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  • 1
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    Cancer-Associated Fibroblasts in Gastrointestinal Cancers: Unveiling Their Dynamic Roles in the Tumor Microenvironment
    MDPI AG ; 2023
    In:  International Journal of Molecular Sciences Vol. 24, No. 22 ( 2023-11-19), p. 16505-
    Details
    In: International Journal of Molecular Sciences, MDPI AG, Vol. 24, No. 22 ( 2023-11-19), p. 16505-
    Abstract: Gastrointestinal cancers are highly aggressive malignancies with significant mortality rates. Recent research emphasizes the critical role of the tumor microenvironment (TME) in these cancers, which includes cancer-associated fibroblasts (CAFs), a key component of the TME that have diverse origins, including fibroblasts, mesenchymal stem cells, and endothelial cells. Several markers, such as α-SMA and FAP, have been identified to label CAFs, and some specific markers may serve as potential therapeutic targets. In this review article, we summarize the literature on the multifaceted role of CAFs in tumor progression, including their effects on angiogenesis, immune suppression, invasion, and metastasis. In addition, we highlight the use of single-cell transcriptomics to understand CAF heterogeneity and their interactions within the TME. Moreover, we discuss the dynamic interplay between CAFs and the immune system, which contributes to immunosuppression in the TME, and the potential for CAF-targeted therapies and combination approaches with immunotherapy to improve cancer treatment outcomes.
    Type of Medium: Online Resource
    ISSN: 1422-0067
    URL: Article
    DOI: 10.3390/ijms242216505
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
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  • 2
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    Abstract 6557: A 6-microbial signature risk score for overall survival prediction in hepatocellular carcinoma: a machine learning approach
    American Association for Cancer Research (AACR) ; 2023
    In:  Cancer Research Vol. 83, No. 7_Supplement ( 2023-04-04), p. 6557-6557
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 6557-6557
    Abstract: Introduction: Hepatocellular carcinoma (HCC) is a severe type of cancer associated with high malignancy and poor prognosis. To reach a better prognosis, several factors affecting HCC patients must be considered. In this study, we aim to investigate the relationship between gut microbiota with the survival of HCC patients, and to build a machine learning (ML) prognostic model based on microbiome signatures. Methods: Microbial and survival data for HCC patients from The Cancer Genome Atlas Program (TCGA) were obtained using the cBioportal database. The microbiome signatures were screened for prognostic effect using the univariate cox regression. The significant microbial signatures were fitted into the Least Absolute Selection and Shrinkage Operator - Cox (LASSO-Cox) model to reduce multicollinearity. Signatures with non-zero LASSO coefficients were screened for independent effect in the multivariate cox regression analysis. Results: A total of 1407 microbial signatures were downloaded from TCGA dataset. 63 prognostic microbial signatures were identified using the univariate cox. A total of 30 prognostic microbial signatures were screened out using LASSO and further evaluated using independent effect to obtain microbiome risk score. Six microbial signatures were identified as independent prognostic markers including: Marichromatium (HR: 1.26; 95%CI: [0.01-1.56]), p=0.038), Paraclostridium (HR: 1.45; 95%CI:[1.15-1.82] , p=0.002), Pseudorhodoferax (HR: 1.20; 95%CI:[1.01-1.43], p=0.037), Chitinimonas (HR: 1.47 95%CI: [1.10-1.97] , p=0.009), Marinobacter (HR: 0.79; 95%CI: [0.65-0.97], p=0.022), Desulfovermiculus (HR: 1.44; 95%CI: [1.16-1.78, p=0.001] ). Kaplan-Meier survival plot showed better overall survival (OS) in the low-risk group compared to the high-risk group (median OS: 83.6 vs. 41.8; log-rank p-value & lt;0.0001). Conclusion: Our results show that the gut microbiota is significantly associated with HCC prognosis, in which 5 of them were associated with poorer prognosis and one was associated with better survival. These microbial signatures provide a potential prognostic marker for HCC survival. Citation Format: Nour N. Al-Bzour, Ayah N. Al-Bzour. A 6-microbial signature risk score for overall survival prediction in hepatocellular carcinoma: a machine learning approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6557.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2023-6557
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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  • 3
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    Meta-analytical evidence of functional and structural abnormalities associated with pain processing in migraine patients: An activation likelihood estimation
    Ovid Technologies (Wolters Kluwer Health) ; 2022
    In:  Medicine Vol. 101, No. 43 ( 2022-10-28), p. e31206-
    Details
    In: Medicine, Ovid Technologies (Wolters Kluwer Health), Vol. 101, No. 43 ( 2022-10-28), p. e31206-
    Abstract: Migraine is a primary headache disorder that causes debilitating throbbing pain. Several functional MRI (fMRI) and voxel-based morphometry (VBM) studies have been used to investigate the structural and functional alteration in migraine. Here, we aim to study the converged brain regions of functional and structural abnormalities in gray matter volume (GMV) associated with pain processing and management in migraineurs and healthy controls (HC). Methods: A systematic search through PubMed and Sleuth was carried out for peer-reviewed functional and structural neuroimaging studies on migraine patients and HC yielded a total of 1136 studies. We performed an activation likelihood estimation (ALE) meta-analysis on VBM and pain stimulation task-based fMRI studies to investigate the converged areas of GMV and functional abnormalities between migraineurs and HC. We performed two subgroup analyses between migraine with aura (MwA) and migraine without aura (MwoA) relative to HC, and between chronic migraine (CM) and episodic migraine (EM) compared to HC. Results: The total sample included 16 fMRI and 22 VBM studies, consisting of 1295 migraine patients, compared to 995 HC. In fMRI analysis, ALE maps for pain stimulation tasks revealed hyperactivation in migraineurs in the substantia nigra compared to HC, whereas hypoactivation was seen in the cerebellum. For the VBM analysis, ALE clusters of increased GMV in migraineurs were observed in the parahippocampus and putamen nucleus. Whereas clusters of reduced GMV in migraineurs were seen in the frontal gyri. Compared to HC, MwoA patients showed a GMV reduction in the insula, and anterior cingulate, whereas MwA patients showed GMV reduction in the cerebellum, cingulate gyrus, and insula. CM patients showed decreased GMV in the precentral gyrus, whereas EM patients showed decreased GMV in the parahippocampus, and inferior frontal gyrus when compared to HC. Conclusions: Our findings represent a potential biomarker for the diagnosis and management of migraine, by showing clustered brain regions of abnormal patterns of activation and GMV changes between migraineurs and HC which might be associated with hyposensitivity to pain in migraineurs. Further studies are required to determine disease progression or therapeutic interventions’ effect on migraine.
    Type of Medium: Online Resource
    ISSN: 0025-7974
    URL: Article
    DOI: 10.1097/MD.0000000000031206
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2022
    detail.hit.zdb_id: 2049818-4
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  • 4
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    Limbic and cortical regions as functional biomarkers associated with emotion regulation in bipolar disorder: A meta-analysis of neuroimaging studies
    Elsevier BV ; 2023
    In:  Journal of Affective Disorders Vol. 323 ( 2023-02), p. 506-513
    Details
    In: Journal of Affective Disorders, Elsevier BV, Vol. 323 ( 2023-02), p. 506-513
    Type of Medium: Online Resource
    ISSN: 0165-0327
    URL: Article
    DOI: 10.1016/j.jad.2022.11.071
    RVK:
    XA 10000
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
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    SSG: 12
    SSG: 5,2
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  • 5
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    Compensatory Hyperhidrosis After Non-Surgical Treatment of Primary Focal Hyperhidrosis: Two-Year Single-Centered Prospective Study From Jordan
    SAGE Publications ; 2023
    In:  Journal of Cutaneous Medicine and Surgery
    Details
    In: Journal of Cutaneous Medicine and Surgery, SAGE Publications
    Abstract: Primary focal hyperhidrosis (PH) can be managed by a wide range of medical and surgical modalities. Compensatory hyperhidrosis (CH) is a well-documented complication of surgical treatment. We aimed to investigate the occurrence of compensatory hyperhidrosis (CH) in PH patients after nonsurgical treatment with botulinum toxin A (BTX- A) or iontophoresis. Methodology We carried out a unicentric prospective study on PH patients from King Abdullah University Hospital (KAUH) in Jordan. PH patients were evaluated after 1-month of nonsurgical treatment. Patients who developed CH were re-assessed after 3-6 months through a telephone-based interview. Results A total of 86 patients with PH who underwent nonsurgical treatment with iontophoresis or botulinum toxin were recruited. Twenty-four (27.9%) patients developed subjective CH. It was mild in (75%), moderate in (21%), and severe in (4%) of patients affected, it was self-limiting within a few months in all patients. Patients with CH did not differ significantly in demographic or clinical variables from patients who did not develop CH except at the site of PH (p value = .05). Conclusion The findings of this study indicate that more than quarter (27.9%) of patients with PH may develop minor compensatory sweating, however this didn’t affect satisfaction with treatment.
    Type of Medium: Online Resource
    ISSN: 1203-4754 , 1615-7109
    URL: Article
    DOI: 10.1177/12034754231191488
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
    detail.hit.zdb_id: 2038674-6
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  • 6
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    Exploring Genetic Determinants: A Comprehensive Analysis of Serpin B Family SNPs and Prognosis in Glioblastoma Multiforme Patients
    MDPI AG ; 2024
    In:  Cancers Vol. 16, No. 6 ( 2024-03-10), p. 1112-
    Details
    In: Cancers, MDPI AG, Vol. 16, No. 6 ( 2024-03-10), p. 1112-
    Abstract: Serpins are serine proteinase inhibitors, with several serpins being overexpressed in cancer cells. Thus, we aim to analyze the single-nucleotide polymorphism (SNP) of Serpinb11 and its association with GBM survival. A cohort of 63 GBM patients recruited from King Abdullah University Hospital in Jordan underwent polymorphism analysis and overall survival (OS) assessments. The Cancer Genome Atlas (GBM) cohort was useful for validation. We constructed a risk score using the principal component analysis for the following Serpin genes: Serpinb3, Serpinb5, Serpinb6, Serpinb11, and Serpinb12, and patients were grouped into high- vs. low-risk groups based on the median cutoff. Univariable Cox models were used to study the survival outcomes. We identified a significant association between rs4940595 and survival. In the TCGA cohort, Serpinb3 alterations showed worse OS. Univariable Cox showed worse PFS outcomes with higher SERPINB5 and SERPINB6 expression. A Serpin B 5-gene risk score showed a trend towards worse PFS in the high-risk group. Upregulated DEGs showed GO enrichment in cytokine regulation and production, positive regulation of leukocyte activation, and the MAPK cascade. The high-risk group showed a significantly higher infiltration of M2 macrophages and activated mast cells. Our findings showed a significant role of the Serpin B family in GBM survival in the Jordanian population.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers16061112
    Language: English
    Publisher: MDPI AG
    Publication Date: 2024
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  • 7
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    Abstract 2046: Identification of the cuproptosis-related gene signature associated with the tumor environment and prognosis of patients with glioblastoma multiforme (GBM)
    American Association for Cancer Research (AACR) ; 2023
    In:  Cancer Research Vol. 83, No. 7_Supplement ( 2023-04-04), p. 2046-2046
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 2046-2046
    Abstract: Background: Glioblastoma multiforme (GBM) is the most common tumor of the central nervous system with poor prognosis. Cuproptosis is a novel programmed cell death pathway targeting lipoylated tricarboxylic acid cycle proteins. Previous studies have found that it participates in tumor progression, but its role in GBM is still elusive. In this study, we aimed to develop a cuproptosis gene-signature risk score using bioinformatics analysis and machine learning. Methods: We acquired transcriptomic and clinical information of GBM patients from The Cancer Genome Atlas (TCGA). A total of 2283 differentially expressed genes (DEGs) were obtained from the GEPIA2 database. 26 cuproptosis-related genes (CRGs) were retrieved from literature. A correlation analysis between the 26 CRGs and the DEGs were conducted to retrieve the cuproptosis-related DEGs. Then, a univariate cox analysis was conducted to obtain the prognostic-related DEGs for overall survival (OS). The least absolute shrinkage and selection operator (LASSO) were conducted for regularization and the gene risk score was constructed using the multivariate cox coefficients. Results: A total of 731 downregulated DEGs were correlated with CRGs, while 68 upregulated DEGs were correlated with CRGs and were further screened for prognostic value using the univariate cox analysis. A total of 70 prognostic related CRGs were identified and were further screened using the LASSO cox analysis. After multivariate cox analysis, a total of seven genes were significantly associated with survival (p-value & lt;0.01). A risk-score gene signature was constructed from the cox coefficients multiplied by the expression of the following genes: -0.0012*DPP10+0.0021*EGR4+0.0015*ITPKA+ 0.0003* PTPRN+ 0.0007* STEAP2+ 0.0006* TENM2+- 0.0017* ZNF540. Conclusion: Univariate and multivariate Cox regression analyses showed the CRGs-based prognostic signature independently functioned as a risk factor for OS in GBM patients. Furthermore, our results gave a promising understanding of cuproptosis in GBM, as well as a tailored prediction tool for prognosis and immunotherapeutic responses in patients. Citation Format: Yaman B. Ahmed, Ayah N. Al-Bzour, Ghayda'a N. Al-Majali, Zaid M. Khalefa, Saja M. Alzghoul. Identification of the cuproptosis-related gene signature associated with the tumor environment and prognosis of patients with glioblastoma multiforme (GBM) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2046.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2023-2046
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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  • 8
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    A prognostic machine learning model for the prediction of pancreatic adenocarcinoma prognosis based on genomic expression of four cell-cycle associated hub genes
    AME Publishing Company ; 2023
    In:  Annals of Pancreatic Cancer Vol. 6 ( 2023-4), p. 4-4
    Details
    In: Annals of Pancreatic Cancer, AME Publishing Company, Vol. 6 ( 2023-4), p. 4-4
    Type of Medium: Online Resource
    ISSN: 2616-2741
    URL: Journal
    URL: Article
    DOI: 10.21037/apc
    DOI: 10.21037/apc-22-6
    Language: Unknown
    Publisher: AME Publishing Company
    Publication Date: 2023
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  • 9
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    Abstract 650: A machine learning model accurately predicts response to immune checkpoint inhibitors in colorectal cancer using the gut microbiome
    American Association for Cancer Research (AACR) ; 2023
    In:  Cancer Research Vol. 83, No. 7_Supplement ( 2023-04-04), p. 650-650
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 650-650
    Abstract: Background: Immune checkpoint inhibitors (ICI) have shown a long-lasting response in many cancers such as colorectal cancer. However, only patients with microsatellite instability (MSI-High) or mismatch repair deficiency (dMMR) benefit from ICI. One of the many factors that can influence ICI-response is the gut microbiome, however its effect remains elusive. Here we aim to investigate the role of the gut microbiome in ICI-response using machine learning (ML). Methods: Clinical and genomic data of ICI-treated CRC patients were retrieved from the KEYNOTE-177 trial. Microbiome abundances from The Cancer Genome Atlas (TCGA) were downloaded from the cBioportal database. A label propagation semi-supervised ML approach was conducted to label patients’ responses to ICI treatment in the TCGA cohort. Tumor mutational burden (TMB), MSI status, treatment, histological subtype, TNM staging and prior chemotherapy were fitted into the semi-supervised model to predict ICI-response in the combination cohort. The normalized abundance of 1406 microbial signatures were regularized using a LASSO model to avoid multicollinearity. Signatures with a non-zero LASSO coefficients were fitted into a supervised ML Random Forest Classification model (RFC) to evaluate their prediction for ICI-response. Samples were split with 80:20 training-testing ratio, and model’s performance was evaluated on the testing set using mean bootstrap estimate, 10-fold cross-validation (CV), and area under curve (AUC). Results: A total of 538 CRC patients from the TCGA and 29 ICI-treat patients from the KEYNOTE-177 trial were fitted into the semi-supervised model to label ICI-response in the TCGA cohort. A total of 95 patients were labeled as responder (R) while 488 patients were labeled as non-responder (NR). Responders had significantly higher TMB levels than NR (mean TMB: 22.5 vs. 12.2, p-value & lt;0.0001). Of the 95 R, 42 were MSS, and 53 patients were MSI-High. While 417 of the NR were MSS. A total of 25 microbial signatures were screened after LASSO for their ICI prediction and were fitted into the RFC model along with TMB and MSI status (Hereafter called RFC27). The RFC27 model performed on the testing set with mean bootstrap estimate of 0.88 and 95%CI: [0.83-0.94], 10-fold CV of 0.84 and AUC of 0.97. TMB and MSI showed highest feature contribution followed by Actinopolyspora and Bacteroides Eggerthia. The relative abundance of both microbiotas was significantly different between R and NR (mean: 0.99 vs. 0.47, 2.0 vs. 1.6; p-value & lt;0.001 respectively). Conclusions: Our results show that the gut microbiome can accurately predict ICI-response and their higher abundance contributed to a better response. Previous studies also reported a pro-inflammatory response of Bacteroides in addition to their association with ICI-response in melanoma patients, thus providing a potential therapeutic marker for ICI. Citation Format: Ayah N. Al-Bzour. A machine learning model accurately predicts response to immune checkpoint inhibitors in colorectal cancer using the gut microbiome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 650.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2023-650
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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  • 10
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    Genomic and Transcriptomic Predictors of Response to Immune Checkpoint Inhibitors in Melanoma Patients: A Machine Learning Approach
    MDPI AG ; 2022
    In:  Cancers Vol. 14, No. 22 ( 2022-11-15), p. 5605-
    Details
    In: Cancers, MDPI AG, Vol. 14, No. 22 ( 2022-11-15), p. 5605-
    Abstract: Immune checkpoint inhibitors (ICIs) became one of the most revolutionary cancer treatments, especially in melanoma. While they have been proven to prolong survival with lesser side effects compared to chemotherapy, the accurate prediction of response remains to be an unmet gap. Thus, we aim to identify accurate clinical and transcriptomic biomarkers for ICI response in melanoma. We also provide mechanistic insight into how high-performing markers impose their effect on the tumor microenvironment (TME). Clinical and transcriptomic data were retrieved from melanoma studies administering ICIs from cBioportal and GEO databases. Four machine learning models were developed using random-forest classification (RFC) entailing clinical and genomic features (RFC7), differentially expressed genes (DEGs, RFC-Seq), survival-related DEGs (RFC-Surv) and a combination model. The xCELL algorithm was used to investigate the TME. A total of 212 ICI-treated melanoma patients were identified. All models achieved a high area under the curve (AUC) and bootstrap estimate (RFC7: 0.71, 0.74; RFC-Seq: 0.87, 0.75; RFC-Surv: 0.76, 0.76, respectively). Tumor mutation burden, GSTA3, and VNN2 were the highest contributing features. Tumor infiltration analyses revealed a direct correlation between upregulated genes and CD8+, CD4+ T cells, and B cells and inversely correlated with myeloid-derived suppressor cells. Our findings confirmed the accuracy of several genomic, clinical, and transcriptomic-based RFC models, that could further support the use of TMB in predicting response to ICIs. Novel genes (GSTA3 and VNN2) were identified through RFC-seq and RFC-surv models that could serve as genomic biomarkers after robust validation.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers14225605
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2527080-1
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