PPIH acts as a potential predictive biomarker for patients with common solid tumors.

BACKGROUND: Our previous studies have indicated that mRNA and protein levels of PPIH are significantly upregulated in Hepatocellular Carcinoma (LIHC) and could act as predictive biomarkers for patients with LIHC. Nonetheless, the expression and implications of PPIH in the etiology and progression of common solid tumors have yet to be explored, including its potential as a serum tumor marker. METHODS: We employed bioinformatics analyses, augmented with clinical sample evaluations, to investigate the mRNA and protein expression and gene regulation networks of PPIH in various solid tumors. We also assessed the association between PPIH expression and overall survival (OS) in cancer patients using Kaplan-Meier analysis with TCGA database information. Furthermore, we evaluated the feasibility and diagnostic efficacy of PPIH as a serum marker by integrating serological studies with established clinical tumor markers. RESULTS: Through pan-cancer analysis, we found that the expression levels of PPIH mRNA in multiple tumors were significantly different from those in normal tissues. This study is the first to report that PPIH mRNA and protein levels are markedly elevated in LIHC, Colon adenocarcinoma (COAD), and Breast cancer (BC), and are associated with a worse prognosis in these cancer patients. Conversely, serum PPIH levels are decreased in patients with these tumors (LIHC, COAD, BC, gastric cancer), and when combined with traditional tumor markers, offer enhanced sensitivity and specificity for diagnosis. CONCLUSION: Our findings propose that PPIH may serve as a valuable predictive biomarker in tumor patients, and its secreted protein could be a potential serum marker, providing insights into the role of PPIH in cancer development and progression.

Prospective of Mitochondrial DNA Variations in Cancer on Genomic Medicine.

Mitochondrial DNA (mtDNA) has emerged as a pivotal component in understanding the etiology and susceptibility of cancer. A recent study by Chen and colleagues delineated the germline genetic effect of mtDNA single-nucleotide polymorphisms (SNP) and haplogroups across pan-cancer risk. They identified a subset of mtSNPs and the corresponding risk score, as well as haplogroups A and M7 alongside their genetic interactions, conferring a protective effect against various cancers. These findings underscored the value of mtDNA variations as biomarkers for cancer etiology and as tools for cancer risk stratification. Future investigations are encouraged to integrate comprehensive omics data of genomics, transcriptomics, proteomics, and metabolomics, etc., from nuclear DNA with mtDNA variations, alongside single-cell and spatial technologies, to unravel the tumor mechanism and identify the drug targets. Moreover, the incorporation of polygenic risk score, that included mtDNA variations with both rare and common frequencies, and liquid biopsy-based biomarkers would enhance the predictive performance of cancer risk assessment and refine the risk stratification of population-based cancer screening. This commentary advocates for the validation across diverse populations to harness the full potential of mitochondrial genomics, and ultimately paves the prospective way for advancements in personalized cancer therapeutics and prevention strategies. See related article by Chen and colleagues, Cancer Epidemiol Biomarkers Prev 2024;33:381-8.

A Deep Learning Model for Cancer Type Prediction Sets a New Standard.

SUMMARY: Classifying tumor types using machine learning approaches is not always trivial, particularly for challenging cases such as cancers of unknown primary. In this issue of Cancer Discovery, Darmofal and colleagues describe a new tool that uses information from a clinical sequencing panel to diagnose tumor type, and show that the model is particularly robust. See related article by Darmofal et al., p. 1064 (1).

Cytotoxic and molecular differences of anticancer agents on 2D and 3D cell culture.

BACKGROUND: Cancer and multidrug resistance are regarded as concerns related to poor health outcomes. It was found that the monolayer of 2D cancer cell cultures lacks many important features compared to Multicellular Tumor Spheroids (MCTS) or 3D cell cultures which instead have the ability to mimic more closely the in vivo tumor microenvironment. This study aimed to produce 3D cell cultures from different cancer cell lines and to examine the cytotoxic activity of anticancer medications on both 2D and 3D systems, as well as to detect alterations in the expression of certain genes levels. METHOD: 3D cell culture was produced using 3D microtissue molds. The cytotoxic activities of colchicine, cisplatin, doxorubicin, and paclitaxel were tested on 2D and 3D cell culture systems obtained from different cell lines (A549, H1299, MCF-7, and DU-145). IC50 values were determined by MTT assay. In addition, gene expression levels of PIK3CA, AKT1, and PTEN were evaluated by qPCR. RESULTS: Similar cytotoxic activities were observed on both 3D and 2D cell cultures, however, higher concentrations of anticancer medications were needed for the 3D system. For instance, paclitaxel showed an IC50 of 6.234 µM and of 13.87 µM on 2D and 3D H1299 cell cultures, respectively. Gene expression of PIK3CA in H1299 cells also showed a higher fold change in 3D cell culture compared to 2D system upon treatment with doxorubicin. CONCLUSION: When compared to 2D cell cultures, the behavior of cells in the 3D system showed to be more resistant to anticancer treatments. Due to their shape, growth pattern, hypoxic core features, interaction between cells, biomarkers synthesis, and resistance to treatment penetration, the MCTS have the advantage of better simulating the in vivo tumor conditions. As a result, it is reasonable to conclude that 3D cell cultures may be a more promising model than the traditional 2D system, offering a better understanding of the in vivo molecular changes in response to different potential treatments and multidrug resistance development.

The scaffold protein disabled 2 (DAB2) and its role in tumor development and progression.

BACKGROUND: Disabled 2 (DAB2) is a multifunctional protein that has emerged as a critical component in the regulation of tumor growth. Its dysregulation is implicated in various types of cancer, underscoring its importance in understanding the molecular mechanisms underlying tumor development and progression. This review aims to unravel the intricate molecular mechanisms by which DAB2 exerts its tumor-suppressive functions within cancer signaling pathways. METHODS AND RESULTS: We conducted a comprehensive review of the literature focusing on the structure, expression, physiological functions, and tumor-suppressive roles of DAB2. We provide an overview of the structure, expression, and physiological functions of DAB2. Evidence supporting DAB2's role as a tumor suppressor is explored, highlighting its ability to inhibit cell proliferation, induce apoptosis, and modulate key signaling pathways involved in tumor suppression. The interaction between DAB2 and key oncogenes is examined, elucidating the interplay between DAB2 and oncogenic signaling pathways. We discuss the molecular mechanisms underlying DAB2-mediated tumor suppression, including its involvement in DNA damage response and repair, regulation of cell cycle progression and senescence, and modulation of epithelial-mesenchymal transition (EMT). The review explores the regulatory networks involving DAB2, covering post-translational modifications, interactions with other tumor suppressors, and integration within complex signaling networks. We also highlight the prognostic significance of DAB2 and its role in pre-clinical studies of tumor suppression. CONCLUSION: This review provides a comprehensive understanding of the molecular mechanisms by which DAB2 exerts its tumor-suppressive functions. It emphasizes the significance of DAB2 in cancer signaling pathways and its potential as a target for future therapeutic interventions.

OGA mutant aberrantly hydrolyzes O-GlcNAc modification from PDLIM7 to modulate p53 and cytoskeleton in promoting cancer cell malignancy.

O-GlcNAcase (OGA) is the only human enzyme that catalyzes the hydrolysis (deglycosylation) of O-linked beta-N-acetylglucosaminylation (O-GlcNAcylation) from numerous protein substrates. OGA has broad implications in many challenging diseases including cancer. However, its role in cell malignancy remains mostly unclear. Here, we report that a cancer-derived point mutation on the OGA's noncatalytic stalk domain aberrantly modulates OGA interactome and substrate deglycosylation toward a specific set of proteins. Interestingly, our quantitative proteomic studies uncovered that the OGA stalk domain mutant preferentially deglycosylated protein substrates with +2 proline in the sequence relative to the O-GlcNAcylation site. One of the most dysregulated substrates is PDZ and LIM domain protein 7 (PDLIM7), which is associated with the tumor suppressor p53. We found that the aberrantly deglycosylated PDLIM7 suppressed p53 gene expression and accelerated p53 protein degradation by promoting the complex formation with E3 ubiquitin ligase MDM2. Moreover, deglycosylated PDLIM7 significantly up-regulated the actin-rich membrane protrusions on the cell surface, augmenting the cancer cell motility and aggressiveness. These findings revealed an important but previously unappreciated role of OGA's stalk domain in protein substrate recognition and functional modulation during malignant cell progression.

Pan-cancer analysis of the TRAF family genes and their correlation with prognosis, TME, immune and drug sensitivity.

BACKGROUND: Tumor necrosis factor receptor-associated factors family genes play a pivotal role in tumorigenesis and metastasis, functioning as adapters or E3 ubiquitin ligases across various signaling pathways. To date, limited research has explored the association between tumor necrosis factor receptor-associated factors family genes and the clinicopathological characteristics of tumors, immunity, and the tumor microenvironment (TME). This comprehensive study investigates the relationship between tumor necrosis factor receptor-associated factors family and prognosis, TME, immune response, and drug sensitivity in a pan-cancer context. METHODS: Utilizing current public databases, this study examines the expression levels and prognostic significance of tumor necrosis factor receptor-associated factors family genes in a pan-cancer context through bioinformatic analysis. In addition, it investigates the correlation between tumor necrosis factor receptor-associated factors expression and various factors, including the TME, immune subtypes, stemness scores, and drug sensitivity in pan-cancer. RESULTS: Elevated expression levels of tumor necrosis factor receptor-associated factor 2, 3, 4, and 7 were observed across various cancer types. Patients exhibiting high expression of these genes generally faced a worse prognosis. Furthermore, a significant correlation was noted between the expression of tumor necrosis factor receptor-associated factors family genes and multiple dimensions of the TME, immune subtypes, and drug sensitivity.

A History of Cancer Research: Oncogenic Transcription Factors.

Transcription factors play crucial roles in cancer, and oncogenic counterparts of cellular transcription factors are present in a number of tumor viruses. It was studies in the early 1980s that first showed tumor viruses could encode nuclear as well as cytoplasmic oncoproteins. Subsequent work provided detailed insight into their mechanisms of action, as well as potential therapeutic avenues. In this excerpt from his forthcoming book on the history of cancer research, Joe Lipsick looks back at early work on nuclear oncogenes, including the discovery of MYC, MYB, FOS and JUN, Rel/NF-κB, and nuclear receptors such as the retinoic acid receptor and thyroid hormone receptor.

Identification and validation of biomarkers in membranous nephropathy and pan-cancer analysis.

Background: Membranous nephropathy (MN) is an autoimmune disease and represents the most prevalent type of renal pathology in adult patients afflicted with nephrotic syndrome. Despite substantial evidence suggesting a possible link between MN and cancer, the precise underlying mechanisms remain elusive. Methods: In this study, we acquired and integrated two MN datasets (comprising a single-cell dataset and a bulk RNA-seq dataset) from the Gene Expression Omnibus database for differential expression gene (DEG) analysis, hub genes were obtained by LASSO and random forest algorithms, the diagnostic ability of hub genes was assessed using ROC curves, and the degree of immune cell infiltration was evaluated using the ssGSEA function. Concurrently, we gathered pan-cancer-related genes from the TCGA and GTEx databases, to analyze the expression, mutation status, drug sensitivity and prognosis of hub genes in pan-cancer. Results: We conducted intersections between the set of 318 senescence-related genes and the 366 DEGs, resulting in the identification of 13 senescence-related DEGs. Afterwards, we meticulously analyzed these genes using the LASSO and random forest algorithms, which ultimately led to the discovery of six hub genes through intersection (PIK3R1, CCND1, TERF2IP, SLC25A4, CAPN2, and TXN). ROC curves suggest that these hub genes have good recognition of MN. After performing correlation analysis, examining immune infiltration, and conducting a comprehensive pan-cancer investigation, we validated these six hub genes through immunohistochemical analysis using human renal biopsy tissues. The pan-cancer analysis notably accentuates the robust association between these hub genes and the prognoses of individuals afflicted by diverse cancer types, further underscoring the importance of mutations within these hub genes across various cancers. Conclusion: This evidence indicates that these genes could potentially play a pivotal role as a critical link connecting MN and cancer. As a result, they may hold promise as valuable targets for intervention in cases of both MN and cancer.

Modification of the Hi-C Technology for Molecular Genetic Analysis of Formalin-Fixed Paraffin-Embedded Sections of Tumor Tissues.

Molecular genetic analysis of tumor tissues is the most important step towards understanding the mechanisms of cancer development; it is also necessary for the choice of targeted therapy. The Hi-C (high-throughput chromatin conformation capture) technology can be used to detect various types of genomic variants, including balanced chromosomal rearrangements, such as inversions and translocations. We propose a modification of the Hi-C method for the analysis of chromatin contacts in formalin-fixed paraffin-embedded (FFPE) sections of tumor tissues. The developed protocol allows to generate high-quality Hi-C data and detect all types of chromosomal rearrangements. We have analyzed various databases to compile a comprehensive list of translocations that hold clinical importance for the targeted therapy selection. The practical value of molecular genetic testing is its ability to influence the treatment strategies and to provide prognostic insights. Detecting specific chromosomal rearrangements can guide the choice of the targeted therapies, which is a critical aspect of personalized medicine in oncology.

Sample-Wise and Gene-Wise Comparisons Confirm a Greater Similarity of RNA and Protein Expression Data at the Level of Molecular Pathways and Suggest an Approach for the Data Quality Check in High-Throughput Expression Databases.

Identification of genes and molecular pathways with congruent profiles in the proteomic and transcriptomic datasets may result in the discovery of promising transcriptomic biomarkers that would be more relevant to phenotypic changes. In this study, we conducted comparative analysis of 943 paired RNA and proteomic profiles obtained for the same samples of seven human cancer types from The Cancer Genome Atlas (TCGA) and NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) [two major open human cancer proteomic and transcriptomic databases] that included 15,112 protein-coding genes and 1611 molecular pathways. Overall, our findings demonstrated statistically significant improvement of the congruence between RNA and proteomic profiles when performing analysis at the level of molecular pathways rather than at the level of individual gene products. Transition to the molecular pathway level of data analysis increased the correlation to 0.19-0.57 (Pearson) and 0.14-057 (Spearman), or 2-3-fold for some cancer types. Evaluating the gain of the correlation upon transition to the data analysis the pathway level can be used to refine the omics data by identifying outliers that can be excluded from the comparison of RNA and proteomic profiles. We suggest using sample- and gene-wise correlations for individual genes and molecular pathways as a measure of quality of RNA/protein paired molecular data. We also provide a database of human genes, molecular pathways, and samples related to the correlation between RNA and protein products to facilitate an exploration of new cancer transcriptomic biomarkers and molecular mechanisms at different levels of human gene expression.

Prevalence of Potentially Pathogenic Germline Variants Among Adult Patients in the Philippines With Solid Malignancies Who Underwent Tumor Genomic Profiling.

PURPOSE: In high-income countries, 2%-10% of tumor genomic profiling (TGP) reports reveal incidental pathogenic germline variants. A third of these patients would not qualify for genetic testing on the basis of current guidelines. Our study determined the prevalence of potentially pathogenic germline variants (PPGVs) in TGP results of adult patients with solid malignancies in the Philippines. METHODS: Annotated reports of patients with solid cancers who underwent TGP using FoundationOne or FoundationOne Heme between January 2021 and July 2023 were reviewed. PPGV criteria include having a variant allele frequency of >30% and were categorized as (1) high penetrance gene (HP), founder variant (FV), or variant associated with clinical presentation (VA). Pathogenicity was crosschecked through the ClinVar database. RESULTS: Of 446 patients, 13 PPGV variants were found in 50 (11.2%) patients at a median age of 60.5 years. Of them, 28 (56%) had HP (BRCA1, BRCA2, MSH2, MSH6, MLH1, RAD51C, RAD51D), 25 (50%) patients had VA (APC, SMAD4, CDH1, CDKN2A, PTEN), and two patients with lung cancer had a FV (EGFR p.Thr790Met). Six patients had more than one PPGV. PPGVs were primarily found in patients with colorectal (42% of 50 patients with PPGVs), breast (16%), ovarian (6%), and lung (6%) cancer (P < .001). HP genes were mostly found in female patients (71.4%; P = .03). CONCLUSION: With a PPGV prevalence of 11% in this study, it is important to recognize PPGVs as it can prompt genetic counseling and confirmatory germline testing.

Implementation of Universal Pan-Cancer Germline Genetic Testing in an Arab Population: The Jordanian Exploratory Cancer Genetics Study.

PURPOSE: Germline genetic testing (GGT) significantly affects cancer care. While universal testing has been studied in Western societies, less is known about adoption elsewhere. MATERIALS AND METHODS: In this study, 3,319 unselected, pan-cancer Jordanian patients diagnosed between April 2021 and September 2022 received GGT. Pathogenic germline variant (PGV) frequency among patients who were in-criteria (IC) or out-of-criteria (OOC; 2020 National Comprehensive Cancer Network criteria) and changes in clinical management in response to GGT results were evaluated. Statistical analysis was performed using two-tailed Fisher's exact test with significance level P < .05. RESULTS: The cohort was predominantly female (69.9%), with a mean age of 53.7 years at testing, and 53.1% were IC. While patients who were IC were more likely than patients who were OOC to have a PGV (15.8% v 9.6%; P < .0001), 149 (34.8%) patients with PGVs were OOC. Clinical management recommendations in response to GGT, including changes to treatment and/or follow-up, were made for 57.3% (161 of 281) of patients with high- or moderate-risk PGVs, including 26.1% (42 of 161) of patients who were OOC. CONCLUSION: Universal GGT of patients with newly diagnosed cancer was successfully implemented in Jordan and led to identification of actionable PGVs that would have been missed with guidelines-based testing.

LKB1 biology: assessing the therapeutic relevancy of LKB1 inhibitors.

Liver Kinase B1 (LKB1), encoded by Serine-Threonine Kinase 11 (STK11), is a master kinase that regulates cell migration, polarity, proliferation, and metabolism through downstream adenosine monophosphate-activated protein kinase (AMPK) and AMPK-related kinase signalling. Since genetic screens identified STK11 mutations in Peutz-Jeghers Syndrome, STK11 mutants have been implicated in tumourigenesis labelling it as a tumour suppressor. In support of this, several compounds reduce tumour burden through upregulating LKB1 signalling, and LKB1-AMPK agonists are cytotoxic to tumour cells. However, in certain contexts, its role in cancer is paradoxical as LKB1 promotes tumour cell survival by mediating resistance against metabolic and oxidative stressors. LKB1 deficiency has also enhanced the selectivity and cytotoxicity of several cancer therapies. Taken together, there is a need to develop LKB1-specific pharmacological compounds, but prior to developing LKB1 inhibitors, further work is needed to understand LKB1 activity and regulation. However, investigating LKB1 activity is strenuous as cell/tissue type, mutations to the LKB1 signalling pathway, STE-20-related kinase adaptor protein (STRAD) binding, Mouse protein 25-STRAD binding, splicing variants, nucleocytoplasmic shuttling, post-translational modifications, and kinase conformation impact the functional status of LKB1. For these reasons, guidelines to standardize experimental strategies to study LKB1 activity, associate proteins, spliced isoforms, post-translational modifications, and regulation are of upmost importance to the development of LKB1-specific therapies. Therefore, to assess the therapeutic relevancy of LKB1 inhibitors, this review summarizes the importance of LKB1 in cell physiology, highlights contributors to LKB1 activation, and outlines the benefits and risks associated with targeting LKB1.

Exploring potential circRNA biomarkers for cancers based on double-line heterogeneous graph representation learning.

BACKGROUND: Compared with the time-consuming and labor-intensive for biological validation in vitro or in vivo, the computational models can provide high-quality and purposeful candidates in an instant. Existing computational models face limitations in effectively utilizing sparse local structural information for accurate predictions in circRNA-disease associations. This study addresses this challenge with a proposed method, CDA-DGRL (Prediction of CircRNA-Disease Association based on Double-line Graph Representation Learning), which employs a deep learning framework leveraging graph networks and a dual-line representation model integrating graph node features. METHOD: CDA-DGRL comprises several key steps: initially, the integration of diverse biological information to compute integrated similarities among circRNAs and diseases, leading to the construction of a heterogeneous network specific to circRNA-disease associations. Subsequently, circRNA and disease node features are derived using sparse autoencoders. Thirdly, a graph convolutional neural network is employed to capture the local graph network structure by inputting the circRNA-disease heterogeneous network alongside node features. Fourthly, the utilization of node2vec facilitates depth-first sampling of the circRNA-disease heterogeneous network to grasp the global graph network structure, addressing issues associated with sparse raw data. Finally, the fusion of local and global graph network structures is inputted into an extra trees classifier to identify potential circRNA-disease associations. RESULTS: The results, obtained through a rigorous five-fold cross-validation on the circR2Disease dataset, demonstrate the superiority of CDA-DGRL with an AUC value of 0.9866 and an AUPR value of 0.9897 compared to existing state-of-the-art models. Notably, the hyper-random tree classifier employed in this model outperforms other machine learning classifiers. CONCLUSION: Thus, CDA-DGRL stands as a promising methodology for reliably identifying circRNA-disease associations, offering potential avenues to alleviate the necessity for extensive traditional biological experiments. The source code and data for this study are available at https://github.com/zywait/CDA-DGRL .

Effects of super-enhancers in cancer metastasis: mechanisms and therapeutic targets.

Metastasis remains the principal cause of cancer-related lethality despite advancements in cancer treatment. Dysfunctional epigenetic alterations are crucial in the metastatic cascade. Among these, super-enhancers (SEs), emerging as new epigenetic regulators, consist of large clusters of regulatory elements that drive the high-level expression of genes essential for the oncogenic process, upon which cancer cells develop a profound dependency. These SE-driven oncogenes play an important role in regulating various facets of metastasis, including the promotion of tumor proliferation in primary and distal metastatic organs, facilitating cellular migration and invasion into the vasculature, triggering epithelial-mesenchymal transition, enhancing cancer stem cell-like properties, circumventing immune detection, and adapting to the heterogeneity of metastatic niches. This heavy reliance on SE-mediated transcription delineates a vulnerable target for therapeutic intervention in cancer cells. In this article, we review current insights into the characteristics, identification methodologies, formation, and activation mechanisms of SEs. We also elaborate the oncogenic roles and regulatory functions of SEs in the context of cancer metastasis. Ultimately, we discuss the potential of SEs as novel therapeutic targets and their implications in clinical oncology, offering insights into future directions for innovative cancer treatment strategies.

TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology.

Cancer is a complex disease composing systemic alterations in multiple scales. In this study, we develop the Tumor Multi-Omics pre-trained Network (TMO-Net) that integrates multi-omics pan-cancer datasets for model pre-training, facilitating cross-omics interactions and enabling joint representation learning and incomplete omics inference. This model enhances multi-omics sample representation and empowers various downstream oncology tasks with incomplete multi-omics datasets. By employing interpretable learning, we characterize the contributions of distinct omics features to clinical outcomes. The TMO-Net model serves as a versatile framework for cross-modal multi-omics learning in oncology, paving the way for tumor omics-specific foundation models.

MONET: a database for prediction of neoantigens derived from microsatellite loci.

Background: Microsatellite instability (MSI) secondary to mismatch repair (MMR) deficiency is characterized by insertions and deletions (indels) in short DNA sequences across the genome. These indels can generate neoantigens, which are ideal targets for precision immune interception. However, current neoantigen databases lack information on neoantigens arising from coding microsatellites. To address this gap, we introduce The MicrOsatellite Neoantigen Discovery Tool (MONET). Method: MONET identifies potential mutated tumor-specific neoantigens (neoAgs) by predicting frameshift mutations in coding microsatellite sequences of the human genome. Then MONET annotates these neoAgs with key features such as binding affinity, stability, expression, frequency, and potential pathogenicity using established algorithms, tools, and public databases. A user-friendly web interface (https://monet.mdanderson.org/) facilitates access to these predictions. Results: MONET predicts over 4 million and 15 million Class I and Class II potential frameshift neoAgs, respectively. Compared to existing databases, MONET demonstrates superior coverage (>85% vs. <25%) using a set of experimentally validated neoAgs. Conclusion: MONET is a freely available, user-friendly web tool that leverages publicly available resources to identify neoAgs derived from microsatellite loci. This systems biology approach empowers researchers in the field of precision immune interception.