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Abstract The IL6-GP130-STAT3 pathway facilitates lung cancer progression and resistance to tyrosine kinase inhibitors. Although glycosylation alters the stability of GP130, its effect on the ligand IL6 remains unclear. We herein find that N-glycosylated IL6, especially at Asn73, primarily stimulates JAK-STAT3 signaling and prolongs STAT3 phosphorylation, whereas N-glycosylation-defective IL6 (deNG-IL6) induces shortened STAT3 activation and alters the downstream signaling preference for the SRC-YAP-SOX2 axis. This signaling shift induces epithelial-mesenchymal transition (EMT) and migration in vitro and metastasis in vivo, which are suppressed by targeted inhibitors and shRNAs against SRC, YAP, and SOX2. Osimertinib-resistant lung cancer cells secrete a large amount of deNG-IL6 through reduced N-glycosyltransferase gene expression, leading to clear SRC-YAP activation. deNG-IL6 contributes to drug resistance, as confirmed by in silico analysis of cellular and clinical transcriptomes and signal expression in patient specimens. Therefore, the N-glycosylation status of IL6 not only affects cell behaviors but also shows promise in monitoring the dynamics of lung cancer evolution.

Abstract Background Ovarian carcinoma (OC) is a fatal malignancy, with most patients experiencing recurrence and resistance to chemotherapy. In contrast to hematogenous metastasizing tumors, ovarian cancer cells disseminate within the peritoneal cavity, especially the omentum. Previously, we reported omental crown-like structure (CLS) number is associated with poor prognosis of advanced-stage OC. CLS that have pathologic features of a dead or dying adipocyte was surrounded by several macrophages is well known a histologic hallmark for inflammatory adipose tissue. In this study, we attempted to clarify the interaction between metastatic ovarian cancer cells and omental CLS, and to formulate a therapeutic strategy for advanced-stage ovarian cancer. Methods A three-cell (including OC cells, adipocytes and macrophages) coculture model was established to mimic the omental tumor microenvironment (TME) of ovarian cancer. Caspase-1 activity, ATP and free fatty acids (FFA) levels were detected by commercial kits. An adipocyte organoid model was established to assess macrophages migration and infiltration. In vitro and in vivo experiments were performed for functional assays and therapeutic effect evaluations. Clinical OC tissue samples were collected for immunochemistry stain and statistics analysis. Results In three-cell coculture model, OC cells-derived IL-6 and IL-8 could induce the occurrence of pyroptosis in omental adipocytes. The pyroptotic adipocytes release ATP to increase macrophage infiltration, release FFA into TME, uptake by OC cells to increase chemoresistance. From OC tumor samples study, we demonstrated patients with high gasdermin D (GSDMD) expression in omental adipocytes is highly correlated with chemoresistance and poor outcome in advanced-stage OC. In animal model, by pyroptosis inhibitor, DSF, effectively retarded tumor growth and prolonged mice survival. Conclusions Omental adipocyte pyroptosis may contribute the chemoresistance in advanced stage OC. Omental adipocytes could release FFA and ATP through the GSDMD-mediate pyroptosis to induce chemoresistance and macrophages infiltration resulting the poor prognosis in advanced-stage OC. Inhibition of adipocyte pyroptosis may be a potential therapeutic modality in advanced-stage OC with omentum metastasis.

Abstract Background Cingulin (CGN) is a pivotal cytoskeletal adaptor protein located at tight junctions. This study investigates the link between CGN mutation and increased cancer susceptibility through genetic and mechanistic analyses and proposes a potential targeted therapeutic approach. Methods In a high-cancer-density family without known pathogenic variants, we performed tumor-targeted and germline whole-genome sequencing to identify novel cancer-associated variants. Subsequently, these variants were validated in a 222 cancer patient cohort, and CGN c.3560C > T was identified as a potential cancer-risk allele. Both wild-type (WT) (c.3560C > C) and variant (c.3560C > T) were transfected into cancer cell lines and incorporated into orthotopic xenograft mice model for evaluating their effects on cancer progression. Western blot, immunofluorescence analysis, migration and invasion assays, two-dimensional gel electrophoresis with mass spectrometry, immunoprecipitation assays, and siRNA applications were used to explore the biological consequence of CGN c.3560C > T. Results In cancer cell lines and orthotopic animal models, CGN c.3560C > T enhanced tumor progression with reduced sensitivity to oxaliplatin compared to the CGN WT. The variant induced downregulation of epithelial marker, upregulation of mesenchymal marker and transcription factor, which converged to initiate epithelial-mesenchymal transition (EMT). Proteomic analysis was conducted to investigate the elements driving EMT in CGN c.3560C > T. This exploration unveiled overexpression of IQGAP1 induced by the variant, contrasting the levels observed in CGN WT. Immunoprecipitation assay confirmed a direct interaction between CGN and IQGAP1. IQGAP1 functions as a regulator of multiple GTPases, particularly the Rho family. This overexpressed IQGAP1 was consistently associated with the activation of Rac1, as evidenced by the analysis of the cancer cell line and clinical sample harboring CGN c.3560C > T. Notably, activated Rac1 was suppressed following the downregulation of IQGAP1 by siRNA. Treatment with NSC23766, a selective inhibitor for Rac1-GEF interaction, resulted in the inactivation of Rac1. This intervention mitigated the EMT program in cancer cells carrying CGN c.3560C > T. Consistently, xenograft tumors with WT CGN showed no sensitivity to NSC23766 treatment, but NSC23766 demonstrated the capacity to attenuate tumor growth harboring c.3560C > T. Conclusions CGN c.3560C > T leads to IQGAP1 overexpression, subsequently triggering Rac1-dependent EMT. Targeting activated Rac1 is a strategy to impede the advancement of cancers carrying this specific variant.

Introduction: Lynch syndrome is caused by a germline mutation in mismatch repair (MMR) genes, leading to the loss of expression of MMR heterodimers, either MLH1/PMS2 or MSH2/MSH6, or isolated loss of PMS2 or MSH6. Concurrent loss of both heterodimers is uncommon, and patients carrying pathogenic variants affecting different MMR genes are rare, leading to the lack of cancer screening recommendation for these patients.Case presentation:Here, we reported a female with a family history of Lynch syndrome with MLH1 c.676C > T mutation. She developed endometrial cancer at 37 years old, with loss of MLH1/PMS2 expression. Immunohistochemical staining on tumor samples incidentally detected the additional loss of MSH6 expression. Whole exome sequencing on genomic DNA from peripheral blood revealed MSH6 c.2731C > T mutation, which was confirmed to be inherited from her mother, who had an early-onset ascending colon cancer without cancer family history. Conclusion: This is a rare case of the Lynch syndrome harboring germline mutations simultaneously in two different MMR genes inherited from two families with Lynch syndrome. The diagnosis of endometrial cancer at the age less than 40 years is uncommon for Lynch syndrome-related endometrial cancer. This suggests an earlier cancer screening for patients carrying two MMR mutations.

Goal: The early diagnosis and treatment of hepatitis is essential to reduce hepatitis-related liver function deterioration and mortality. One component of the widely-used Ishak grading system for the grading of periportal interface hepatitis is based on the percentage of portal borders infiltrated by lymphocytes. Thus, the accurate detection of lymphocyte-infiltrated periportal regions is critical in the diagnosis of hepatitis. However, the infiltrating lymphocytes usually result in the formation of ambiguous and highly-irregular portal boundaries, and thus identifying the infiltrated portal boundary regions precisely using automated methods is challenging. This study aims to develop a deep-learning-based automatic detection framework to assist diagnosis. Methods: The present study proposes a framework consisting of a Structurally-REfined Deep Portal Segmentation module and an Infiltrated Periportal Region Detection module based on heterogeneous infiltration features to accurately identify the infiltrated periportal regions in liver Whole Slide Images. Results: The proposed method achieves 0.725 in F1-score of lymphocyte-infiltrated periportal region detection. Moreover, the statistics of the ratio of the detected infiltrated portal boundary have high correlation to the Ishak grade (Spearman's correlations more than 0.87 with p-values less than 0.001) and medium correlation to the liver function index aspartate aminotransferase and alanine aminotransferase (Spearman's correlations more than 0.63 and 0.57 with p-values less than 0.001). Conclusions: The study shows the statistics of the ratio of infiltrated portal boundary have correlation to the Ishak grade and liver function index. The proposed framework provides pathologists with a useful and reliable tool for hepatitis diagnosis.

Objective: The COVID-19 pandemic has had an enormous impact on society and the medical environment in Taiwan in 2022. As pregnant women with COVID-19 are at higher risk for multiple complications, Taiwan needs a COVID-19 specialized maternity unit to improve the quality of maternal and neonatal care. Materials and methods: We share our experience with specialized maternity unit for pregnant women with COVID-19 at the National Cheng Kung University Hospital, where we can have careful evaluation, safe birth, and comprehensive postpartum care. Results: Our COVID-19 specialized maternity unit enrolled 253 pregnant women with COVID-19, 90 (35.6%) pregnant women were admitted to the specialized maternity unit, and 71 (28.1%) pregnant women gave birth during hospitalization in two months. All pregnant women recovery well and real-time polymerase chain reaction tests on all infants were negative for COVID-19. Conclusion: A specialized maternity unit can provide pregnant women with a safe birth environment, immediate maternity care, and high medical quality. It can also help health workers in non-specialized maternity units deal with COVID-19-related psychological stress. Therefore, setting up one specialized maternity unit in the city during the pandemic should be guardedly considered.

Abstract Background Many methodologies for selecting histopathological images, such as sample image patches or segment histology from regions of interest (ROIs) or whole-slide images (WSIs), have been utilized to develop survival models. With gigapixel WSIs exhibiting diverse histological appearances, obtaining clinically prognostic and explainable features remains challenging. Therefore, we propose a novel deep learning-based algorithm combining tissue areas with histopathological features to predict cancer survival. Methods The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) dataset was used in this investigation. A deep convolutional survival model (DeepConvSurv) extracted histopathological information from the image patches of nine different tissue types, including tumors, lymphocytes, stroma, and mucus. The tissue map of the WSIs was segmented using image processing techniques that involved localizing and quantifying the tissue region. Six survival models with the concordance index (C-index) were used as the evaluation metrics. Results We extracted 128 histopathological features from four histological types and five tissue area features from WSIs to predict colorectal cancer survival. Our method performed better in six distinct survival models than the Whole Slide Histopathological Images Survival Analysis framework (WSISA), which adaptively sampled patches using K-means from WSIs. The best performance using histopathological features was 0.679 using LASSO-Cox. Compared to histopathological features alone, tissue area features increased the C-index by 2.5%. Based on histopathological features and tissue area features, our approach achieved performance of 0.704 with RIDGE-Cox. Conclusions A deep learning-based algorithm combining histopathological features with tissue area proved clinically relevant and effective for predicting cancer survival.

Abstract Background The metabolome is the best representation of cancer phenotypes. Gene expression can be considered a confounding covariate affecting metabolite levels. Data integration across metabolomics and genomics to establish the biological relevance of cancer metabolism is challenging. This study aimed to eliminate the confounding effect of metabolic gene expression to reflect actual metabolite levels in microsatellite instability (MSI) cancers. Methods In this study, we propose a new strategy using covariate-adjusted tensor classification in high dimensions (CATCH) models to integrate metabolite and metabolic gene expression data to classify MSI and microsatellite stability (MSS) cancers. We used datasets from the Cancer Cell Line Encyclopedia (CCLE) phase II project and treated metabolomic data as tensor predictors and data on gene expression of metabolic enzymes as confounding covariates. Results The CATCH model performed well, with high accuracy (0.82), sensitivity (0.66), specificity (0.88), precision (0.65), and F1 score (0.65). Seven metabolite features adjusted for metabolic gene expression, namely, 3-phosphoglycerate, 6-phosphogluconate, cholesterol ester, lysophosphatidylethanolamine (LPE), phosphatidylcholine, reduced glutathione, and sarcosine, were found in MSI cancers. Only one metabolite, Hippurate, was present in MSS cancers. The gene expression of phosphofructokinase 1 (PFKP), which is involved in the glycolytic pathway, was related to 3-phosphoglycerate. ALDH4A1 and GPT2 were associated with sarcosine. LPE was associated with the expression of CHPT1, which is involved in lipid metabolism. The glycolysis, nucleotide, glutamate, and lipid metabolic pathways were enriched in MSI cancers. Conclusions We propose an effective CATCH model for predicting MSI cancer status. By controlling the confounding effect of metabolic gene expression, we identified cancer metabolic biomarkers and therapeutic targets. In addition, we provided the possible biology and genetics of MSI cancer metabolism.

Abstract Background Certain sequences of genomic mutations can lead to cancer formation and affect treatment outcomes and drug resistance. We constructed a cancer evolutionary tree using bulk-targeted deep sequencing to explore the impact of sequential and co-occurring somatic mutations on patients with stage III colorectal cancer (CRC). Methods A total of 108 stage III CRC patients from National Cheng Kung University Hospital (NCKUH) were recruited for this study between Jan. 2014 and Jan. 2019. Clinical information and tumor-targeted deep sequencing data were collected. Phylogenetic trees were reconstructed for evolutionary trajectories. We used a machine learning model for survival analysis. Results Six sequential somatic mutations stratified patients into seven subgroups based on survival. Patients carrying sequential germline followed by DNA damage response-related ATM or BRCA2 somatic mutations or non-TP53, APC somatic mutations had a better outcome than those without such mutations. The 4-year recurrence-free survival (RFS) probability was 88% in the low-risk group (G1) and 46% in the high-risk group (G2) (log-rank p-value 2e-05). The predictive efficacy by the area under the curve (AUC) was 0.73, 0.7, 0.797, and 0.88 at 2, 4, 6, and 8 years, respectively. The mutation status of mismatch repair (MMR) genes was not associated with RFS. Different genomic features were found between the groups. The orders of APC, KRAS and APC, BRCA2 sequential somatic mutations were associated with clinical outcomes. The occurrence of somatic mutations in BRCA2, such as TP53 somatic mutations, affected recurrence-free survival. Conclusions According to the evolution model, DNA damage response (DDR)-related ATM or BRCA2 somatic mutations are promising biomarkers for assessing the response of stage III CRC patients to oxaliplatin-based chemotherapy. The sequential order and co-occurring DDR somatic mutations are associated with recurrence-free survival.

Abstract Background Functional disruptions by large germline genomic structural variants in susceptible genes are known risks for cancer. We used deletion structural variants (DSVs) generated from germline whole-genome sequencing (WGS) and DSV immune-related association tumor microenvironment (TME) to predict cancer risk and prognosis. Methods We investigated the contribution of germline DSVs to cancer susceptibility and prognosis by silicon and causal inference models. DSVs in germline WGS data were generated from the blood samples of 192 cancer and 499 non-cancer subjects. Clinical information, including family cancer history (FCH), was obtained from the National Cheng Kung University Hospital and Taiwan Biobank. Ninety-nine colorectal cancer (CRC) patients had immune response gene expression data. We used joint calling tools and an attention-weighted model to build the cancer risk predictive model and identify DSVs in familial cancer. The survival support vector machine (survival-SVM) was used to select prognostic DSVs. Results We identified 671 DSVs that could predict cancer risk. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of the attention-weighted model was 0.71. The 3 most frequent DSV genes observed in cancer patients were identified as ADCY9, AURKAPS1, and RAB3GAP2 (p < 0.05). The DSVs in SGSM2 and LHFPL3 were relevant to colorectal cancer. We found a higher incidence of FCH in cancer patients than in non-cancer subjects (p < 0.05). SMYD3 and NKD2DSV genes were associated with cancer patients with FCH (p < 0.05). We identified 65 immune-associated DSV markers for assessing cancer prognosis (p < 0.05). The functional protein of MUC4 DSV gene interacted with MAGE1 expression, according to the STRING database. The causal inference model showed that deleting the CEP72 DSV gene affect the recurrence-free survival (RFS) of IFIT1 expression. Conclusions We established an explainable attention-weighted model for cancer risk prediction and used the survival-SVM for prognostic stratification by using germline DSVs and immune gene expression datasets. Comprehensive assessments of germline DSVs can predict the cancer risk and clinical outcome of colon cancer patients.

Background Multidisciplinary rounds (MDRs) are scheduled, patient-focused communication mechanisms among multidisciplinary providers in the intensive care unit (ICU). Objectivei-Dashboard is a custom-developed visualization dashboard that supports (1) key information retrieval and reorganization, (2) time-series data, and (3) display on large touch screens during MDRs. This study aimed to evaluate the performance, including the efficiency of prerounding data gathering, communication accuracy, and information exchange, and clinical satisfaction of integrating i-Dashboard as a platform to facilitate MDRs. MethodsA cluster-randomized controlled trial was performed in 2 surgical ICUs at a university hospital. Study participants included all multidisciplinary care team members. The performance and clinical satisfaction of i-Dashboard during MDRs were compared with those of the established electronic medical record (EMR) through direct observation and questionnaire surveys. ResultsBetween April 26 and July 18, 2021, a total of 78 and 91 MDRs were performed with the established EMR and i-Dashboard, respectively. For prerounding data gathering, the median time was 10.4 (IQR 9.1-11.8) and 4.6 (IQR 3.5-5.8) minutes using the established EMR and i-Dashboard (P

Abstract Circulating cell-free DNA (cfDNA) analysis is an important tool for cancer monitoring. The patient-specific mutations identified in colorectal cancer (CRC) tissues are usually used to design the cfDNA analysis. Despite high specificity in predicting relapse, the sensitivity in most studies is around 40–50%. To improve this weakness, we designed a cfDNA panel according to the CRC genomic landscape and recurrent-specific mutations. The pathological variants in cfDNA samples from 60 CRC patients were studied by a next-generation sequencing (NGS) method incorporating the dual molecular barcode. Interestingly, patients in the disease positive group had a significantly higher cfDNA concentration than those in the disease negative group. Based on receiver operating characteristic analysis, the cfDNA concentration of 7 ng/mL was selected into the analytical workflow. The sensitivity in determining the disease status was 72.4%, which represented a considerable improvement on prior studies, and the specificity remained high at 80.6%. Compared to standard imaging and laboratory studies, earlier detection of residual disease and clinical benefits were shown on two cases by this cfDNA assay. We conclude this integrative framework of cfDNA analytical pipeline with a satisfactory sensitivity and specificity could be used in postoperative CRC surveillance.

Store-operated Ca2+ entry (SOCE) is an essential pathway for Ca2+ signaling, and regulates various vital cellular functions. It is triggered by the endoplasmic reticulum Ca2+ sensor stromal interaction molecule 1 (STIM1). Illustration of STIM1 spatiotemporal structure at the nanometer scale during SOCE activation provides structural and functional insights into the fundamental Ca2+ homeostasis. In this study, we used direct stochastic optical reconstruction microscopy (dSTORM) to revisit the dynamic process of the interaction between STIM1, end-binding protein (EB), and microtubules to the ER-plasma membrane. Using dSTORM, we found that“powder-like”STIM1 aggregates into “trabecular-like” architectures toward the cell periphery during SOCE, and that an intact microtubule network and EB1 are essential for STIM1 trafficking. After thapsigargin treatment, STIM1 can interact with EB1 regardless of undergoing aggregation. We generated STIM1 variants adapted from a real-world database and introduced them into SiHa cells to clarify the impact of STIM1 mutations on cancer cell behavior. The p.D76G and p.D84Y variants locating on the Ca2+ binding domain of STIM1 result in inhibition of focal adhesion turnover, Ca2+ influx during SOCE and subsequent cell migration. Inversely, the p.R643C variant on the microtubule interacting domain of STIM1 leads to dissimilar consequence and aggravates cell migration. These findings imply that STIM1 mutational patterns have an impact on cancer metastasis, and therefore could be either a prognostic marker or a novel therapeutic target to inhibit the malignant behavior of STIM1-mediated cancer cells. Altogether, we generated novel insight into the role of STIM1 during SOCE activation, and uncovered the impact of real-world STIM1 variants on cancer cells.

Summary: The new generation, i.e., second- and third-generation, drug-eluting stents (DESs) remain a risk of in-stent restenosis (ISR). We evaluated the power of a genetic risk score (GRS) model to identify high-risk populations for new generation DES ISR. We enrolled patients with coronary artery disease (CAD) treated with new generations DESs by a single-center cohort study in Taiwan and evaluated their genetic profile. After propensity score matching, there were 343 patients and 153 patients in the derivation and validation cohorts, respectively. Five selected single-nucleotide polymorphisms (SNPs), i.e., SNPs in CAMLG, GALNT2, C11orf84, THOC5, and SAMD11, were included to calculate the GRS for new generation DES ISR. In the derivation and the validation cohorts, patients with a GRS greater than or equal to 3 had significantly higher new generation DES ISR rates. We provide biological information for interventional cardiologists prior to percutaneous coronary intervention by specific five SNP-derived GRS.

Programmed death-ligand 1 (PD-L1) is an immune checkpoint molecule that can regulate immune responses in the tumor microenvironment (TME); however, the clinical applications of PD-L1 in early-stage colorectal cancer (CRC) remain unclear. In this study, we aimed to investigate the relationship between PD-L1 expression and survival outcome and explore its relevant immune responses in CRC. PD-L1 expression was evaluated by immunohistochemical staining to determine the tumor proportion score and combined positive score (CPS) in a Taiwanese CRC cohort. The oncomine immune response research assay was conducted for immune gene expression analyses. CRC datasets from the TCGA database were reappraised for PD-L1-associated gene enrichment analyses using GSEA. The high expression of PD-L1 (CPS ≥ 5) was associated with longer recurrence-free survival (p = 0.031) and was an independent prognostic factor as revealed by multivariate analysis. High PD-L1 expression was related to six immune-related gene signatures, and CXCL9 is the most significant overexpressed gene in differential analyses. High CXCL9 expression correlated with increased infiltration levels of immune cells in the TME, including CD8+ T lymphocytes and M1 macrophages. These findings suggest that high PD-L1 expression is a prognostic factor of early-stage CRC, and CXCL9 may play a key role in regulating PD-L1 expression.

To provide precision medicine for better cancer care, researchers must work on clinical patient data, such as electronic medical records, physiological measurements, biochemistry, computerized tomography scans, digital pathology, and the genetic landscape of cancer tissue. To interpret big biodata in cancer genomics, an operational flow based on artificial intelligence (AI) models and medical management platforms with high-performance computing must be set up for precision cancer genomics in clinical practice. To work in the fast-evolving fields of patient care, clinical diagnostics, and therapeutic services, clinicians must understand the fundamentals of the AI tool approach. Therefore, the present article covers the following four themes: (i) computational prediction of pathogenic variants of cancer susceptibility genes; (ii) AI model for mutational analysis; (iii) single-cell genomics and computational biology; (iv) text mining for identifying gene targets in cancer; and (v) the NVIDIA graphics processing units, DRAGEN field programmable gate arrays systems and AI medical cloud platforms in clinical next-generation sequencing laboratories. Based on AI medical platforms and visualization, large amounts of clinical biodata can be rapidly copied and understood using an AI pipeline. The use of innovative AI technologies can deliver more accurate and rapid cancer therapy targets.

The analysis of cell-free DNA (cfDNA) is rapidly emerging as a powerful approach to guide the clinical care of cancer patients. Several comprehensive cfDNA assays designed to detect mutations across several genes are now available. Here, we analyzed the use of a cfDNA panel in colorectal cancer (CRC) patients. Twenty-eight CRC patients with relapse or metastatic disease and 31 patients with no evidence of disease (NED) were enrolled. Genomic alterations in cfDNA were analyzed by the Oncomine™ Pan-Cancer Cell-Free Assay that detects hotspot mutations, small indels, copy number changes, and gene fusions across 52 genes. In the NED group, genomic alterations in cfDNA were detected in 12/31 patients (38.7%). The detection of alterations was more common in patients who were ≥60 years old, and the most common genomic alteration was a TP53 mutation. Fifty percent of the TP53 mutations were frequently or very frequently found in human cancers. Among 28 patients with relapse or metastatic disease, 22 (78.6%) had genomic alterations in cfDNA. The alterations were detected most frequently in TP53 (n = 10), followed by KRAS (n = 9). Actionable targets for CRC, including ERBB2 amplification and BRAF mutations, could be identified by this cfDNA assay. Compared with mutational profiling routinely analyzed using tumor samples, several additional targets with currently available therapies, including IDH1, IDH2, and PDGFRA mutations, were discovered. The cfDNA assay could identify potentially actionable targets for CRC. Identifying how to filter out cancer-like genomic alterations not derived from tumors remains a challenge.

Many studies failed to demonstrate benefit from the addition of targeted agents to current standard adjuvant FOLFOX chemotherapy in stage III colorectal cancer (CRC) patients. Intratumor heterogeneity may foster the resistant subclones and leads to cancer recurrence. Here, we built a cancer evolution model and applied machine learning analysis to identify potential therapeutic targets. Among 78 CRC cases, whole-genome (WGS) and deep targeted sequencing data generated from paired blood and primary tumor were used for phylogenetic tree reconstruction. Genetic alterations in the PI3K/AKT, and RTK oncogenic signaling pathways were commonly detected in founding clones. The dominant subclones frequently exhibited dysregulations in the TP53, FBXW7/NOTCH1 tumor suppression, and DNA repair pathways. Fourteen genetic mutations were simultaneously selected by random forest and LASSO methods. The logistic regression model had better accuracy (79%), precision (70%), and recall (65%) and area under the curve (AUC) (82%) for cancer recurrence prediction. Three genes, including MYO18A in the founding clone, FBXW7, and ATM in the dominant subclone, affected the prognosis were selected simultaneously by different feature sets. The in vitro studies, HCT-116 cells transfected with MYO18A siRNA demonstrated a significant reduction in cell migration activity by 20–40%. These results indicate that MYO18A plays a crucial role in the migration of human CRC cells. The cancer evolution model revealed the critical mutations in the founding and dominant subclones. They can be used to predict clinical outcomes and the development of novel therapeutic targets for stage III CRC.

This study aimed to determine the real-world, long-term prognostic impacts, and adverse effects (AEs) of bevacizumab (BEV) in Asian patients with ovarian/tubal/peritoneal cancers. We retrospectively reviewed the medical records of consecutive patients with ovarian/tubal/peritoneal cancer on front-line chemotherapy with or without BEV (Cohort 1) and those who relapsed following chemotherapy and/or BEV (Cohort 2) between 2011 and 2018 in a tertiary medical centre. Patient characteristics, BEV dosages, clinical outcomes, and AEs were analyzed. Hazard ratios for disease progression and death were analyzed using a cox proportional regression model. Benefits of BEV used throughout triweekly, in terms of improved progression-free survival (PFS) and overall survival (OS), were observed at a dosage of 7.5–15 mg/kg among advanced-stage Cohort 1 patients. A progression-free interval of

The impact of germline variants on the regulation of the expression of tumor microenvironment (TME)-based immune response genes remains unclear. Expression quantitative trait loci (eQTL) provide insight into the effect of downstream target genes (eGenes) regulated by germline-associated variants (eVariants). Through eQTL analyses, we illustrated the relationships between germline eVariants, TME-based immune response eGenes, and clinical outcomes. In this study, both RNA sequencing data from primary tumor and germline whole-genome sequencing data were collected from patients with stage III colorectal cancer (CRC). Ninety-nine high-risk subjects were subjected to immune response gene expression analyses. Seventy-seven subjects remained for further analysis after quality control, of which twenty-two patients (28.5%) experienced tumor recurrence. We found that 65 eQTL, including 60 germline eVariants and 22 TME-based eGenes, impacted the survival of cancer patients. For the recurrence prediction model, 41 differentially expressed genes (DEGs) achieved the best area under the receiver operating characteristic curve of 0.93. In total, 19 survival-associated eGenes were identified among the DEGs. Most of these genes were related to the regulation of lymphocytes and cytokines. A high expression of HGF, CCR5, IL18, FCER1G, TDO2, IFITM2, and LAPTM5 was significantly associated with a poor prognosis. In addition, the FCER1G eGene was associated with tumor invasion, tumor nodal stage, and tumor site. The eVariants that regulate the TME-based expression of FCER1G, including rs2118867 and rs12124509, were determined to influence survival and chromatin binding preferences. We also demonstrated that FCER1G and co-expressed genes in TME were related to the aggregation of leukocytes via pathway analysis. By analyzing the eQTL from the cancer genome using germline variants and TME-based RNA sequencing, we identified the eQTL in immune response genes that impact colorectal cancer characteristics and survival.

Cervical cancer is a significant gynecological cancer and causes cancer-related deaths worldwide. Human papillomavirus (HPV) is implicated in the etiology of cervical malignancy. However, much evidence indicates that HPV infection is a necessary but not sufficient cause in cervical carcinogenesis. Therefore, the cellular pathophysiology of cervical cancer is worthy of study. This review summarizes the recent findings concerning the ion transport processes involved in cell volume regulation and intracellular Ca2+ homeostasis of epithelial cells and how these transport systems are themselves regulated by the tumor microenvironment. For cell volume regulation, we focused on the volume-sensitive Cl− channels and K+-Cl− cotransporter (KCC) family, important regulators for ionic and osmotic homeostasis of epithelial cells. Regarding intracellular Ca2+ homeostasis, the Ca2+ store sensor STIM molecules and plasma membrane Ca2+ channel Orai proteins, the predominant Ca2+ entry mechanism in epithelial cells, are discussed. Furthermore, we evaluate the potential of these membrane ion transport systems as diagnostic biomarkers and pharmacological interventions and highlight the challenges.

Systemic characterization of genomic alterations into signaling pathways helps to understand the molecular pathogenies of colorectal cancer; however, their clinical implications remain unclear. Here, 128 patients with metastatic colorectal cancer (mCRC) receiving targeted next generation sequencing were retrospectively enrolled to analyze the impact of altered oncogenic pathways on clinical outcome. The datasets from Memorial Sloan Kettering Cancer Center were used for validation. In 123 patients with non-MSI-high tumor, the most common mutated gene was TP53 (84.6%), followed by APC (78.0%), KRAS (49.6%), and SMAD4 (22.8%). When mutated genes were allocated into signaling pathways defined as The Cancer Genome Atlas Pan-Cancer Analysis Project, alterations of cell cycle, Wnt, p53, RTK-RAS, PI3K, TGF-β, Notch, and Myc pathways were identified in 88%, 87%, 85%, 75%, 28%, 26%, 17%, and 10% of mCRC tissues, respectively. The survival analyses revealed that Myc and TGF-β pathway alterations were associated with a shorter overall survival (OS) (hazard ratio [HR]: 2.412; 95% confidence interval [CI]: 1.139–5.109; p = 0.018 and HR: 2.754; 95% CI: 1.044–7.265; p = 0.033, respectively). The negative prognostic impact of altered TGF-β pathway was maintained in patients receiving an anti-EGFR antibody. The OS of patients with mCRC carrying MYC and BRAF mutation was shorter than those with either MYC or BRAF mutation (HR: 4.981, 95% CI: 0.296–83.92; p = 0.02). These findings have clinical implications, such as prognosis prediction, treatment guidance, and molecular-targeted therapy development.

Colony-stimulating factor 1 receptor (CSF-1R) acts as the receptor for colony stimulating factor 1, a cytokine that controls the production, differentiation, and function of macrophages. Prior studies showed cancer patients harboring germline CSF1R c.1085A>G genetic variant had better survival. Here, primary tumor samples from a stage III colorectal cancer (CRC) cohort were analyzed by a targeted gene expression assay containing 395 immune-related genes to study the immune mechanism underlying the different outcomes. CRC patients with CSF1R c.1085 genotype A_G had a better disease-free and overall survival than those with CSF1R genotype A_A. Compared to the group of patients without CSF1R variant, higher CD40LG expression, a surface marker of T cells, was found in the tumor tissues of patients with CSF1R c.1085 variant. In parallel with the higher CD40LG gene expression, immunofluorescent staining also showed more CD3+CD40L+ T cell infiltrates in tumors with CSF1R c.1085 genotype A_G. Moreover, higher IL-2 expression, known to be regulated by CD40 pathway, was also observed in tumors with CSF1R c.1085 genotype A_G than genotype A_A. Higher IL-2 expression generated by the interaction of CD40 ligand and CD40 between T cells and macrophages with CSF1R c.1085A>G variant is the potential mechanism explaining the different outcomes.

Abstract Background The Solanum species have been used for the treatment of warts, tumor and cancer in folk medicine. The S. incanum extract is an important traditional Chinese medicine in Taiwan since 1973. The purpose of the present study was to evaluate the efficacy and safety of Solanum incanum (synonym: Solanum undatum) extract (SR-T100), a water-soluble product primarily composed of alkaloid solamargine, for the treatment of human condyloma and to study the possible underlying anti-condyloma mechanisms. Methods We conducted a pilot study to test the effectiveness and side effects of SR-T100 gel (2.3% solamargine in Solanum incanum plant extract) for the treatment of external genital warts. We produced different types of human papillomavirus (HPV) E6/E7-infected cells by lentiviral technology and studied the differences in apoptosis and autophagy between these cells under the treatment of SR-T100. Results Nineteen (73%) of 26 patients using the SR-T100 gel exhibited a response, and 16 (61.5%) patients achieved total clearance. Only one patient showed severe (grade 3-4) skin-related side effects. SR-T100 induced mitochondria-dependent apoptosis in HPV-infected cells. Cells expressing the high-risk HPV E6/E7 type were resistant to SR-T100-induced apoptosis. SR-T100 induced a greater autophagic response in HPV 16, 18-E6/E7 cells than in HPV 6b, 11-E6/E7 cells. Autophagy inhibition enhanced SR-T100-induced apoptosis in HPV 16, 18-E6/E7 cells, whereas apoptosis inhibition enhanced SR-T100-induced autophagy in HPV 6b, 11-E6/E7 cells. Conclusions SR-T100 is effective for the treatment of human vulva condyloma, with few side effects. Compared with those with high-risk HPVs, cells with low-risk HPVs were more sensitive to SR-T100 treatment. Autophagy played a protective role in SR-T100-induced apoptosis in HPV-infected cells. Trial registration NCT01676792 ; Registered: August 29, 2012 (retrospectively registered).

Abstract Selective serotonin reuptake inhibitors (SSRIs) are the most commonly prescribed drugs for mood disorders. Long term use of SSRIs is associated with an increased risk of diabetes, but the underlying mechanism(s) remains elusive. E-cadherin-mediated cell-cell adhesion and elevated [Ca2+]i are important for insulin release and pancreatic β cell functions. This study aims to investigate whether a SSRI, fluoxetine (Prozac), induces pancreatic β cell dysfunction through affecting E-cadherin and/or [Ca2+]i. Here we show that fluoxetine significantly reduces glucose stimulated insulin secretion (GSIS). MIN6 cells, an established murine immortalized β cell line, form smaller colonies of loosely packed cells with reduced cell-cell contact after fluoxetine treatment. Immunofluorescence staining reveals that fluoxetine increases cytoplasmic accumulation of E-cadherin and reduces the membrane-localized E-cadherin probably due to increase of its endocytosis. Fluoxetine inhibits spreading of β cells on E-cad/Fc coated slides and also disrupts E-cadherin-mediated actin filaments. Additionally, fluoxetine significantly suppresses endoplasmic reticulum (ER) calcium release and store-operated calcium entry (SOCE) activation, probably through reduction of ER calcium storage and inhibition of stromal interaction molecule 1 (STIM1) trafficking. These data suggest that exposure to fluoxetine results in impaired β cell functions, occurring in concert with reduction of E-cadherin-dependent cell adhesion and alterations of calcium homeostasis.

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