Comprehensive Analysis Reveals a 4-Gene Signature in Predicting Response to Temozolomide in Low-Grade Glioma Patients

Promising odor-based therapeutics targeting ectopic olfactory receptor proteins in cancer: A review

Cancer remains a formidable adversary in global health, necessitating the development of innovative strategies to curb the proliferation, invasion, and metastasis of cancer cells for effective treatment outcomes. Traditional cancer therapies often fall short in addressing the diverse therapeutic requirements of patients. Consequently, the exploration of novel therapeutic targets has become increasingly vital. Olfactory receptors (ORs) belonging to the G protein-coupled receptor (GPCR) subfamily, are present in non-nasal tissues and contribute to a wide range of physiological functions. ORs are specifically expressed in malignant tumors and have emerged as potential biomarkers for cancer detection. They can regulate diverse tumor biological behaviors and are involved in the development of malignant tumors, indicating that they might serve as potential targets for cancer treatment. This paper provides a comprehensive review of the ectopic expression of ORs, their functions in malignancies and odor-based therapeutics targeting ectopic olfactory receptors (EORs) in cancer, and aims to clarify their connection with cancer, providing new clues for probing the tumor biology and developing therapeutic strategies against cancer.

Construction and validation of cell cycle-related prognostic genetic model for glioblastoma

Glioblastoma (GBM) is a common primary malignant brain tumor and the prognosis of these patients remains poor. Therefore, further understanding of cell cycle-related molecular mechanisms of GBM and identification of appropriate prognostic markers and therapeutic targets are key research imperatives. Based on RNA-seq expression datasets from The Cancer Genome Atlas database, prognosis-related biological processes in GBM were screened out. Gene Set Variation Analysis (GSVA), LASSO-COX, univariate and multivariate Cox regression analyses, Kaplan-Meier survival analysis, and Pearson correlation analysis were performed for constructing a predictive prognostic model. A total of 58 cell cycle-related genes were identified by GSVA and analysis of differential expression between GBM and control samples. By univariate Cox and LASSO regression analyses, 8 genes were identified as prognostic biomarkers in GBM. A nomogram with superior performance to predict the survival of GBM patients was established regarding risk score, cancer status, recurrence type, and mRNAsi. This study revealed the prognostic value of cell cycle-related genes in GBM. In addition, we constructed a reliable model for predicting the prognosis of GBM patients. Our findings reinforce the relationship between cell cycle and GBM and may help improve the prognostic assessment of patients with GBM. Our predictive prognostic model, based on independent prognostic factors, enables tailored treatment strategies for GBM patients. It is particularly useful for subgroups with uncertain prognosis or treatment challenges.

Signature Genes Selection and Functional Analysis of Astrocytoma Phenotypes: A Comparative Study

Novel cancer biomarkers discoveries are driven by the application of omics technologies. The vast quantity of highly dimensional data necessitates the implementation of feature selection. The mathematical basis of different selection methods varies considerably, which may influence subsequent inferences. In the study, feature selection and classification methods were employed to identify six signature gene sets of grade 2 and 3 astrocytoma samples from the Rembrandt repository. Subsequently, the impact of these variables on classification and further discovery of biological patterns was analysed. Principal component analysis (PCA), uniform manifold approximation and projection (UMAP), and hierarchical clustering revealed that the data set (10,096 genes) exhibited a high degree of noise, feature redundancy, and lack of distinct patterns. The application of feature selection methods resulted in a reduction in the number of genes to between 28 and 128. Notably, no single gene was selected by all of the methods tested. Selection led to an increase in classification accuracy and noise reduction. Significant differences in the Gene Ontology terms were discovered, with only 13 terms overlapping. One selection method did not result in any enriched terms. KEGG pathway analysis revealed only one pathway in common (cell cycle), while the two methods did not yield any enriched pathways. The results demonstrated a significant difference in outcomes when classification-type algorithms were utilised in comparison to mixed types (selection and classification). This may result in the inadvertent omission of biological phenomena, while simultaneously achieving enhanced classification outcomes.

Circulating tumor-associated autoantibodies as novel diagnostic biomarkers in pancreatic adenocarcinoma

To develop a superior diagnostic approach for pancreatic adenocarcinoma (PAAC), the present study prospectively included 338 PAAC patients, 294 normal healthy volunteers (NHV), 122 chronic pancreatitis (CP) patients and 100 patients with non-PAAC malignancies. In the identification phase, HuProt Human Proteome Microarray, comprising 21 065 proteins, was used to identify serum tumor-associated autoantibodies (TAAbs) candidates differentiating PAAC (n = 30) from NHV (n = 30). A PAAC-focused array containing 165 differentially expressed TAAbs identified was subsequently adopted in the validation phase (n = 712) for specificity and sensitivities. The multivariate TAAbs signature for differentiation PAAC from controls (NHV + CP) identified five candidates, namely the IgG-type TAAbs against CLDN17, KCNN3, SLAMF7, SLC22A11 and OR51F2. Multivariate logistic performance model of y = (22.893 × CA19-9 + 0.68 × CLDN17 - 4.012) showed a significant better diagnostic accuracy than that of CA19-9 and CLDN17 in differentiating PAAC from controls (NHV + CP) (AUC = 0.97, 0.92 and 0.82, respectively, P-value < .0001). We further tested the autoantigen level of CLDN17 by ELISA in 82 sera samples from PAAC (n = 42), CP (n = 24) and NHV (n = 16). Similarly, the model showed superior diagnostic performance than that of CA19-9 and CLDN17 (AUC = 0.93, 0.83 and 0.81, respectively, P-value < .0001) in differentiating PAAC from controls. In conclusion, our study is the first to characterize the circulating TAAbs signatures in PAAC. The results showed that CLDN17 combined with CA19-9 provided potentially clinical value and may serve as noninvasive novel biomarkers for PAAC diagnosis.

A Risk Model Developed Based on Homologous Recombination Deficiency Predicts Overall Survival in Patients With Lower Grade Glioma

The role of homologous recombination deficiency (HRD) in lower grade glioma (LGG) has not been elucidated, and accurate prognostic prediction is also important for the treatment and management of LGG. The aim of this study was to construct an HRD-based risk model and to explore the immunological and molecular characteristics of this risk model. The HRD score threshold = 10 was determined from 506 LGG samples in The Cancer Genome Atlas cohort using the best cut-off value, and patients with high HRD scores had worse overall survival. A total of 251 HRD-related genes were identified by analyzing differentially expressed genes, 182 of which were associated with survival. A risk score model based on HRD-related genes was constructed using univariate Cox regression, least absolute shrinkage and selection operator regression, and stepwise regression, and patients were divided into high- and low-risk groups using the median risk score. High-risk patients had significantly worse overall survival than low-risk patients. The risk model had excellent predictive performance for overall survival in LGG and was found to be an independent risk factor. The prognostic value of the risk model was validated using an independent cohort. In addition, the risk score was associated with tumor mutation burden and immune cell infiltration in LGG. High-risk patients had higher HRD scores and “hot” tumor immune microenvironment, which could benefit from poly-ADP-ribose polymerase inhibitors and immune checkpoint inhibitors. Overall, this big data study determined the threshold of HRD score in LGG, identified HRD-related genes, developed a risk model based on HRD-related genes, and determined the molecular and immunological characteristics of the risk model. This provides potential new targets for future targeted therapies and facilitates the development of individualized immunotherapy to improve prognosis.

Gene Promoter-Methylation Signature as Biomarker to Predict Cisplatin-Radiotherapy Sensitivity in Locally Advanced Cervical Cancer

Despite efforts to promote health policies focused on screening and early detection, cervical cancer continues to be one of the leading causes of mortality in women; in 2020, estimated 30,000 deaths in Latin America were reported for this type of tumor. While the therapies used to treat cervical cancer have excellent results in tumors identified in early stages, those women who are diagnosed in locally advanced and advanced stages show survival rates at 5 years of <50%. Molecular patterns associated with clinical response have been studied in patients who present resistance to treatment; none of them have reached clinical practice. It is therefore necessary to continue analyzing molecular patterns that allow us to identify patients at risk of developing resistance to conventional therapy. In this study, we analyzed the global methylation profile of 22 patients diagnosed with locally advanced cervical cancer and validated the genomic results in an independent cohort of 70 patients. We showed that BRD9 promoter region methylation and CTU1 demethylation were associated with a higher overall survival (p = 0.06) and progression-free survival (p = 0.0001), whereas DOCK8 demethylation was associated with therapy-resistant patients and a lower overall survival and progression-free survival (p = 0.025 and p = 0.0001, respectively). Our results suggest that methylation of promoter regions in specific genes may provide molecular markers associated with response to treatment in cancer; further investigation is needed.

Odorant receptors in cancer

Odorant receptors (ORs), the largest subfamily of G protein-coupled receptors, detect odorants in the nose. In addition, ORs were recently shown to be expressed in many nonolfactory tissues and cells, indicating that these receptors have physiological and pathophysiological roles beyond olfaction. Many ORs are expressed by tumor cells and tissues, suggesting that they may be associated with cancer progression or may be cancer biomarkers. This review describes OR expression in various types of cancer and the association of these receptors with various types of signaling mechanisms. In addition, the clinical relevance and significance of the levels of OR expression were evaluated. Namely, levels of OR expression in cancer were analyzed based on RNA-sequencing data reported in the Cancer Genome Atlas; OR expression patterns were visualized using t-distributed stochastic neighbor embedding (t-SNE); and the associations between patient survival and levels of OR expression were analyzed. These analyses of the relationships between patient survival and expression patterns obtained from an open mRNA database in cancer patients indicate that ORs may be cancer biomarkers and therapeutic targets.

Odorant receptors in cancer

Odorant receptors (ORs), the largest subfamily of G protein-coupled receptors, detect odorants in the nose. In addition, ORs were recently shown to be expressed in many nonolfactory tissues and cells, indicating that these receptors have physiological and pathophysiological roles beyond olfaction. Many ORs are expressed by tumor cells and tissues, suggesting that they may be associated with cancer progression or may be cancer biomarkers. This review describes OR expression in various types of cancer and the association of these receptors with various types of signaling mechanisms. In addition, the clinical relevance and significance of the levels of OR expression were evaluated. Namely, levels of OR expression in cancer were analyzed based on RNA-sequencing data reported in the Cancer Genome Atlas; OR expression patterns were visualized using t-distributed stochastic neighbor embedding (t-SNE); and the associations between patient survival and levels of OR expression were analyzed. These analyses of the relationships between patient survival and expression patterns obtained from an open mRNA database in cancer patients indicate that ORs may be cancer biomarkers and therapeutic targets. [BMB Reports 2022;55(2): 72-80].

Odorant G protein-coupled receptors as potential therapeutic targets for adult diffuse gliomas: a systematic analysis and review

Odorant receptors (ORs) account for about 60% of all human G protein-coupled receptors (GPCRs). OR expression outside of the nose has functions distinct from odor perception, and may contribute to the pathogenesis of disorders including brain diseases and cancers. Glioma is the most common adult malignant brain tumor and requires novel therapeutic strategies to improve clinical outcomes. Here, we outlined the expression of brain ORs and investigated OR expression levels in glioma. Although most ORs were not ubiquitously expressed in gliomas, a subset of ORs displayed glioma subtype-specific expression. Moreover, through systematic survival analysis on OR genes, OR51E1 (mouse Olfr558) was identified as a potential biomarker of unfavorable overall survival, and OR2C1 (mouse Olfr15) was identified as a potential biomarker of favorable overall survival in isocitrate dehydrogenase (IDH) wild-type glioma. In addition to transcriptomic analysis, mutational profiles revealed that somatic mutations in OR genes were detected in > 60% of glioma samples. OR5D18 (mouse Olfr1155) was the most frequently mutated OR gene, and OR5AR1 (mouse Olfr1019) showed IDH wild-type-specific mutation. Based on this systematic analysis and review of the genomic and transcriptomic profiles of ORs in glioma, we suggest that ORs are potential biomarkers and therapeutic targets for glioma.

Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer

Background

In recent years, the availability of high throughput technologies, establishment of large molecular patient data repositories, and advancement in computing power and storage have allowed elucidation of complex mechanisms implicated in therapeutic response in cancer patients. The breadth and depth of such data, alongside experimental noise and missing values, requires a sophisticated human-machine interaction that would allow effective learning from complex data and accurate forecasting of future outcomes, ideally embedded in the core of machine learning design.

Objective

In this review, we will discuss machine learning techniques utilized for modeling of treatment response in cancer, including Random Forests, support vector machines, neural networks, and linear and logistic regression. We will overview their mathematical foundations and discuss their limitations and alternative approaches in light of their application to therapeutic response modeling in cancer.

Conclusion

We hypothesize that the increase in the number of patient profiles and potential temporal monitoring of patient data will define even more complex techniques, such as deep learning and causal analysis, as central players in therapeutic response modeling.

Development and Multi-Data Set Verification of an RNA Binding Protein Signature for Prognosis Prediction in Glioma

Objective: Increasing evidence emphasizes the clinical implications of RNA binding proteins (RBPs) in cancers. This study aimed to develop a RBP signature for predicting prognosis in glioma.

Methods: Two glioma datasets as training (n = 693) and validation (n = 325) sets were retrieved from the CGGA database. In the training set, univariate Cox regression analysis was conducted to screen prognosis-related RBPs based on differentially expressed RBPs between WHO grade II and IV. A ten-RBP signature was then established. The predictive efficacy was evaluated by ROCs. The applicability was verified in the validation set. The pathways involving the risk scores were analyzed by ssGSEA. scRNA-seq was utilized for evaluating their expression in different glioma cell types. Moreover, their expression was externally validated between glioma and control samples.

Results: Based on 39 prognosis-related RBPs, a ten RBP signature was constructed. High risk score distinctly indicated a poorer prognosis than low risk score. AUCs were separately 0.838 and 0.822 in the training and validation sets, suggesting its well performance for prognosis prediction. Following adjustment of other clinicopathological characteristics, the signature was an independent risk factor. Various cancer-related pathways were significantly activated in samples with high risk score. The scRNA-seq identified that risk RBPs were mainly expressed in glioma malignant cells. Their high expression was also found in glioma than control samples.

Conclusion: This study developed a novel RBP signature for robustly predicting prognosis of glioma following multi-data set verification. These RBPs may affect the progression of glioma.

Identification of an 8-miRNA signature as a potential prognostic biomarker for glioma

Background

Glioma is the most common form of primary malignant intracranial tumor.

Methods

In the current study, miRNA matrix were obtained from the Chinese Glioma Genome Atlas (CGGA), and then univariate Cox regression analysis and Lasso regression analysis were utilized to select candidate miRNAs and multivariate Cox regression analysis was applied to establish a miRNA signature for predicting overall survival (OS) of glioma. The signature was assessed with the area under the curve (AUC) of the receiver operating characteristic curve (ROC) and validated by data from Gene Expression Omnibus (GEO).

Results

Eight miRNAs (miR-1246, miR-148a, miR-150, miR-196a, miR-338-3p, miR-342-5p, miR-548h and miR-645) were included in the miRNA signature. The AUC of ROC analysis for 1- and 3-year OS in the CGGA dataset was 0.747 and 0.905, respectively. In the GEO dataset, The AUC for 1- and 3-year was 0.736 and 0.809, respectively. The AUC in both the CGGA and GEO datasets was similar to that based on WHO 2007 classification (0.736 and 0.799) and WHO 2016 classification (0.663 and 0.807). Additionally, Kaplan–Meier plot revealed that high-risk score patients had a poorer clinical outcome. Multivariate Cox regression analysis suggested that the miRNA signature was an independent prognosis-related factor [HR: 6.579, 95% CI [1.227−35.268], p = 0.028].

Conclusion

On the whole, in the present study, based on eight miRNAs, a novel prognostic signature was developed for predicting the 1- and 3- year survival rate in glioma. The results may be conducive to predict the precise prognosis of glioma and to elucidate the underlying molecular mechanisms. However, further experimental researches of miRNAs are needed to validate the findings of this study.

Integrative analysis of DNA methylation and gene expression to identify key epigenetic genes in glioblastoma

Glioblastoma (GBM) ranks the most common and aggressive primary brain malignant tumor worldwide. However, the survival rates of patients remain very poor. Therefore, molecular oncology of GBM are urgently needed. In this study, we performed an integrative analysis of DNA methylation and gene expression to identify key epigenetic genes in GBM. The methylation and gene expression of GBM patients in The Cancer Genome Atlas (TCGA) database were downloaded. After data preprocessing, we identified 4,881 differentially expressed genes (DEGs) between tumor and normal samples, including 1,111 upregulated and 3,770 downregulated genes. Then, we randomly separated all samples into training set (n = 69) and testing set (n = 69). We next obtained 11,269 survival-methylation sites by univariate and multivariate Cox regression analyses. In the correlation analysis, we defined 198 low promoter methylation with high gene expression as epigenetically induced (EI) genes and 111 high promoter methylation with low gene expression as epigenetically suppressed (ES) genes. Key markers including C1orf61 and FAM50B were selected with a Pearson correlation coefficient greater than 0.75. Further, we chose the 20 CpG methylation sites of above two genes in unsupervised clustering analysis using the Euclidean distance. We found that the prognosis of the hypomethylated group was significantly better than that in the hypermethylated group (log-rank test p-value = 0.011). Based on the validation in the TCGA testing set and GEO dataset, we validated the prognostic value of our signature (p-value = 0.02 in TCGA and 0.012 in GEO). In conclusion, our findings provided predictive and prognostic value as methylation-based biomarkers for the diagnosis and treatment of GBM.