Diagnosis Of Human Cancer Research Articles

Persistent autonomous molecular motion of DNA walker along a single-molecule nano-track for intracellular MicroRNA imaging

While the intracellular imaging of miRNA biomarkers is of significant importance for the diagnosis and treatment of human cancers, DNA assembled nanoprobe has recently attracted considerable attention for imaging intracellular biomolecules. However, the complex construction process, intrinsic vulnerability to nuclease degradation and the limited signal transduction efficiency hamper its widespread application. In this contribution, based on persistent autonomous molecular motion of DNAzyme walker along a nano-substrate track, a DNA nanosphere probe (PNLD) is developed for the sensitive intracellular miR-21 imaging. Specifically, DNA nanosphere (called PN, single-molecule nano-track) is assembled from only one palindromic substrate, into which the locking strand-silenced DNAzymes (LD) are installed in a controlled manner. PNLD (made of PN and LD) can protect all DNA components against nuclease attack and maintain its structural integrity in serum solution over 24 h. Upon the activation by target miRNA, DNAzyme walker can move on the substrate scattered within PNLD (or on the surface) and between different PNLD objects and cleave many DNA substrates, generating an amplified signal. As a result, miR-21 can be detected down to 6.83 pM without the detectable interference from co-existing nontarget miRNAs. Moreover, PNLD system can accurately screen the different expression levels of miR-21 within the same type of cells and different types of cells, which is consistent with gold standard polymerase chain reaction (PCR) assay. Via changing the target recognition sequence, the PNLD system can be suitable for the intracellular imaging of miR-155, exhibiting the desirable universality. In addition, the DNAzyme walker-based PNLD system can be used to distinguish cancer cells from healthy cells, implying the potential application in cancer diagnosis and prognosis.

Automatic Diagnosis of Lung Diseases (Pneumonia, Cancer) with given Reliabilities on the Basis of an Irradiation Images of Patients

The article proposes algorithms for the automatic diagnosis of human lung diseases pneumonia and cancer, based on images obtained by radiation irradiation, which allow us to make decisions with the necessary reliability, that is, to restrict the probabilities of making possible errors to a pre-planned level. Since the information obtained from the observation is random, Wald’s sequential analysis method and Constrained Bayesian Method (CBM) of statistical hypothesis testing are used for making a decision, which allow us to restrict both types of possible errors. Both methods have been investigated using statistical simulation and real data, which fully confirmed the correctness of theoretical reasoning and the ability to make decisions with the required reliability using artificial intelligence. The advantage of CBM compared to Wald’s method is shown, which is expressed in the relative scarcity of observation results needed to make a decision with the same reliability. The possibility of implementing the proposed method in modern computerized X-ray equipment due to its simplicity and promptness of decision-making is also shown.

Intracellular nonenzymatic in situ growth of layered nanosheet DNA architectures based on palindrome-chained dumbbell probes for miRNA imaging

MicroRNA (miRNA) represents a class of important potential biomarkers, and their intracellular imaging is extremely useful for fundamental research and early diagnosis of human cancers. Hybridization chain reaction (HCR) has been shown to be effective in detecting miRNA in living cells. However, its practical applications are still hampered by inefficient reaction kinetics and poor biological stability under complex intracellular conditions. To address these issues, we report a palindrome-mediated multiple hybridization chain reaction (P-HCR) system to better visualize intracellular miRNAs. In the presence of the target miRNA, a layered nanosheet DNA architecture (LSDA) can be assembled in situ via the palindrome-mediated multiple HCR process. We demonstrate that the biological stability of this reaction system could be significantly improved by designing the probes to dumbbell-shaped structures and the distance of hairpins was effectively decreased due to palindrome-chained effect. Consequently, miRNA can be quantitatively identified even at extremely low concentrations of 4.7 pM. The P-HCR system can effectively differentiate the expression levels of miRNA in different tumor cells and normal cells, as demonstrated in live cell tests and the results were in agreement with the PCR, which is considered the gold standard. The new (P-HCR) system has the potential to revolutionize miRNA imaging in living cells.

Abstract 114: Identification of the actionable target, LILRB4, through genetic linkage analysis of Diversity Outbred (DO) F1 mice expressing HER2(neu)

Abstract Our goal is to identify, in situ, the regulatory genes that dictate the onset age and growth rate of spontaneously arising tumors to implement new intervention strategies. For genetic linkage analysis, we crossed HER2(neu) Tg mice with Diversity Outbred (DO) mice, an outbred population comprised of 8 founding strains (A/J, C57BL/6J, 129S1/SvlmJ, NOD/ShiLtJ, NZO/HILtJ, CAST/EiJ, PWK/PhJ, WSB/EiJ) maintained by non-sibling crossing to ensure each mouse is genetically unique. Using R/QTL package, we linked Quantitative Trait Loci (QTL) in Chr 1 and X with tumor onset age, and a Chr 10 QTL with tumor growth rate. The Chr1 (=human Chr2) QTL was linked to human breast cancer diagnosis in 11 Genome-Wide Association Studies (GWAS; the human GWAS catalog, https://www.ebi.ac.uk/gwas/). Because human GWAS data is deficient in Chr X mapping and also does not assess tumor growth rate as a trait, the QTL loci identified in mouse Chr X and 10 were discoveries beyond the capacity of human GWAS. In total, we identified 26 candidate genes across the 3 QTL. For clinical validation, the genes were analyzed with CodeAI (Caris LS) which associated cancer patient clinical data with candidate gene expression. We found 21/26 genes significantly associated with the survival of patients with primary (n=3,533) and/or metastatic (n=4,870) breast cancers and 17/26 were associated with lung cancer (n=11,334) survival, with 13 overlapping genes between lung and breast cancer, indicating broad applicability of these candidate genes. We interrogated the immune status in BALB NeuT and (BALBxPWK)F1 NeuT mice because the F1 mice developed tumors around 9 wk, much earlier than the 14 wk observed in BALB NeuT mice. Surprisingly, (BALBxPWK)F1 mice responded to cancer vaccines more vigorously. Single cell RNA (scRNA) libraries from (BALBxPWK)F1 NeuT tumors showed an expanded lymphocyte cluster (12%), compared with BALB NeuT (2%) and an enrichment of T cell activation genes. These findings may reflect a deficiency in tumor cell recognition by the activated anti-tumor effectors and a window of opportunity for correction. Of the 21 candidate genes, LILRB4, a myeloid cell checkpoint molecule, was of particular interest. Expression of LILRB4 is predictive of patient survival in both breast and lung cancer. Using scRNA sequencing, we found this transmembrane receptor expressed primarily by tumor infiltrating myeloid cells. In a reported scRNA sequencing dataset (GSE161529), LILRB4 was expressed by infiltrating macrophages in human breast cancers, and is also correlated with reduced survival. In a PAN Cancer ATACseq dataset (Corces et al., 2018), promoter accessibility of LILRB4 was associated with reduced survival. Taken together, there are multiple immune regulatory mechanisms of tumor progression and LILRB4 is an important new actionable target. CA76340 (WZW), KCI TBM program (JBJ) and CCSG P30 CA022453 (GB). Citation Format: Jennifer B. Jacob, Wei-Zen Wei, Benjamin L. Kidder, Tolulope Adeyelu, Andrew Elliott, Gerold Bepler, Joyce D. Reyes. Identification of the actionable target, LILRB4, through genetic linkage analysis of Diversity Outbred (DO) F1 mice expressing HER2(neu) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 114.

Lipid Isobaric Mass Tagging for Enhanced Relative Quantification of Unsaturated sn-Positional Isomers.

Changes in the levels of lipid sn-positional isomers are associated with perturbation of the physiological environment within the biological system. Consequently, knowing the concentrations of these lipids holds significant importance for unraveling their involvement in disease diagnosis and pathological mechanisms. However, existing methods for lipid quantification often fall short in accuracy due to the structural diversity and isomeric forms of lipids. To address this challenge, we have developed an aziridine-based isobaric tag labeling strategy that allows (i) differentiation and (ii) enhanced relative quantification of lipid sn-positional isomers from distinct samples in a single run. The methodology enabled by aziridination, isobaric tag labeling, and lithiation has been applied to various phospholipids, enabling the determination of the sn-positions of fatty acyl chains and enhanced relative quantification. The analysis of Escherichia coli lipid extracts demonstrated the enhanced determination of the concentration ratios of lipid isomers by measuring the intensity ratios of mass reporters released from sn-positional diagnostic ions. Moreover, we applied the method to the analysis of human colon cancer plasma. Intriguingly, 17 PC lipid sn-positional isomers were identified and quantified simultaneously, and among them, 7 showed significant abundance changes in the colon cancer plasma, which can be used as potential plasma markers for diagnosis of human colon cancer.

Multi‑dimensional analysis reveals NCKAP5L is a promising biomarker for the diagnosis and prognosis of human cancers, especially colorectal cancer.

The Nck-associated protein 5-like (NCKAP5L) gene, also known as Cep169, is associated with certain cancers. However, the diagnosis and prognosis value of NCKAP5L in several types of human cancer, including colorectal cancer, is not fully understood. In the present study, a comprehensive pan-cancer analysis of NCKAP5L was performed using several approaches, including gene expression and alteration, protein phosphorylation, immune infiltration, survival prognosis analyses and gene enrichment using the following: The University of California Santa Cruz Genome Browser Human Dec. 2013 (GRCh38/hg38) Assembly, Tumor Immune Estimation Resource (version 2), Human Protein Atlas, Gene Expression Profiling Interactive Analysis (version 2), University of Alabama at Birmingham Cancer Data Analysis portal, the Kaplan-Meier Plotter, cBioportal, Search Tool for the Retrieval of Interacting Genes/Proteins, Jvenn and the Metascape server. The role of NCKAP5L in colorectal cancer was further assessed by reverse transcription-quantitative PCR. The results demonstrated that NCKAP5L was upregulated in the majority of cancer types, including colorectal cancer. The high expression of NCKAP5L was significantly correlated with patient survival prognosis and immune infiltration of cancer-associated fibroblasts in numerous types of cancer, including colorectal cancer. Furthermore, Gene Ontology analysis identified that NCKAP5L may serve an important role in metabolic and cellular processes in human cancers. In summary, the data from the present study demonstrate that NCKAP5L is a potential tumor biomarker for the diagnosis and prognosis of human cancers, especially colorectal cancer.

The Diagnostic Value of Stool-Based MicroRNA-135b in the Early Detection of Colorectal Cancer and the Potent Neuro Function of MiR-146a

Background: Detecting microRNA (miRNA) in stool is a non-invasive approach for colorectal cancer (CRC) screening. This study aims to assess the diagnostic performance of stool-based microRNA-135b in detection of colorectal cancer and the role of miR-146a. Methods: To identify eligible studies that are well suited for diagnostic value of miR-135b in stool of colorectal cancer patients, we have evaluated 3 papers after systematic literature search of public database. The sensitivity and specificity were used to plot the summary receiver operator characteristic (SROC) curve and calculate the area under the SROC curve (AUC). The between-study heterogeneity was evaluated by Q test and I2 statistics. Importantly, we have also identified microRNA (miRNA) gene expression patterns by expression profiling. Moreover, we execute the pathway analysis to find the function of miR-146a. Results: A total of 3 studies from 19 articles were included for the meta-analysis according to the inclusion criteria. The overall analysis showed that microRNA-135b has a relatively good diagnostic performance in colorectal cancer, with a sensitivity of 0.685, a specificity of 0.813 and a partial AUC of 0.658. In stool samples, level of microRNA-135b was 2.21 fold change higher in subjects with CRC (P < 0.0001). Moreover, our study proved that miR-146a involved in the neuro and immune function. Interpretation: In conclusion, stool-based microRNA-135b shows a moderate level of overall diagnostic accuracy in diagnosis of CRC, which may present as auxiliary means for the diagnosis of colorectal cancer compare with colonoscopy. Further large-scale prospective studies are necessary to validate their potential applicability (miR-135b and miR-146a) combined with FBT methods in human cancer diagnosis.

A golgi-targeting and polarity-specific fluorescent probe for the diagnosis of cancer and fatty liver in living cells and tissues

Elucidating the intrinsic relationship between diseases and Golgi apparatus polarity remains a great challenge owing to the lack of the Golgi-specific fluorescent probe for polarity. Until now, the visualization of abnormal Golgi apparatus polarity in clinical cancer patient samples has not been achieved. To meet this urgent challenge, we facilely synthesized a robust Golgi-targeting and polarity-specific fluorescent probe (GCSP), which consists of an electron-acceptor solvatochromic coumarin 343 and an electron-donor Golgi-targeting group phenylsulfonamide. Owing to the typical D-π-A molecular configuration with unique intramolecular charge transfer effect, GCSP exhibits high sensitivity to polarity change in different solvents. Moreover, we revealed that GCSP possessed a satisfactory ability to sensitively monitor Golgi apparatus polarity changes in living cells. Using GCSP, we have successfully shown that Golgi apparatus polarity may serve as an ubiquitous marker for cancer and fatty liver detection. Surprisingly, the visualization of Golgi polarity has been achieved not only at the cellular levels, but also in clinical tissue samples from cancer patients, thus holding great potential in the clinical diagnosis of human cancer. All these features render GCSP an effective tool for the accurate diagnosis of Golgi apparatus related diseases.

An in situ fluorescent copolymer dots-based kit for the specific detection of monoamine oxidase-A in cell/tissue/human prostate cancer

Currently, the development of simple, fast, low-cost, and environmentally friendly MAO-A specific detection methods remains a huge challenge. Herein, a simple in situ fluorescent copolymer dots (FCDs)-based kit is constructed for the specific detection of monoamine oxidase-A in cell, tissue and human prostate cancer. The developed kit is depended on the high reaction activity between epinephrine and polyethyleneimine to form FCDs and the selective catalyzing activity of MAO-A on epinephrine. The gained FCDs are identified and characterized by high-resolution TEM, absorption and fluorescence spectrum. The present kit achieves highly sensitive detection of MAO-A with a limit of detection (LOD) of 20 ng/mL as well as screening of inhibitor with an IC50 (the concentration responding to 50% inhibition) of 1.071 μM by using clorgyline as a model. The MAO-A and MAO-B as the two isoforms of MAO are discriminated successfully, demonstrating the excellent selectivity of our kit. Noteworthy, the proposed method is applicable for measuring MAO-A not only from serum samples, but also for endogenous MAO-A in cells, mouse tissues and human prostate tumors with satisfied results. The proposed kit will be a great potential tool for human prostate cancer (PCa) diagnosis and the development of anti-PCa drug screening.

Abstract PO-037: Biomarkers for early diagnosis of human papillomavirus-related oropharyngeal cancer

Abstract PO-037: Biomarkers for early diagnosis of human papillomavirus-related oropharyngeal cancer

Fiber Laser-Based Lasso-Shaped Biosensor for High Precision Detection of Cancer Biomarker-CEACAM5 in Serum.

Detection of trace tumor markers in blood/serum is essential for the early screening and prognosis of cancer diseases, which requires high sensitivity and specificity of the assays and biosensors. A variety of label-free optical fiber-based biosensors has been developed and yielded great opportunities for Point-of-Care Testing (POCT) of cancer biomarkers. The fiber biosensor, however, suffers from a compromise between the responsivity and stability of the sensing signal, which would deteriorate the sensing performance. In addition, the sophistication of sensor preparation hinders the reproduction and scale-up fabrication. To address these issues, in this study, a straightforward lasso-shaped fiber laser biosensor was proposed for the specific determination of carcinoembryonic antigen (CEA)-related cell adhesion molecules 5 (CEACAM5) protein in serum. Due to the ultra-narrow linewidth of the laser, a very small variation of lasing signal caused by biomolecular bonding can be clearly distinguished via high-resolution spectral analysis. The limit of detection (LOD) of the proposed biosensor could reach 9.6 ng/mL according to the buffer test. The sensing capability was further validated by a human serum-based cancer diagnosis trial, enabling great potential for clinical use. The high reproduction of fabrication allowed the mass production of the sensor and extended its utility to a broader biosensing field.

Supramolecular fluorescent probe based on acyclic cucurbituril for detection of cancer Labels in human urine.

Spermine (Spm) and spermidine (Spmd) are considered as potential biomarker for early diagnosis of human cancer. Herein, a novel acyclic cucurbituril derivative (UL-ACB) was firstly designed and synthesized, which fluoresces at 460nm after excitation at 365nm. UL-ACB is rich in oxygen atoms which are capable of forming coordinate bonds with copper (Cu2+) that cause quenching of UL-ACB fluorescence. Moreover, the addition of biological endogenous substances Spm and Spmd can turn on fluorescence of UL-ACB. Interestingly, the probe showed a remarkable detection efficiency for Spm and Spmd in human urine (the detection limits of Spm and Spmd were 0.156μM and 0.762μM, and the linear ranges are 0.156∼43.06μM and 0.762∼29.10μM), which completely covered the early diagnosis of urinary Spm (1∼10μM) and urine Spmd (1∼20μM) required concentration range in cancer patients. The probe for Spm and Spmd is simple, time-saving and selective, which may provide a new promising strategy for early cancer diagnosis.

Molecular classification of cancer site of origin and lineage.

3116 Background: Histopathologic assessment has been the primary modality for the diagnosis of human cancers since the 19th century, and to this day remains the mainstay of diagnosis, risk stratification and staging. While the field has made significant advances, the “art of pathology” relies heavily on subjective visual inspection, with significant levels of inter-observer variability and sometimes uncertainty in diagnosis. Advances in next-generation sequencing have ushered in new molecular diagnostic frameworks that can improve accuracy, critical in deciding treatment. Methods: We utilized machine learning (XGBoost) and developed comprehensive molecular classifiers for cancer site of origin (22 classes i.e. breast, prostate, lung, etc.) and cancer lineage (8 classes i.e. adenocarcinoma, squamous cell carcinoma, etc.) to augment traditional histopathologic assessment. These models were trained on a total of 8,249 tumor samples. We then evaluated performance using a large independent validation cohort consisting of 10,376 samples. While 8,886 of these were primary tumors from 97 different datasets, uniquely we also assessed performance in 1,490 metastatic tumors from 17 datasets. Pathologic diagnosis of metastatic tumors can be more difficult due to de-differentiation, but metastatic samples for molecular profiling are difficult to obtain and were only rarely included in previous efforts. Results: After model training, we locked the site of origin and lineage expression-based models and next evaluated performance on our independent validation cohort. Overall accuracy was 92.5% for cancer site of origin and 97.2% for cancer lineage on validation. Accuracy among primary site samples was higher than for metastatic samples (93.4%, and 86.8% for cancer site of origin, respectively; 97.5% and 95.7% for cancer lineage, respectively). However, accuracy jumped to an astounding 98-99% for both site of origin and lineage in both primary and metastatic samples where the signatures were highly confident, encompassing the majority of cases. Our unique approach in evaluating site of origin and lineage separately allowed us to identify a lineage differentiation score that was associated with small cell lung and neuroendocrine prostate tumors (AUC 0.955 and 0.833, respectively), as well as worse survival across most evaluable tumor types. Conclusions: To our knowledge, this is the largest and most comprehensive validation of platform-independent site of origin/lineage classifiers to date. Our approach can be applied to any existing research or commercial RNA-seq assay, and provides an objective and quantifiable confidence measurement that is correlated with accuracy. This allows for nuanced interpretation of high vs. low confidence predictions, which can complement and potentially even help guide traditional histopathologic assessment in cancer research, clinical trial design, and ultimately clinical practice.

An integrated omics study on the role of HDAC9 gene in the oncogenic events of human gastrointestinal-tract associated cancers

Esophageal, pancreatic, and colorectal cancers are considered as the main cancers associated with the gastrointestinal-tract taking a huge toll on human health. Exploring new cancer-critical genes for early diagnosis and treatment is of utmost importance for adopting an appropriate therapeutic strategy. Histone deacetylase 9 (HDAC9) is a class II histone-deacetylase that removes acetyl groups from lysine residues on the N-terminus of histones to repress transcription and mediates critical cellular processes including regulation of transcription and cell cycle. Herein, we present a comprehensive omics-based exploration of the oncogenomic relevance of HDAC9 in the neoplastic transformation events of human GI tract-associated cancers. Differential expression and oncogenomics of HDAC9 in various cancer forms, effect of HDAC9 mutation on survivability, association of different signaling pathways, immune infiltration, and protein-protein interaction have been deciphered through bioinformatics approaches. This bioinformatics study collectively revealed that abundance of HDAC9 mRNA is higher in esophageal and pancreatic cancer while lower in colorectal cancer. Moreover, transcriptional expression of HDAC9 was found to be negatively correlated with the survival of patients with pancreatic and colorectal cancer. Most of the subjects included in the study with perturbation of HDAC9 gene expression were noted to possess overactivation of vital signaling pathways, including TGF-β, RTK-RAS, Wnt, and p53 signaling pathways associated with oncogenesis. Taken together, this study proposes HDAC9 as a potential cancer-critical gene that could be considered a biomarker for the diagnosis and therapeutic intervention of human GI tract cancer.

Alternative CDC20 translational isoforms tune mitotic arrest duration.

Mitotic defects activate the spindle-assembly checkpoint, which inhibits the anaphase-promoting complex co-activator CDC20 to induce a prolonged cell cycle arrest1,2. Once errors are corrected, the spindle-assembly checkpoint is silenced, allowing anaphase onset to occur. However, in the presence of persistent unresolvable errors, cells can undergo 'mitotic slippage', exiting mitosis into a tetraploid G1 state and escaping the cell death that results from a prolonged arrest. The molecular logic that enables cells to balance these duelling mitotic arrest and slippage behaviours remains unclear. Here we demonstrate that human cells modulate the duration of their mitotic arrest through the presence of conserved, alternative CDC20 translational isoforms. Downstream translation initiation results in a truncated CDC20 isoform that is resistant to spindle-assembly-checkpoint-mediated inhibition and promotes mitotic exit even in the presence of mitotic perturbations. Our study supports a model in which the relative levels of CDC20 translational isoforms control the duration of mitotic arrest. During a prolonged mitotic arrest, new protein synthesis and differential CDC20 isoform turnover create a timer, with mitotic exit occurring once the truncated Met43 isoform achieves sufficient levels. Targeted molecular changes or naturally occurring cancer mutations that alter CDC20 isoform ratios or its translational control modulate mitotic arrest duration and anti-mitotic drug sensitivity, with potential implications for the diagnosis and treatment of human cancers.

Abstract LB077: Analysis of indel and structural variant error profiles in deep next generation sequencing data

Abstract Background: Accurate detection of low frequency mutations is of critical importance in the study of genetic heterogeneity, such as on the detection of minimal residual diseases for leukemias. Our prior work has resulted in successful error suppression for substitutions (10−4-10−5). However, the error profiles of indels and structural variants (SVs) remain elusive. Results: In this work, we generated ultra-deep sequencing data using our previously established dilution models (COLO829) on known somatic indels (n=23) and SVs (n=17). We discovered that the error rate of indels (10−6) and SVs (<2 × 10−7) are 100- to >1000-fold lower than that of SNVs. This finding was fully recapitulated in our analysis of 347 indels and 1248 SVs discovered from a relapsed B-ALL cohort of 103 patients, although homopolymer indels can have high error rates (>1%). We then performed a comprehensive study of homopolymer indels in 361 cancer driver genes by using whole genome data from 1662 healthy donors from the SJLIFE cohort. Our data indicated that the number of repeating units are highly predictive relative to the error rate of homopolymer indels (R2=0.988, p=4.89 × 10−8). Utilizing these insights, we assayed end-of-induction remission samples from 72 B-cell lymphoblastic leukemia patients that relapsed by selecting ~5 somatic clonal SNV/Indel/SV markers, which confirmed that SVs and indels have >10-fold lower error rates than SNVs. Our next generation sequencing (NGS) approach had 44 positive detections (61%) and outperformed the current standard method of clinical flow cytometry (n=37; 51%) for detecting minimal residual disease. The NGS-based method detected 92% of designed markers for samples with MRD>0.3%, and this detection rate dropped to 27% for MRD between 0.1% and 0.01%, indicating the difficulty in recovering mutant molecules when their frequencies are very low. Conclusions: Overall, we established indel and SV error profiles in deep next generation sequencing data enabling superior tumor detection performance at very low burdens, with lower error rates than what is observed for SNVs. Our work will have a significant impact on the clinical diagnosis and monitoring of human cancers and beyond. Citation Format: Ying Shao, Quang Tran, Pandurang Kolekar, Yanling Liu, Andrea McBride, Tyler Jones, Heather Mulder, Lingyun Ji, Benjamin Huang, Soheil Meshinchi, Jeffery Klco, Jinghui Zhang, William Carroll, Mignon Loh, Patrick Brown, John Easton, Xiaotu Ma. Analysis of indel and structural variant error profiles in deep next generation sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB077.

Predictive significance of PRR11 in prognosis and immune infiltration of glioma patients.

Glioma, characterized by invasive growth and high recurrence, is the main cause of brain-tumor-related deaths. Currently, proline rich 11 (PRR11) has served as a reliable predictor in diagnosis, prognosis, and immunotherapeutic response of several human cancers. However, the predictive significance of PRR11 in prognosis and immune infiltration of glioma patients is still uncertain. Therefore, glioma-related information from multiple data sets and single-cell sequencing (scRNA-seq), including CGGA, TCGA_GTEx, GEO, 10x Genomics, and Smart-seq, was included in this study. Then, clinical correlation analysis, univariate and multivariate Cox analysis, Kaplan-Meier survival analysis, and receiver operating characteristic curve analysis were utilized to explore diagnostic and prognostic value of PRR11 in glioma. Besides, functional mechanisms of PRR11 in glioma were also carried out via Spearman's correlation, immune infiltration, scRNA-seq, gene ontology enrichment, and Kyoto encyclopedia of genes and genomes analysis. Finally, we selected two glioma cell lines, U251 and LN229, for validation of oncogenic role of PRR11 in glioma. As a result, PRR11 is a promising and reliable predictor in diagnosis and prognosis of glioma patients, positively correlates with clinical malignancy, World Health Organization grades, IDH mutation status, 1p19q codeletion status, histology, and poor survival time. Mechanically, PRR11 is specifically overexpressed in Mano/Macro_C21 and CD8Tex cells and interferes with cell-cycle, transcription factors, immune infiltration, MYC targets, and PI3K/AKT/mTOR signaling. Further exploration of biological mechanisms of PRR11 in glioma patients reveals that PRR11 might exert its oncogenic effects via involvement with chromosome segregation, coenzyme binding, cell cycle, and pyruvate metabolism. Moreover, PRR11 knockdown suppressed cell viability, migration, cell-cycle progression, and induced apoptosis and autophagy in glioma cells. In summary, our results suggest that PRR11 might be a novel and reliable diagnostic and prognostic predictor in glioma patients, providing a promising clinical therapeutic target for glioma.

Vaginal Douching: A Neglected Health Risk Behavior among Women and Sexually Active Adolescent Girls in Tanzania?

In this paper I present the public heath importance of vaginal douching or vaginal cleaning risk behavior among women and sexually active adolescent girls in Tanzania. I argue, vaginal douching control exclusion from sexually transmitted infections, human immunodeficiency virus and cervical cancer diagnosis, treatment and control guidelines explains, in part, why health professionals (physician, obstetricians and gynecologists) rarely advise their clients on this risk behavior. Similarly, this exclusion explains why there are limited research and (public) health interventions aiming at combating vaginal douching hazards among douching women and sexually active adolescent girls in this country. I recommend the Ministry of Health and (health) development partners to initiate, encourage and support health (medical and public health) research to generate comprehensive and informative multidisciplinary data on vaginal douching behaviors and practices and their impact to douching women’s and sexually active adolescent girls’ health and wellbeing. The goal is to generate data that would facilitate improving healthcare professionals’ ability to diagnose, treat, control, and prevent vaginal douching-related health problems and illnesses among douching women, sexually active adolescent girls and the populace in Tanzania.

Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning

Biopsy is the recommended standard for pathological diagnosis of liver carcinoma. However, this method usually requires sectioning and staining, and well-trained pathologists to interpret tissue images. Here, we utilize Raman spectroscopy to study human hepatic tissue samples, developing and validating a workflow for in vitro and intraoperative pathological diagnosis of liver cancer. We distinguish carcinoma tissues from adjacent non-tumour tissues in a rapid, non-disruptive, and label-free manner by using Raman spectroscopy combined with deep learning, which is validated by tissue metabolomics. This technique allows for detailed pathological identification of the cancer tissues, including subtype, differentiation grade, and tumour stage. 2D/3D Raman images of unprocessed human tissue slices with submicrometric resolution are also acquired based on visualization of molecular composition, which could assist in tumour boundary recognition and clinicopathologic diagnosis. Lastly, the potential for a portable handheld Raman system is illustrated during surgery for real-time intraoperative human liver cancer diagnosis.

Memetic micro-genetic algorithms for cancer data classification

Fast and precise medical diagnosis of human cancer is crucial for treatment decisions. Gene selection consists of identifying a set of informative genes from microarray data to allow high predictive accuracy in human cancer classification. This task is a combinatorial search problem, and optimisation methods can be applied for its resolution. In this paper, two memetic micro-genetic algorithms (MμV1 and MμV2) with different hybridisation approaches are proposed for feature selection of cancer microarray data. Seven gene expression datasets are used for experimentation. The comparison with stochastic state-of-the-art optimisation techniques concludes that problem-dependent local search methods combined with micro-genetic algorithms improve feature selection of cancer microarray data.