumour suppressive mechanism, which acts as a barrier to cancer development by arresting cell cycle progression in response to harmful stimuli. However, senescent cell accumulation becomes deleterious in aging and contributes to a wide range of age-related pathologies. Furthermore, senescence has beneficial roles and is associated with a growing list of normal physiological processes including wound healing and embryonic development. Therefore, the biological role of senescent cells has become increasingly nuanced and complex. The emergence of sophisticated, next-generation profiling technologies, such as single-cell RNA sequencing, has accelerated our understanding of the heterogeneity of senescence, with distinct final cell states emerging within models as well as between cell types and tissues. In order to explore data sets of increasing size and complexity, the senescence field has begun to employ machine learning (ML) methodologies to probe these intricacies. Most notably, ML has been used to aid the classification of cells as senescent, as well as to characterise the final senescence phenotypes. Here, we provide a background to the principles of ML tasks, as well as some of the most commonly used methodologies from both traditional and deep ML. We focus on the application of these within the context of senescence research, by addressing the utility of ML for the analysis of data from different laboratory technologies (microscopy, transcriptomics, proteomics, methylomics), as well as the potential within senolytic drug discovery. Together, we aim to highlight both the progress and potential for the application of ML within senescence research.

Aging characteristics of colorectal cancer based on gut microbiota

BACKGROUND

Aging is one of the factors leading to cancer. Gut microbiota is related to aging and colorectal cancer (CRC).

METHODS

A total of 11 metagenomic data sets related to CRC were collected from the R package curated Metagenomic Data. After batch effect correction, healthy individuals and CRC samples were divided into three age groups. Ggplot2 and Microbiota Process packages were used for visual description of species composition and PCA in healthy individuals and CRC samples. LEfSe analysis was performed for species relative abundance data in healthy/CRC groups according to age. Spearman correlation coefficient of age-differentiated bacteria in healthy individuals and CRC samples was calculated separately. Finally, the age prediction model and CRC risk prediction model were constructed based on the age-differentiated bacteria.

RESULTS

The structure and composition of the gut microbiota were significantly different among the three groups. For example, the abundance of Bacteroides vulgatus in the old group was lower than that in the other two groups, the abundance of Bacteroides fragilis increased with aging. In addition, seven species of bacteria whose abundance increases with aging were screened out. Furthermore, the abundance of pathogenic bacteria (Escherichia_coli, Butyricimonas_virosa, Ruminococcus_bicirculans, Bacteroides_fragilis and Streptococcus_vestibularis) increased with aging in CRCs. The abundance of probiotics (Eubacterium_eligens) decreased with aging in CRCs. The age prediction model for healthy individuals based on the 80 age-related differential bacteria and model of CRC patients based on the 58 age-related differential bacteria performed well, with AUC of 0.79 and 0.71, respectively. The AUC of CRC risk prediction model based on 45 disease differential bacteria was 0.83. After removing the intersection between the disease-differentiated bacteria and the age-differentiated bacteria from the healthy samples, the AUC of CRC risk prediction model based on remaining 31 bacteria was 0.8. CRC risk prediction models for each of the three age groups showed no significant difference in accuracy (young: AUC=0.82, middle: AUC=0.83, old: AUC=0.85).

CONCLUSION

Age as a factor affecting microbial composition should be considered in the application of gut microbiota to predict the risk of CRC.

Gynecological cancer prognosis using machine learning techniques: A systematic review of last three decades (1990–2022)

OBJECTIVE

Many Computer Aided Prognostic (CAP) systems based on machine learning techniques have been proposed in the field of oncology. The objective of this systematic review was to assess and critically appraise the methodologies and approaches used in predicting the prognosis of gynecological cancers using CAPs.

METHODS

Electronic databases were used to systematically search for studies utilizing machine learning methods in gynecological cancers. Study risk of bias (ROB) and applicability were assessed using the PROBAST tool. 139 studies met the inclusion criteria, of which 71 predicted outcomes for ovarian cancer patients, 41 predicted outcomes for cervical cancer patients, 28 predicted outcomes for uterine cancer patients, and 2 predicted outcomes for gynecological malignancies broadly.

RESULTS

Random forest (22.30 %) and support vector machine (21.58 %) classifiers were used most commonly. Use of clinicopathological, genomic and radiomic data as predictors was observed in 48.20 %, 51.08 % and 17.27 % of studies, respectively, with some studies using multiple modalities. 21.58 % of studies were externally validated. Twenty-three individual studies compared ML and non-ML methods. Study quality was highly variable and methodologies, statistical reporting and outcome measures were inconsistent, preventing generalized commentary or meta-analysis of performance outcomes.

CONCLUSION

There is significant variability in model development when prognosticating gynecological malignancies with respect to variable selection, machine learning (ML) methods and endpoint selection. This heterogeneity prevents meta-analysis and conclusions regarding the superiority of ML methods. Furthermore, PROBAST-mediated ROB and applicability analysis demonstrates concern for the translatability of existing models. This review identifies ways that this can be improved upon in future works to develop robust, clinically translatable models within this promising field.

A risk stratification and prognostic prediction model for lung adenocarcinoma based on aging-related lncRNA

To create a risk model of aging-related long non-coding RNAs (arlncRNAs) and determine whether they might be useful as markers for risk stratification, prognosis prediction, and targeted therapy guidance for patients with lung adenocarcinoma (LUAD). Data on aging genes and lncRNAs from LUAD patients were obtained from Human Aging Genomic Resources 3 and The Cancer Genome Atlas, and differential co-expression analysis of established differentially expressed arlncRNAs (DEarlncRNAs) was performed. They were then paired with a matrix of 0 or 1 by cyclic single pairing. The risk coefficient for each sample of LUAD individuals was obtained, and a risk model was constructed by performing univariate regression, least absolute shrinkage and selection operator regression analysis, and univariate and multivariate Cox regression analysis. Areas under the curve were calculated for the 1-, 3-, and 5-year receiver operating characteristic curves to determine Akaike information criterion-based cutoffs to identify high- and low-risk groups. The survival rate, correlation of clinical characteristics, malignant-infiltrating immune-cell expression, ICI-related gene expression, and chemotherapeutic drug sensitivity were contrasted with the high- and low-risk groups. We found that 99 DEarlncRNAs were upregulated and 12 were downregulated. Twenty pairs of DEarlncRNA pairs were used to create a prognostic model. The 1-, 3-, and 5-year survival curve areas of LUAD individuals were 0.805, 0.793, and 0.855, respectively. The cutoff value to classify patients into two groups was 0.992. The mortality rate was higher in the high-risk group. We affirmed that the LUAD outcome-related independent predictor was the risk score (p < 0.001). Validation of tumor-infiltrating immune cells and ICI-related gene expression differed substantially between the groups. The high-risk group was highly sensitive to docetaxel, erlotinib, gefitinib, and paclitaxel. Risk models constructed from arlncRNAs can be used for risk stratification in patients with LUAD and serve as prognostic markers to identify patients who might benefit from targeted and chemotherapeutic agents.

Machine learning applications in gynecological cancer: A critical review

Machine Learning (ML) represents a computer science capable of generating predictive models, by exposure to raw, training data, without being rigidly programmed. Over the last few years, ML has gained attention within the field of oncology, with considerable strides in both diagnostic, predictive, and prognostic spectrum of malignancies, but also as a catalyst of cancer research. In this review, we discuss the state of ML applications on gynecologic oncology and systematically address major technical and ethical concerns, with respect to their real-world medical practice translation. Undoubtedly, advances in ML will enable the analysis of large, rather complex, datasets for improved, cost-effective, and efficient clinical decisions.

Comprehensive Expression Profiling and Molecular Basis of CDC28 Protein Kinase Regulatory Subunit 2 in Cervical Cancer

More and more evidence suggests the oncogenic function of overexpressed CDC28 protein kinase regulatory subunit 2 (CKS2) in various human cancers. However, CKS2 has rarely been studied in cervical cancer. Herein, taking advantage of massive genetics data from multicenter RNA-seq and microarrays, we were the first group to perform tissue microarrays for CKS2 in cervical cancer. We were also the first to evaluate the clinical significance of CKS2 with large samples (980 cervical cancer cases and 422 noncancer cases). We further excavated the mechanism of the tumor-promoting activities of CKS2 in cervical cancer through analysis of genetic mutation profiles, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) significant enrichment of genes coexpressed with CKS2. According to the results, expression data from multilevels unanimously supported the overexpression of CKS2 in cervical cancer. Patients with cervical cancer in stage II from inhouse microarrays had significantly higher expression of CKS2, and CKS2 overexpression had an adverse impact on the disease-free survival status of cervical cancer patients in GSE44001. Both mutation types of mRNA high and mRNA low appeared in cervical cancer cases from the TCGA Firehose project. Gene coexpressed with CKS2 participated in pathways including the cell cycle, estrogen signaling pathway, and DNA replication. In summary, upregulated CKS2 is closely associated with the malignant clinical development of cervical cancer and might serve as a valuable therapeutic target in cervical cancer.

Nutrition Interventions of Herbal Compounds on Cellular Senescence

When cells undergo large-scale senescence, organ aging ensues, resulting in irreversible organ pathology and organismal aging. The study of senescence in cells provides an important avenue to understand the factors that influence aging and can be used as one of the useful tools for examining age-related human diseases. At present, many herbal compounds have shown effects on delaying cell senescence. This review summarizes the main characteristics and mechanisms of cell senescence, age-related diseases, and the recent progress on the natural products targeting cellular senescence, with the aim of providing insights to aid the clinical management of age-related diseases.