Current approaches in glioblastoma multiforme immunotherapy

Glioblastoma multiform (GBM) is the most prevalent CNS (central nervous system) tumor in adults, with an average survival length shorter than 2 years and rare metastasis to organs other than CNS. Despite extensive attempts at surgical resecting, the inherently permeable nature of this disease has rendered relapse nearly unavoidable. Thus, immunotherapy is a feasible alternative, as stimulated immune cells can enter into the remote and inaccessible tumor cells. Immunotherapy has revolutionized patient upshots in various malignancies and might introduce different effective ways for GBM patients. Currently, researchers are exploring various immunotherapeutic strategies in patients with GBM to target both the innate and acquired immune responses. These approaches include reprogrammed tumor-associated macrophages, the use of specific antibodies to inhibit tumor progression and metastasis, modifying tumor-associated macrophages with antibodies, vaccines that utilize tumor-specific dendritic cells to activate anti-tumor T cells, immune checkpoint inhibitors, and enhanced T cells that function against tumor cells. Despite these findings, there is still room for improving the response faults of the many currently tested immunotherapies. This study aims to review the currently used immunotherapy approaches with their molecular mechanisms and clinical application in GBM.

Role of long non-coding RNA ELFN1-AS1 in carcinogenesis

As one of the leading causes of death worldwide, cancer significantly burdens patients and the healthcare system. The role of long non-protein coding RNAs (lncRNAs) in carcinogenesis has been extensively studied. The lncRNA ELFN1-AS1 was discovered recently, and subsequent studies have revealed its aberrantly high expression in various cancer tissues. In vitro and in vivo experiments have consistently demonstrated the close association between increased ELFN1-AS1 expression and malignant tumor characteristics, particularly in gastrointestinal malignancies. Functional assays have further revealed the mechanistic role of ELFN1-AS1 as a competitive endogenous RNA for microRNAs, inducing tumor growth, invasive features, and drug resistance. Additionally, the investigation into the clinical implication of ELFN1-AS1 has demonstrated its potential as a diagnostic, therapeutic, and, notably, prognostic marker. This review provides a comprehensive summary of evidence regarding the involvement of ELFN1-AS1 in cancer initiation and development, highlighting its clinical significance.

MRI-Based Radiomics Methods for Predicting Ki-67 Expression in Breast Cancer: A Systematic Review and Meta-analysis

RATIONALE AND OBJECTIVES

The purpose of this systematic review and meta-analysis was to assess the quality and diagnostic accuracy of MRI-based radiomics for predicting Ki-67 expression in breast cancer.

MATERIALS AND METHODS

A systematic literature search was performed to find relevant studies published in different databases, including PubMed, Web of Science, and Embase up until March 10, 2023. All papers were independently evaluated for eligibility by two reviewers. Studies that matched research questions and provided sufficient data for quantitative synthesis were included in the systematic review and meta-analysis, respectively. The quality of the articles was assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools. The predictive value of MRI-based radiomics for Ki-67 antigen in patients with breast cancer was assessed using pooled sensitivity (SEN), specificity, and area under the curve (AUC). Meta-regression was performed to explore the cause of heterogeneity. Different covariates were used for subgroup analysis.

RESULTS

31 studies were included in the systematic review; among them, 21 reported sufficient data for meta-analysis. 20 training cohorts and five validation cohorts were pooled separately. The pooled sensitivity, specificity, and AUC of MRI-based radiomics for predicting Ki-67 expression in training cohorts were 0.80 [95% CI, 0.73-0.86], 0.82 [95% CI, 0.78-0.86], and 0.88 [95%CI, 0.85-0.91], respectively. The corresponding values for validation cohorts were 0.81 [95% CI, 0.72-0.87], 0.73 [95% CI, 0.62-0.82], and 0.84 [95%CI, 0.80-0.87], respectively. Based on QUADAS-2, some risks of bias were detected for reference standard and flow and timing domains. However, the quality of the included article was acceptable. The mean RQS score of the included articles was close to 6, corresponding to 16.6% of the maximum possible score. Significant heterogeneity was observed in pooled sensitivity and specificity of training cohorts (I2 > 75%). We found that using deep learning radiomic methods, magnetic field strength (3 T vs. 1.5 T), scanner manufacturer, region of interest structure (2D vs. 3D), route of tissue sampling, Ki-67 cut-off, logistic regression for model construction, and LASSO for feature reduction as well as PyRadiomics software for feature extraction had a great impact on heterogeneity according to our joint model analysis. Diagnostic performance in studies that used deep learning-based radiomics and multiple MRI sequences (e.g., DWI+DCE) was slightly higher. In addition, radiomic features derived from DWI sequences performed better than contrast-enhanced sequences in terms of specificity and sensitivity. No publication bias was found based on Deeks' funnel plot. Sensitivity analysis showed that eliminating every study one by one does not impact overall results.

CONCLUSION

This meta-analysis showed that MRI-based radiomics has a good diagnostic accuracy in differentiating breast cancer patients with high Ki-67 expression from low-expressing groups. However, the sensitivity and specificity of these methods still do not surpass 90%, restricting them from being used as a supplement to current pathological assessments (e.g., biopsy or surgery) to predict Ki-67 expression accurately.

FOXO1, a tiny protein with intricate interactions: Promising therapeutic candidate in lung cancer

Nowadays, lung cancer is the most common cause of cancer-related deaths in both men and women globally. Despite the development of extremely efficient targeted agents, lung cancer progression and drug resistance remain serious clinical issues. Increasing knowledge of the molecular mechanisms underlying progression and drug resistance will enable the development of novel therapeutic methods. It has been revealed that transcription factors (TF) dysregulation, which results in considerable expression modifications of genes, is a generally prevalent phenomenon regarding human malignancies. The forkhead box O1 (FOXO1), a member of the forkhead transcription factor family with crucial roles in cell fate decisions, is suggested to play a pivotal role as a tumor suppressor in a variety of malignancies, especially in lung cancer. FOXO1 is involved in diverse cellular processes and also has clinical significance consisting of cell cycle arrest, apoptosis, DNA repair, oxidative stress, cancer prevention, treatment, and chemo/radioresistance. Based on the critical role of FOXO1, this transcription factor appears to be an appropriate target for future drug discovery in lung cancers. This review focused on the signaling pathways, and molecular mechanisms involved in FOXO1 regulation in lung cancer. We also discuss pharmacological compounds that are currently being administered for lung cancer treatment by affecting FOXO1 and also point out the essential role of FOXO1 in drug resistance. Future preclinical research should assess combination drug strategies to stimulate FOXO1 and its upstream regulators as potential strategies to treat resistant or advanced lung cancers.

Radiomics diagnostic performance in predicting lymph node metastasis of papillary thyroid carcinoma: A systematic review and meta-analysis

PURPOSE

To evaluate the diagnostic performance of radiomics in lymph node metastasis (LNM) prediction in patients with papillary thyroid carcinoma (PTC) through a systematic review and meta-analysis.

METHOD

A literature search of PubMed, EMBASE, and Web of Science was conducted to find relevant studies published until February 18th, 2023. Studies that reported the accuracy of radiomics in different imaging modalities for LNM prediction in PTC patients were selected. The methodological quality of included studies was evaluated by radiomics quality score (RQS) and quality assessment of diagnostic accuracy studies (QUADAS-2) tools. General characteristics and radiomics accuracy were extracted. Overall sensitivity, specificity, and area under the curve (AUC) were calculated for diagnostic accuracy evaluation. Spearman correlation coefficient and subgroup analysis were performed for heterogeneity exploration.

RESULTS

In total, 25 studies were included, of which 22 studies provided adequate data for meta-analysis. We conducted two types of meta-analysis: one focused solely on radiomics features models and the other combined radiomics and non-radiomics features models in the analysis. The pooled sensitivity, specificity, and AUC of radiomics and combined models were 0.75 [0.68, 0.80] vs. 0.77 [0.74, 0.80], 0.77 [0.74, 0.81] vs. 0.83 [0.78, 0.87] and 0.80 [0.73, 0.85] vs 0.82 [0.75, 0.88], respectively. The analysis showed a high heterogeneity level among the included studies. There was no threshold effect. The subgroup analysis demonstrated that utilizing ultrasonography, 2D segmentation, central and lateral LNM detection, automatic segmentation, and PyRadiomics software could slightly improve diagnostic accuracy.

CONCLUSIONS

Our meta-analysis shows that the radiomics has the potential for pre-operative LNM prediction in PTC patients. Although methodological quality is sufficient but we still need more prospective studies with larger sample sizes from different centers.

Diagnostic performance of CT scan-based radiomics for prediction of lymph node metastasis in gastric cancer: a systematic review and meta-analysis

Objective

The purpose of this study was to evaluate the diagnostic performance of computed tomography (CT) scan-based radiomics in prediction of lymph node metastasis (LNM) in gastric cancer (GC) patients.

Methods

PubMed, Embase, Web of Science, and Cochrane Library databases were searched for original studies published until 10 November 2022, and the studies satisfying the inclusion criteria were included. Characteristics of included studies and radiomics approach and data for constructing 2 × 2 tables were extracted. The radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) were utilized for the quality assessment of included studies. Overall sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated to assess diagnostic accuracy. The subgroup analysis and Spearman's correlation coefficient was done for exploration of heterogeneity sources.

Results

Fifteen studies with 7,010 GC patients were included. We conducted analyses on both radiomics signature and combined (based on signature and clinical features) models. The pooled sensitivity, specificity, DOR, and AUC of radiomics models compared to combined models were 0.75 (95% CI, 0.67-0.82) versus 0.81 (95% CI, 0.75-0.86), 0.80 (95% CI, 0.73-0.86) versus 0.85 (95% CI, 0.79-0.89), 13 (95% CI, 7-23) versus 23 (95% CI, 13-42), and 0.85 (95% CI, 0.81-0.86) versus 0.90 (95% CI, 0.87-0.92), respectively. The meta-analysis indicated a significant heterogeneity among studies. The subgroup analysis revealed that arterial phase CT scan, tumoral and nodal regions of interest (ROIs), automatic segmentation, and two-dimensional (2D) ROI could improve diagnostic accuracy compared to venous phase CT scan, tumoral-only ROI, manual segmentation, and 3D ROI, respectively. Overall, the quality of studies was quite acceptable based on both QUADAS-2 and RQS tools.

Conclusion

CT scan-based radiomics approach has a promising potential for the prediction of LNM in GC patients preoperatively as a non-invasive diagnostic tool. Methodological heterogeneity is the main limitation of the included studies.

Systematic review registration

https://www.crd.york.ac.uk/Prospero/display_record.php?RecordID=287676, identifier CRD42022287676.

Differentiating nasopharyngeal carcinoma from lymphoma in the head and neck region using the apparent diffusion coefficient (ADC) value: a systematic review and meta-analysis

Purpose

This study aimed to assess the applicability of the apparent diffusion coefficient (ADC) for differentiating nasopharyngeal carcinoma (NPC) from lymphomas in the head and neck region.

Material and methods

Four databases, including PubMed, the Cochrane Library, EMBASE, and Web of Science, were searched systematically to find relevant literature. The search date was updated to 8 September 2022, with no starting time restriction. The methodological quality of the studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Firstly, a random-effects model was used in a meta-analysis of continuous variables with low heterogeneity to determine the overall effect size, which was reported as the standard mean difference (SMD). Then, bivariate random effects modelling was used to calculate the combined sensitivity and specificity. The area under the curve (AUC) for each diffusion parameter was calculated after constructing summary receiver operating characteristic curves. The presence of heterogeneity was evaluated using subgroup and meta-regression analysis.

Results

Twelve studies involving 181 lymphoma and 449 NPC lesions (N = 630) in the head and neck region were included, of which 5 studies provided sufficient data for pooling diagnostic test accuracy. A meta-analysis of the 12 studies using a random-effects model yielded an SMD of 1.03 (CI = 0.76-1.30; p = 0.00001), implying that NPC lesions had a significantly higher ADC value than lymphoma lesions. By pooling 5 standard DWI studies, the pooled sensitivity and specificity of ADC were 0.90 (95% CI: 0.82-0.95) and 0.63 (95% CI: 0.52-0.72), respectively. The area under the curve (AUC) calculated from the SROC curve was 0.74 (95% CI: 0.70-0.78).

Conclusions

According to this systematic review and meta-analysis, nasopharyngeal carcinoma has a significantly higher ADC value than lymphomas. Furthermore, while ADC has excellent sensitivity for distinguishing these 2 types of tumours, its specificity is relatively low, yielding a moderate diagnostic performance. Further investigations with larger sample sizes are required.

Forkhead box transcription factors (FOXOs and FOXM1) in glioma: from molecular mechanisms to therapeutics

Glioma is the most aggressive and malignant type of primary brain tumor, comprises the majority of central nervous system deaths, and is categorized into different subgroups according to its histological characteristics, including astrocytomas, oligodendrogliomas, glioblastoma multiforme (GBM), and mixed tumors. The forkhead box (FOX) transcription factors comprise a collection of proteins that play various roles in numerous complex molecular cascades and have been discovered to be differentially expressed in distinct glioma subtypes. FOXM1 and FOXOs have been recognized as crucial transcription factors in tumor cells, including glioma cells. Accumulating data indicates that FOXM1 acts as an oncogene in various types of cancers, and a significant part of studies has investigated its function in glioma. Although recent studies considered FOXO subgroups as tumor suppressors, there are pieces of evidence that they may have an oncogenic role. This review will discuss the subtle functions of FOXOs and FOXM1 in gliomas, dissecting their regulatory network with other proteins, microRNAs and their role in glioma progression, including stem cell differentiation and therapy resistance/sensitivity, alongside highlighting recent pharmacological progress for modulating their expression.

A review on the roles and molecular mechanisms of MAFG-AS1 in oncogenesis

Long non-coding RNAs (lncRNAs) have more than 200 nucleotides and do not encode proteins. At the same time, they can regulate various biological functions and therefore play an essential role as oncogenes or tumor suppressors in human cancers. MAFG-AS1 is an antisense RNA of MAF BZIP Transcription Factor G (MAFG) located at chromosome 17q25.3 head-to-head with the MAFG encoding gene containing a transcript size of 1895 bp. Accumulating evidence shows that MAFG-AS1 is overexpressed in many cancers, functions as an oncogene, and is significantly associated with poor clinical characteristics and prognosis. In this review, we first discuss the recent literature regarding the role of MAFG-AS1 in different cancers as well as its diagnostic and prognostic values. Then we will provide insights into its biological functions, such as its role in cancer progression, competing endogenous RNA (ceRNA) activity, regulation of EMT, glycolysis, energy metabolism, transcription factors, proteasomal degradation, and signaling pathways.

Role of m6A modification in dysregulation of Wnt/β-catenin pathway in cancer

N6-methyladenosine (m⁶A) modification is the most abundant post-transcriptional regulation of RNAs in eukaryotes. Dysregulation of m⁶A readers, writers, and erasers can significantly promote tumorigenesis by altering the expression of various genes. Wnt/β-catenin is an evolutionarily conserved signaling pathway that has recently been linked to the pathogenesis of many cancers. Given the significance of this pathway in regulating normal tissue homeostasis and stem cell differentiation, a subtle understanding of the molecular mechanism underlying its dysregulation is required for effective targeting. There is mounting evidence that m⁶A regulators are highly implicated in the dysregulation of the Wnt/β-catenin signaling pathway. Since m⁶A regulators can affect Wnt pathway components and dysregulation of either leads to carcinogenesis, this study aims to clarify the relationship between m⁶A regulators and the Wnt/β-catenin signaling pathway to investigate their combined impact on tumorigenesis.

Natural products in suppressing glioma progression: A focus on the role of microRNAs

Glioma is one of the most common malignancies of the central nervous system. Due to inadequate response to the current treatments available, glioma has been at the center of recent cancer studies searching for novel treatment strategies. This has prompted an intensive search using linkage studies and preliminary evidence to gain efficient insight into the mechanisms involved in the alleviation of the pathogenesis of glioma mediated by miRNAs, a group of noncoding RNAs that affect gene expression posttranscriptionally. Dysregulated expression of miRNAs can exacerbate the malignant features of tumor cells in glioma and other cancers. Natural products can exert anticancer effects on glioma cells by stimulating the expression levels of tumor suppressor miRNAs and repressing the expression levels of oncogenic miRNAs. In this review, we aimed to collect and analyze the literature addressing the roles of natural products in the treatment of glioma, with an emphasis on their involvement in the regulation of miRNAs.

Ferroptosis in Lung Cancer: From Molecular Mechanisms to Prognostic and Therapeutic Opportunities

Lung cancer is the second commonly diagnosed malignancy worldwide and has the highest mortality rate among all cancers. Tremendous efforts have been made to develop novel strategies against lung cancer; however, the overall survival of patients still is low. Uncovering underlying molecular mechanisms of this disease can open up new horizons for its treatment. Ferroptosis is a newly discovered type of programmed cell death that, in an iron-dependent manner, peroxidizes unsaturated phospholipids and results in the accumulation of radical oxygen species. Subsequent oxidative damage caused by ferroptosis contributes to cell death in tumor cells. Therefore, understanding its molecular mechanisms in lung cancer appears as a promising strategy to induce ferroptosis selectively. According to evidence published up to now, significant numbers of research have been done to identify ferroptosis regulators in lung cancer. Therefore, this review aims to provide a comprehensive standpoint of molecular mechanisms of ferroptosis in lung cancer and address these molecules’ prognostic and therapeutic values, hoping that the road for future studies in this field will be paved more efficiently.