Survival prediction based on the gene expression associated with cancer morphology and microenvironment in primary central nervous system lymphoma

ACTR10 Overexpression Facilitates the Progression and Tyrosine Kinase Inhibitor Resistance in Hepatocellular Carcinoma

Background

In the present day, hepatocellular carcinoma (HCC) remains a formidable threat to human health. Actin-related protein 10 (ACTR10) is related to tyrosine kinase inhibitor (TKI) resistance. A comprehensive analysis of ACTR10 in HCC will further our understanding of the molecular mechanisms underlying this resistance phenomenon, shedding light on potential therapeutic strategies for combating TKI resistance in HCC.

Methods

We conducted an integration of high-throughput datasets across various centers, analyzing ACTR10 expression using the Cancer Cell Line Encyclopedia (CCLE) and assessing its implications through clustered regularly interspaced short palindromic repeats (CRISPR) knockout screen. Pathogenic mechanisms were elucidated through enrichment analysis. Prognostic assessment utilized Kaplan-Meier survival and univariate Cox analyses. An integrated analysis of gene expression profiles related to TKI in HCC was conducted, and TKI resistance mechanisms were explored through enrichment analysis. Potential therapeutic drugs were identified using the Drug Gene Budger database and molecular docking techniques.

Results

The standardized mean difference (SMD) of 0.34 (95% confidence interval (CI): 0.22 - 0.45, P < 0.05) and ACTR10-dependent growth in HCC cells confirm its upregulation in HCC. The area under the summary receiver operating characteristic (sROC) curve was 0.69, indicating moderate discriminative ability of ACTR10 in HCC patients. ACTR10 exerts its pro-cancer effect by influencing RNA splicing, mRNA processing and nucleocytoplasmic transport. A hazard ratio of 2.19 (95% CI: 1.56 - 3.08, P < 0.05) identifies ACTR10 as an independent prognostic risk factor. Additionally, the SMD of 0.88 (95% CI: 0.01 - 0.76, P < 0.05) validates ACTR10 as a TKI-resistance gene, mediating resistance via enhanced exocytosis, autophagy, and apoptosis in HCC patients. Trichostatin A emerges as a prospective targeted agent for HCC.

Conclusion

The upregulation of ACTR10 accelerates HCC progression, promotes TKI resistance, and emerges as a prospective target for the treatment of HCC.

Exploring transcriptomic databases: unraveling circadian gene disruptions in lower grade glioma

The study explored the role of circadian rhythm genes (CRGs) in lower grade glioma (LGG) development and found that certain genes, such as CRY1, NPAS2, and RORB, were associated with increased or decreased risk of LGG. The study also investigated the correlation between CRGs and immune cell infiltration, revealing a negative association with macrophage infiltration and a positive correlation with B cell and CD8 + T cell infiltration. Additionally, the study identified major mutated CRGs, including PER2, BMAL1, CLOCK, and BMAL2, and their potential interaction with other CNS-associated genes. The study suggests that CRGs play a crucial role in immune response and tumorigenesis in LGG patients and warrants further investigation.

Diagnostic potential of vitreoretinal lymphoma by detection of gene mutations with NGS in 25 Chinese patients

BACKGROUND

Vitreoretinal lymphoma (VRL) is a rare malignant lymphoproliferative tumor. Our study aimed to investigate the mutational profile of VRL distinguishing from uveitis using next-generation sequencing (NGS) analysis on small amounts of vitreous fluid.

METHODS

Vitreous samples from twenty-six eyes of twenty VRL patients and six eyes of five uveitis patients were enrolled. All vitreous samples underwent cytology, immunocytochemistry for B-cell markers, cytokines analysis of IL-10 and IL-6, and flow cytometry. NGS was performed in vitreous specimens from the 25 patients using 82 DLBCL-targeted mutation panels. Vitreous fluids from 8 cases were performed paired NGS-based mutation analysis on both cell-free DNA (cfDNA) and genomic DNA.

RESULTS

The sensitivity and accuracy rates for vitreous cytology were 70 % and 76 %, and for cytokine analysis (IL-10/IL-6 > 1) were 65 % and 72 %, respectively. Overall, the common mutations in VRL were PIM1 (88.5 %), IGLL5 (88.5 %), KMT2C (73 %), MYD88 (77 %), CD79B (50 %) and TBL1XR1 (46.2 %). In addition, the genetic mutation in cfDNA was consistent with that in genomic DNA in eight VRL cases.

CONCLUSIONS

The mutation analysis of 82 DLBCL-targeted spectrum mutation panels by NGS on the vitreous samples is a sensitive and specific tool for distinguishing VRL from uveitis. Utilizing cfDNA for NGS analysis may serve as a liquid biopsy to aid in the diagnosis of VRL, particularly when using small-volume aspirate.

Differentiation between primary central nervous system lymphomas and gliomas according to pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging

Purpose

It is difficult to differentiate between primary central nervous system lymphoma and primary glioblastoma due to their similar MRI findings. This study aimed to assess whether pharmacokinetic parameters derived from dynamic contrast-enhanced MRI could provide valuable insights for differentiation.

Methods

Seventeen cases of primary central nervous system lymphoma and twenty-one cases of glioblastoma as confirmed by pathology, were retrospectively analyzed. Pharmacokinetic parameters, including Ktrans, Kep, Ve, and the initial area under the Gd concentration curve, were measured from the enhancing tumor parenchyma, peritumoral parenchyma, and contralateral normal parenchyma. Statistical comparisons were made using Mann-Whitney U tests for Ve and Matrix Metallopeptidase-2, while independent samples t-tests were used to compare pharmacokinetic parameters in the mentioned regions and pathological indicators of enhancing tumor parenchyma, such as vascular endothelial growth factor and microvessel density. The pharmacokinetic parameters with statistical differences were evaluated using receiver-operating characteristics analysis. Except for the Wilcoxon rank sum test for Ve, the pharmacokinetic parameters were compared within the enhancing tumor parenchyma, peritumoral parenchyma, and contralateral normal parenchyma of the primary central nervous system lymphomas and glioblastomas using variance analysis and the least-significant difference method.

Results

Statistical differences were observed in Ktrans and Kep within the enhancing tumor parenchyma and in Kep within the peritumoral parenchyma between these two tumor types. Differences were also found in Matrix Metallopeptidase-2, vascular endothelial growth factor, and microvessel density within the enhancing tumor parenchyma of these tumors. When compared with the contralateral normal parenchyma, pharmacokinetic parameters within the peritumoral parenchyma and enhancing tumor parenchyma exhibited variations in glioblastoma and primary central nervous system lymphoma, respectively. Moreover, the receiver-operating characteristics analysis showed that the diagnostic efficiency of Kep in the peritumoral parenchyma was notably higher.

Conclusion

Pharmacokinetic parameters derived from dynamic contrast-enhanced MRI can differentiate primary central nervous system lymphoma and glioblastoma, especially Kep in the peritumoral parenchyma.

Machine learning-based pathomics signature of histology slides as a novel prognostic indicator in primary central nervous system lymphoma

PURPOSE

To develop and validate a pathomics signature for predicting the outcomes of Primary Central Nervous System Lymphoma (PCNSL).

METHODS

In this study, 132 whole-slide images (WSIs) of 114 patients with PCNSL were enrolled. Quantitative features of hematoxylin and eosin (H&E) stained slides were extracted using CellProfiler. A pathomics signature was established and validated. Cox regression analysis, receiver operating characteristic (ROC) curves, Calibration, decision curve analysis (DCA), and net reclassification improvement (NRI) were performed to assess the significance and performance.

RESULTS

In total, 802 features were extracted using a fully automated pipeline. Six machine-learning classifiers demonstrated high accuracy in distinguishing malignant neoplasms. The pathomics signature remained a significant factor of overall survival (OS) and progression-free survival (PFS) in the training cohort (OS: HR 7.423, p < 0.001; PFS: HR 2.143, p = 0.022) and independent validation cohort (OS: HR 4.204, p = 0.017; PFS: HR 3.243, p = 0.005). A significantly lower response rate to initial treatment was found in high Path-score group (19/35, 54.29%) as compared to patients in the low Path-score group (16/70, 22.86%; p < 0.001). The DCA and NRI analyses confirmed that the nomogram showed incremental performance compared with existing models. The ROC curve demonstrated a relatively sensitive and specific profile for the nomogram (1-, 2-, and 3-year AUC = 0.862, 0.932, and 0.927, respectively).

CONCLUSION

As a novel, non-invasive, and convenient approach, the newly developed pathomics signature is a powerful predictor of OS and PFS in PCNSL and might be a potential predictive indicator for therapeutic response.

Targeting the tumor microenvironment in primary central nervous system lymphoma: Implications for prognosis

Primary central nervous system lymphoma (PCNSL) is a rare extranodal non-Hodgkin lymphoma, and there is limited research on its tumor microenvironment (TME). Nevertheless, more and more studies have evidence that TME has essential effects on tumor cell proliferation, immune escape, and drug resistance. Thus, it is critical to elucidate the role of TME in PCNSL. The understanding of the PCNSL TME is gradually unfolding, including factors that distinguish it from systemic diffuse large B-cell lymphoma (DLBCL). The TME in PCNSL exhibits both transcriptional and spatial intratumor heterogeneity. Cellular interactions between tumor cells and stroma cells reveal immune evasion signaling. The comparative analysis between PCNSL and DLBCL suggests that PCNSL is more likely to be an immunologically deficient tumor. In PCNSL, T cell exhaustion and downregulation of macrophage immune function are accompanied by suppressive microenvironmental factors such as M2 polarized macrophages, endothelin B receptor, HLA depletion, PD-L1, and TIM-3. MMP-9, Integrin-β1, and ICAM-1/LFA-1 play crucial roles in transendothelial migration towards the CNS, while CXCL13/CXCR5, CD44, MAG, and IL-8 are essential for brain parenchymal invasion. Further, macrophages, YKL-40, CD31, CD105, PD-1/PD-L1 axis, osteopontin, galectin-3, aggregative perivascular tumor cells, and HLA deletion may contribute to poor outcomes in patients with PCNSL. This article reviews the effect of various components of TME on the progression and prognosis of PCNSL patients to identify novel therapeutic targets.

Revealing a Novel Methylated Integrin Alpha-8 Related to Extracellular Matrix and Anoikis Resistance Using Proteomic Analysis in the Immune Microenvironment of Lung Adenocarcinoma

Genomic epigenetics of extracellular matrix (ECM) play an important role in lung adenocarcinoma (LUAD). Our study identified a signature of potential prognostic genes associated with ECM and constructed immune risk-related prognosis model in LUAD. We downloaded mRNAs transcriptome data, miRNAs expression data, and clinical patient information for LUAD based on The Cancer Genome Atlas. "Limma, clusterProfiler, ggplot2" R packages and GSEA were used to analyze meaningful genes and explore potential biological function. A competing endogenous RNA network was constructed to reveal the mechanism of ECM-related genes. Combined with clinical LUAD patients' characteristics, univariate and multivariate Cox regression analyses were used to build prognostic immune risk model. Next, we calculated AUC value of ROC curve, and explored survival probability of different risk groups. A total of 2966 mRNAs were differently expressed in LUAD samples and normal samples. Function enrichment analyses proved mRNAs were associated with many tumor pathways, such as cell adhesion, vascular smooth muscle contraction, and cell cycle. There were 18 mRNAs related to ECM receptor signaling pathway, and 7 mRNAs expressions were correlated with EGFR expression, but only 5mRNAs were associated with the long-term prognosis. Based on Integrin alpha-8 (ITGA8) molecule, we identified potential 3 miRNAs from several databases. The promoter of ITGA8 was higher-methylated and lower-expressed in LUAD. And lower-expressed group has poor prognosis for patients. 66 immunomodulators related to ITGA8 were performed to construct immune correlation prediction model (p < 0.05). Comprehensive analyses of ITGA8 revealed it combined focal adhesion kinase to activate PI3K/AKT signaling pathway to influence the occurrence and development of LUAD. A novel immune prognostic model about ITGA8 was constructed and verified in LUAD patients. Combined with non-coding genes and genomic epigenetics, identification of potential biomarkers provided new light on therapeutic strategy for clinical patients.

Emerging Prognostic and Predictive Significance of Stress Keratin 17 in HPV-Associated and Non HPV-Associated Human Cancers: A Scoping Review

A growing body of literature suggests that the expression of cytokeratin 17 (K17) correlates with inferior clinical outcomes across various cancer types. In this scoping review, we aimed to review and map the available clinical evidence of the prognostic and predictive value of K17 in human cancers. PubMed, Web of Science, Embase (via Scopus), Cochrane Central Register of Controlled Trials, and Google Scholar were searched for studies of K17 expression in human cancers. Eligible studies were peer-reviewed, published in English, presented original data, and directly evaluated the association between K17 and clinical outcomes in human cancers. Of the 1705 studies identified in our search, 58 studies met criteria for inclusion. Studies assessed the prognostic significance (n = 54), predictive significance (n = 2), or both the prognostic and predictive significance (n = 2). Altogether, 11 studies (19.0%) investigated the clinical relevance of K17 in cancers with a known etiologic association to HPV; of those, 8 (13.8%) were focused on head and neck squamous cell carcinoma (HNSCC), and 3 (5.1%) were focused on cervical squamous cell carcinoma (SCC). To date, HNSCC, as well as triple-negative breast cancer (TNBC) and pancreatic cancer, were the most frequently studied cancer types. K17 had prognostic significance in 16/17 investigated cancer types and 43/56 studies. Our analysis suggests that K17 is a negative prognostic factor in the majority of studied cancer types, including HPV-associated types such as HNSCC and cervical cancer (13/17), and a positive prognostic factor in 2/17 studied cancer types (urothelial carcinoma of the upper urinary tract and breast cancer). In three out of four predictive studies, K17 was a negative predictive factor for chemotherapy and immune checkpoint blockade therapy response.

Identification of FAT4 as a positive prognostic biomarker in DLBCL by comprehensive genomic analysis

The molecular landscapes of diffuse large B-cell lymphoma (DLBCL) remained to be comprehensively investigated with an urgent need to identify novel prognostic biomarkers guiding prognostic stratification and disease monitoring. Baseline tumor samples of 148 DLBCL patients were analyzed using targeted next-generation sequencing (NGS) for mutational profiling, whose clinical reports were retrospectively reviewed. In this cohort, the subgroup of old DLBCL patients (age at diagnosis > 60, N = 80) exhibited significantly higher Eastern Cooperative Oncology Group scores and International Prognostic Index than their young counterparts (age at diagnosis ≤ 60, N = 68). As revealed by the NGS results, PIM1 (43.9%), KMT2D (31.8%), MYD88 (29.7%), and CD79B (27.0%) were identified as the most frequently mutated genes. Aberrations of genes of the immune escape pathway were significantly enriched in the young subgroup, while the altered epigenetic regulators were more abundant in the old patients. FAT4 mutation was identified as a positive prognostic biomarker, associated with longer progression-free survival and overall survival in the entire cohort and the old subgroup, using the Cox regression analyses. However, the prognostic function of FAT4 was not reproduced in the young subgroup. We comprehensively analyzed the pathological and molecular characteristics of old and young DLBCL patients and demonstrated the prognostic value of FAT4 mutation, which requires further validation with sizable cohorts in future research.

Prognostic impact of peripheral natural killer cells in primary central nervous system lymphoma

Background

Primary central nervous system lymphoma (PCNSL) is an aggressive extranodal non-Hodgkin lymphoma with a poor prognosis. We aimed to evaluate the prognostic impact of circulating NK cells in PCNSL.

Materials and methods

Patients diagnosed with PCNSL who were treated at our institution between December 2018 and December 2019 were retrospectively screened. Patient variables including age, sex, Karnofsky performance status, diagnostic methods, location of lesions, lactate dehydrogenase, cerebrospinal fluids (CSF), and vitreous fluids involvement or not were documented. NK cell count and NK cell proportion (NK cell count/lymphocyte count) in the peripheral blood were evaluated by flow cytometry. Some patients underwent two consecutive NK cell tests before and three weeks after chemotherapy (before the next chemotherapy). The fold change in NK cell proportion and NK cell counts were calculated. CD56-positive NK cells in tumor tissue were assessed by immunohistochemistry. NK cell cytotoxicity assay was performed using flow cytometry.

Results

A total of 161 patients with PCNSL were included in this study. The median NK cell count of all NK cell tests was 197.73/μL (range 13.11-1889.90 cells/μL). The median proportion of NK cells was 14.11% (range 1.68-45.15%) for all. Responders had a higher median NK cell count (p<0.0001) and NK cell proportion (p<0.0001) than non-responders. Furthermore, Responders had a higher median fold change in NK cell proportion than non-responders (p=0.019) or patients in complete remission/partial remission (p<0.0001). A higher median fold change in NK cell count was observed in responders than in non-responders (p=0.0224) or patients in complete remission/partial remission (p=0.0002). For newly diagnosed PCNSL, patients with a high NK cell count (>165 cells/μL) appeared to have a longer median overall survival than those with a low NK cell count (p=0.0054). A high fold change in the proportion of NK cells (>0.1957; p=0.0367) or NK cell count (>0.1045; p=0.0356) was associated with longer progression-free survival. Circulating NK cells from newly-diagnosed PCNSL demonstrated an impaired cytotoxicity capacity compared to those from patients with PCNSL in complete remission or healthy donors.

Conclusion

Our study indicated that circulating NK cells had some impact on the outcome of PCNSL.

Cerebrospinal fluid metabolic markers predict prognosis behavior of primary central nervous system lymphoma with high-dose methotrexate-based chemotherapeutic treatment

Background

Primary central nervous system lymphoma (PCNSL) is a highly aggressive non-Hodgkin's B-cell lymphoma which normally treated by high-dose methotrexate (HD-MTX)-based chemotherapy. However, such treatment cannot always guarantee a good prognosis (GP) outcome while suffering several side effects. Thus, biomarkers or biomarker-based models that can predict PCNSL patient prognosis would be beneficial.

Methods

We first collected 48 patients with PCNSL and applied HPLC-MS/MS-based metabolomic analysis on such retrospective PCNSL patient samples. We then selected the highly dysregulated metabolites to build a logical regression model that can distinguish the survival time length by a scoring standard. Finally, we validated the logical regression model on a 33-patient prospective PCNSL cohort.

Results

Six metabolic features were selected from the cerebrospinal fluid (CSF) that can form a logical regression model to distinguish the patients with relatively GP (Z score ≤0.06) from the discovery cohort. We applied the metabolic marker-based model to a prospective recruited PCNSL patient cohort for further validation, and the model preformed nicely on such a validation cohort (AUC = 0.745).

Conclusions

We developed a logical regression model based on metabolic markers in CSF that can effectively predict PCNSL patient prognosis before the HD-MTX-based chemotherapy treatments.