Comprehensive Multiomics Analyses Establish the Optimal Prognostic Model for Resectable Gastric Cancer : Prognosis Prediction for Resectable GC.

BACKGROUND: Prognostic models based on multiomics data may provide better predictive capability than those established at the single-omics level. Here we aimed to establish a prognostic model for resectable gastric cancer (GC) with multiomics information involving mutational, copy number, transcriptional, methylation, and clinicopathological alterations. PATIENTS AND METHODS: The mutational, copy number, transcriptional, methylation data of 268, 265, 226, and 252 patients with stages I-III GC were downloaded from the TCGA database, respectively. Alterations from all omics were characterized, and prognostic models were established at the individual omics level and optimized at the multiomics level. All models were validated with a cohort of 99 patients with stages I-III GC. RESULTS: TTN, TP53, and MUC16 were among the genes with the highest mutational frequency, while UBR5, ZFHX4, PREX2, and ARID1A exhibited the most prominent copy number variations (CNVs). Upregulated COL10A1, CST1, and HOXC10 and downregulated GAST represented the biggest transcriptional alterations. Aberrant methylation of some well-known genes was revealed, including CLDN18, NDRG4, and SDC2. Many alterations were found to predict the patient prognosis by univariate analysis, while four mutant genes, two CNVs, five transcriptionally altered genes, and seven aberrantly methylated genes were identified as independent risk factors in multivariate analysis. Prognostic models at the single-omics level were established with these alterations, and optimized combination of selected alterations with clinicopathological factors was used to establish a final multiomics model. All single-omics models and the final multiomics model were validated by an independent cohort. The optimal area under the curve (AUC) was 0.73, 0.71, 0.71, and 0.85 for mutational, CNV, transcriptional, and methylation models, respectively. The final multiomics model significantly increased the AUC to 0.92 (P < 0.05). CONCLUSIONS: Multiomics model exhibited significantly better capability in predicting the prognosis of resectable GC than single-omics models.

Molecular Recognition-Modulated Hetero-Assembly of Nanostructures for Visualizable and Portable Detection of Circulating miRNAs.

Biomolecular markers, particularly circulating microRNAs (miRNAs) play an important role in diagnosis, monitoring, and therapeutic intervention of cancers. However, existing detection strategies remain intricate, laborious, and far from being developed for point-of-care testing. Here, we report a portable colorimetric sensor that utilizes the hetero-assembly of nanostructures driven by base pairing and recognition for direct detection of miRNAs. Following hybridization, two sizes of nanoparticles modified with single-strand DNA can be robustly assembled into heterostructures with strong optical resonance, exhibiting distinct structure colors. Particularly, the large nanoparticles are first arranged into nanochains to enhance scattering signals of small nanoparticles, which allows for sensitive detection and quantification of miRNAs without the requirement of target extraction, amplification, and fluorescent labels. Furthermore, we demonstrate the high specificity and single-base selectivity of testing different miRNA samples, which shows great potential in the diagnosis, staging, and monitoring of cancers. These heterogeneous assembled nanostructures provide an opportunity to develop simple, fast, and convenient tools for miRNAs detection, which is suitable for many scenarios, especially in low-resource setting.

Evaluation of the advantages of robotic versus laparoscopic surgery in elderly patients with colorectal cancer.

BACKGROUND: The incidence of colorectal cancer increases with aging. Curative-intent surgery based on a minimally invasive concept is expected to bring survival benefits to elderly patients (aged over 80 years) with colorectal cancer who are frequently with fragile health status and advanced tumors. The study explored survival outcomes in this patient population who received robotic or laparoscopic surgery and aimed to identify an optimal surgical option for those patients. METHODS: We retrieved the clinical materials and follow-up data on elderly patients with colorectal carcinoma who received robotic or laparoscopic surgery in our institution. The pathological and surgical outcomes were compared to examine the efficacy and safety of the two approaches. The DFS (disease-free survival) and OS (overall survival) results at 3 years after surgery were assessed to explore the survival benefits. RESULTS: A total of 111 patients were screened for the study, including 55 in the robotic group and 56 in the laparoscopic group. The demographic details were generally similar between the two groups. No statistically significant difference in the number of removed lymph nodes was observed between the two approaches, with a median of 15 versus 14 (P = 0.053). The intraoperative blood loss was significantly reduced by robotic technique when compared to the laparoscopic approach, with a mean of 76.9 ml versus 161.6 ml (P = 0.025). There were no significant differences in operation time, conversion, postoperative complications and recovery, and long-term outcomes between the two groups. CONCLUSION: Robotic surgery was prized for elderly patients with colorectal cancer who developed anemia and/or hematological conditions.

Comprehensive genomic profiling of colorectal cancer patients reveals differences in mutational landscapes among clinical and pathological subgroups.

With the widespread of colonoscopy, colorectal cancer remains to be one of the most detrimental types of cancer. Though there were multiple studies investigating the genomic landscape of colorectal cancer, a comprehensive analysis uncovering the differences between various types of colorectal cancer is still lacking. In our study, we performed genomic analysis on 133 patients with colorectal cancer. Mutated FAT1 and PKHD1 and altered Hippo pathway genes were found to be enriched in early-onset colorectal cancer. APOBEC signature was prevalent in microsatellite stable (MSS) patients and was related to lymph node metastasis. ZNF217 mutations were significantly associated with early-stage colorectal cancer. In all, this study represents a comprehensive genomic analysis uncovering potential molecular mechanisms underneath different subgroups of colorectal cancer thus providing new targets for precision treatment development.

Epigenetic drug screening defines a PRMT5 inhibitor-sensitive pancreatic cancer subtype.

Systemic therapies for pancreatic ductal adenocarcinoma (PDAC) remain unsatisfactory. Clinical prognosis is particularly poor for tumor subtypes with activating aberrations in the MYC pathway, creating an urgent need for novel therapeutic targets. To unbiasedly find MYC-associated epigenetic dependencies, we conducted a drug screen in pancreatic cancer cell lines. Here, we found that protein arginine N-methyltransferase 5 (PRMT5) inhibitors triggered an MYC-associated dependency. In human and murine PDACs, a robust connection of MYC and PRMT5 was detected. By the use of gain- and loss-of-function models, we confirmed the increased efficacy of PRMT5 inhibitors in MYC-deregulated PDACs. Although inhibition of PRMT5 was inducing DNA damage and arresting PDAC cells in the G2/M phase of the cell cycle, apoptotic cell death was executed predominantly in cells with high MYC expression. Experiments in primary patient-derived PDAC models demonstrated the existence of a highly PRMT5 inhibitor-sensitive subtype. Our work suggests developing PRMT5 inhibitor-based therapies for PDAC.

Identification of a novel immune-related lncRNA signature to predict prognostic outcome and therapeutic efficacy of LGG.

BACKGROUND: Recent studies have shown that the prognosis of low-grade glioma (LGG) patients is closely correlated with the immune infiltration and the expression of long-stranded non-coding RNAs (lncRNAs). It's meaningful to find the immune-related lncRNAs (irlncRNAs). METHODS: The Cancer Genome Atlas (TCGA) data was employed in the study to identify irlncRNAs and Cox regression model was applied to construct the risk proportional model based on irlncRNAs. RESULTS: In the study, we retrieved transcriptomic data of LGG from TCGA and identified 10 lncRNA signatures consisting of irlncRNAs by co-expression analysis. Then we plotted 1-year receiver operating characteristic (ROC) curves and calculated the area under the curve (AUC). LGG patients were divided into high-risk and low-risk groups according to the risk model. We found there were differences in survival prognosis, clinical characteristics, degree of immune cell infiltration, expression of immune gene checkpoint genes, and sensitivity to the commonly used chemotherapeutic agents of high-risk and low-risk groups. CONCLUSIONS: IrlncRNA-based risk assessment model can be used as a prognostic tool to predict the survival outcome and clinical characteristics of LGG and to guide treatment options.

NIR-II Fluorescent Probe for Detecting Trimethylamine Based on Intermolecular Charge Transfer.

A new kind of small organic NIR-II fluorophore molecule (ZS-1010) based on intermolecular charge transfer was developed as a NIR-II fluorescent probe for trimethylamine (TMA) detection, which is important for the diagnosis of cardiovascular disease, chronic kidney disease and diabetes. ZS-1010 has a strong push-pull electron system composed of electron donor unit and electron acceptor unit, exhibiting strong absorption and emission in the NIR-II region. When mixed with TMA which possesses strong electron-donating characteristics, the push-pull system of ZS-1010 will be affected along with the dipole moment change, leading to the quenching of fluorescence. This is the first example of TMA fluorescent probe in the NIR-II window showing deep penetration, fast response speed, high selectivity and pH stability.

Construction of immune-related lncRNA signature to predict aggressiveness, immune landscape, and drug resistance of colon cancer.

BACKGROUND: Colon cancer remains one of the most common malignancies across the world. Thus far, a biomarker, which can comprehensively predict the survival outcomes, clinical characteristics, and therapeutic sensitivity, is still lacking. METHODS: We leveraged transcriptomic data of colon cancer from the existing datasets and constructed immune-related lncRNA (irlncRNA) pairs. After integrating with clinical survival data, we performed differential analysis and identified 11 irlncRNAs signature using Lasso regression analysis. We next plotted the 1-, 5-, and 10-year curve lines of receiver operating characteristics, calculated the areas under the curve, and recognized the optimal cutoff point. Then, we validated the pair-risk model in terms of the survival outcomes of the patients involved. Moreover, we tested the reliability of the model for predicting tumor aggressiveness and therapeutic susceptibility of colon cancer. Additionally, we reemployed the 11 of irlncRNAs involved in the pair-risk model to construct an expression-risk model to predict the prognostic outcomes of the patients involved. RESULTS: We recognized a total of 377 differentially expressed irlncRNAs (DEirlcRNAs), including 28 low-expressed and 349 high-expressed irlncRNAs in colon cancer patients. After performing a univariant Cox analysis, we identified 115 risk irlncRNAs that were significantly correlated with survival outcomes of patients involved. By taking the overlap of the DEirlcRNAs and the risk irlncRNAs, we ultimately recognized 55 irlncRNAs as core irlncRNAs. Then, we established a Cox HR model (pair-risk model) as well as an expression HR model (exp-risk model) based on 11 of the 55 core irlncRNAs. We found that both of the two models significantly outperformed the commonly used clinical characteristics, including age, T, N, and M stages when predicting survival outcomes. Moreover, we validated the pair-risk model as a potential tool for studying the tumor microenvironment of colon cancer and drug susceptibility. Additionally, we noticed that combinational use of the pair-risk model and the exp-risk model yielded a more robust approach for predicting the survival outcomes of patients with colon cancer. CONCLUSIONS: We recognized 11 irlncRNAs and created a pair-risk model and an exp-risk model, which have the potential to predict clinical characteristics of colon cancer, either solely or conjointly.

Immunodeficiencies Push Readmissions in Malignant Tumor Patients: A Retrospective Cohort Study Based on the Nationwide Readmission Database.

BACKGROUND: Immunodeficiency diseases (IDDs) are associated with an increased proportion of cancer-related morbidity. However, the relationship between IDDs and malignancy readmissions has not been well described. Understanding this relationship could help us to develop a more reasonable discharge plan in the special tumor population. METHODS: Using the Nationwide Readmissions Database, we established a retrospective cohort study that included patients with the 16 most common malignancies, and we defined two groups: non-immunodeficiency diseases (NOIDDs) and IDDs. RESULTS: To identify whether the presence or absence of IDDs was associated with readmission, we identified 603,831 patients with malignancies at their time of readmission in which 0.8% had IDDs and in which readmission occurred in 47.3%. Compared with NOIDDs, patients with IDDs had a higher risk of 30-day (hazard ratio (HR) of 1.32; 95% CI of 1.25-1.40), 90-day (HR of 1.27; 95% CI of 1.21-1.34) and 180-day readmission (HR of 1.28; 95% CI of 1.22-1.35). More than one third (37.9%) of patients with IDDs had readmissions that occurred within 30 days and most (82.4%) of them were UPRs. An IDD was an independent risk factor for readmission in patients with colorectal cancer (HR of 1.32; 95% CI of 1.01-1.72), lung cancer (HR of 1.23; 95% CI of 1.02-1.48), non-Hodgkin's lymphoma (NHL) (HR of 1.16; 95% CI of 1.04-1.28), prostate cancer (HR of 1.45; 95% CI of 1.07-1.96) or stomach cancer (HR of 2.34; 95% CI of 1.33-4.14). Anemia (44.2%), bacterial infections (28.6%) and pneumonia (13.9%) were the 30-day UPR causes in these populations. (4) Conclusions: IDDs were independently associated with higher readmission risks for some malignant tumors. Strategies should be considered to prevent the causes of readmission as a post discharge plan.

NDC1 is a Prognostic Biomarker and Associated with Immune Infiltrates in Colon Cancer.

BACKGROUND: Colon cancer is one of the most lethal cancers in the world. NDC1 is a crucial membrane-integral nucleoporin of nuclear pore complexes. The clinical significance of NDC1 in colon cancer has not been demonstrated to date. Therefore, we determined to evaluate the association between NDC1 and colon cancer using the open-access database. METHODS: The TCGA data of colon cancer were extracted to determine the relationship between NDC1 and the clinical characterization. We assessed the predictive role of NDC1 expression in the survival of patients with colon cancer. Univariate and multivariate Cox proportional hazard models were applied to analyze the association between the clinical factors and prognosis. The TIMER database was used to describe the association between immune cell infiltration and specific gene expression in the colon cancer context. Gene set enrichment analysis (GSEA) was performed based on the TCGA dataset. RESULTS: A total of 445 colon cancer patients with complete clinical information were included. NDC1 expression was significantly up-regulated in colon cancer tissues compared to adjacent normal tissues. Univariate and multivariate Cox regression analyses showed that NDC1 was an independent prognostic factor. Patients with a higher level of NDC1 expression tend to survive longer compared to those with a lower level of NDC1 expression. The level of the NDC1 expression is significantly associated with TNM stages. Furthermore, we constructed a nomogram to predict the prognosis by using NDC1 as a factor. The expression of NDC1 was significantly associated with infiltration of B cell, CD8+T cells, macrophages, neutrophils, and dendritic cells in colon cancer lesions. Additionally, NDC1 was predominantly enriched in KRAS-related signaling pathways by GSEA. CONCLUSION: NDC1 can serve as a prognostic biomarker, which is negatively correlated with aggressiveness and positively associated with immune infiltrates of colon cancer.

Tumor-Specific Delivery of 5-Fluorouracil-Incorporated Epidermal Growth Factor Receptor-Targeted Aptamers as an Efficient Treatment in Pancreatic Ductal Adenocarcinoma Models.

BACKGROUNDS & AIMS: Fluoropyrimidine c (5-fluorouracil [5FU]) increasingly represents the chemotherapeutic backbone for neoadjuvant, adjuvant, and palliative treatment of pancreatic ductal adenocarcinoma (PDAC). Even in combination with other agents, 5FU efficacy remains transient and limited. One explanation for the inadequate response is insufficient and nonspecific delivery of 5FU to the tumor. METHODS: We designed, generated, and characterized 5FU-incorporated systematic evolution of ligands by exponential enrichment (SELEX)-selected epidermal growth factor receptor (EGFR)-targeted aptamers for tumor-specific delivery of 5FU to PDAC cells and tested their therapeutic efficacy in vitro and in vivo. RESULTS: 5FU-EGFR aptamers reduced proliferation in a concentration-dependent manner in mouse and human pancreatic cancer cell lines. Time-lapsed live imaging showed EGFR-specific uptake of aptamers via clathrin-dependent endocytosis. The 5FU-aptamer treatment was equally effective in 5FU-sensitive and 5FU-refractory PDAC cell lines. Biweekly treatment with 5FU-EGFR aptamers reduced tumor burden in a syngeneic orthotopic transplantation model of PDAC, in an autochthonously growing genetically engineered PDAC model (LSL-KrasG12D/+;LSL-Trp53flox/+;Ptf1a-Cre [KPC]), in an orthotopic cell line-derived xenograft model using human PDAC cells in athymic mice (CDX; Crl:NU-Foxn1nu), and in patient-derived organoids. Tumor growth was significantly attenuated during 5FU-EGFR aptamer treatment in the course of follow-up. CONCLUSIONS: Tumor-specific targeted delivery of 5FU using EGFR aptamers as the carrier achieved high target specificity; overcame 5FU resistance; and proved to be effective in a syngeneic orthotopic transplantation model, in KPC mice, in a CDX model, and in patient-derived organoids and, therefore, represents a promising backbone for pancreatic cancer chemotherapy in patients. Furthermore, our approach has the potential to target virtually any cancer entity sensitive to 5FU treatment by incorporating 5FU into cancer cell-targeting aptamers as the delivery platform.