Colorectal cancer (CRC) ranks as the third most prevalent tumor and the second leading cause of mortality. Early and accurate diagnosis holds significant importance in enhancing patient treatment and prognosis. Machine learning technology and bioinformatics have provided novel approaches for cancer diagnosis. This study aims to develop a CRC diagnostic model based on immunohistochemical staining image features using machine learning methods. Initially, CRC disease-specific genes were identified through bioinformatics analysis, SVM-RFE and Random Forest algorithm utilizing RNA-seq data from both GEO and TCGA databases. Subsequently, verification of these genes was performed using proteomics data from CPTAC and HPA database, resulting in identification of target proteins (AKR1B10, CA2, DHRS9, and ZG16) for further investigation. SVM and CNN were then employed to analyze and integrate the characteristics of immunohistochemical images to construct a reliable CRC diagnostic model. During the training and validation process of this model, cross-validation along with external validation methods were implemented to ensure accuracy and reliability. The results demonstrate that the established diagnostic model exhibits excellent performance in distinguishing between CRC and normal controls (accuracy rate: 0.999), thereby presenting potential prospects for clinical application. These findings are expected to provide innovative perspectives as well as methodologies for personalized diagnosis of CRC while offering more precise references for promising treatment.

Accurately distinguishing tumor cells from normal cells is a key issue in tumor diagnosis, evaluation, and treatment. Fluorescence-based immunohistochemistry as the standard method faces the inherent challenges of the heterogeneity of tumor cells and the lack of big data analysis of probing images. Here, we have demonstrated a machine learning-driven imaging method for rapid pathological diagnosis of five types of cancers (breast, colon, liver, lung, and stomach) using a perovskite nanocrystal probe. After conducting the bioanalysis of survivin expression in five different cancers, high-efficiency perovskite nanocrystal probes modified with the survivin antibody can recognize the cancer tissue section at the single cell level. The tumor to normal (T/N) ratio is 10.3-fold higher than that of a conventional fluorescent probe, which can successfully differentiate between tumors and adjacent normal tissues within 10 min. The features of the fluorescence intensity and pathological texture morphology have been extracted and analyzed from 1000 fluorescence images by machine learning. The final integrated decision model makes the area under the receiver operating characteristic curve (area under the curve) value of machine learning classification of breast, colon, liver, lung, and stomach above 90% while predicting the tumor organ of 92% of positive patients. This method demonstrates a high T/N ratio probe in the precise diagnosis of multiple cancers, which will be good for improving the accuracy of surgical resection and reducing cancer mortality.

Due to non-specific signs in the early stage of colorectal cancers, most patients result in a low early diagnosis rate. Survivin protein is upregulated in many malignant tumors, making it an ideal diagnostic and therapeutic target for colorectal cancers. However, the survivin protein is located in the cell nucleus, which needs the probe to penetrate the complex nuclear membrane structure for targeting and recognizing. Here, we have developed a sub-100 nm perovskite-based fluorescent probe modified with the survivin antibody, which can freely target survivin protein through the nuclear membrane. Polyvinyl pyrrolidone (PVP) coated perovskite crystals are obtained by the supersaturated crystallization, which keep the probe with great fluorescence intensity and resistance of water and oxygen. We have established colorectal cancer cell lines and colorectal cancer tissues to test the feasibility and efficacy of the probe. After investigating the fluorescence intensity values of 20 probing samples, the average fluorescence intensity of colorectal cancer sections is 8.7 times higher than that of paracancerous tissues. Our work demonstrates that the survivin can be an ideal fluorescence target for rapid diagnosis of colorectal cancer, which also provides a research foundation for fluorescence-guided surgery and nanotherapy for targeting the survivin protein.

Organoid biochips can replicate the micro-environment and functional traits of human organs in vitro, reflecting the physiological and pathological features of the human body. It provides a new platform for disease modeling and drug screening. However, the manual process of organoid cultivation and biochip construction using decellularized extracellular matrix-based gel is typically complex, expensive, and time-consuming (at least one month), which significantly hinders practical application. Here, we introduce a micro-needle-based pneumatic printing strategy for residual-free and high-throughput construction of patient-derived organoid biochips. By developing printable and biomimetic hydrogels, biopsy samples of cancer tissues can be effectively processed into discrete cells. Patient-derived colorectal cancer (CRC) cells in carboxymethylcellulose (CMC) and sodium alginate modified by adhesion sites exhibit high viability at 92

Abstract Background: Cancer stem cells (CSC) carry out a vital responsibility throughout the entire progress of colorectal cancer (CRC), and fulfil an essential biological function. However, lncRNAs participate in regulating CRC stem cells (CCSCs) and correlate strongly with the patients' prognosis. Therefore, it is crucial to identify the CCRC-related lncRNAs in CRC. Methods: We identified CCRCs-related lncRNAs through the Cell marker and TCGA databases. And the CCSC-related lncRNAs model was constructed by the differential, cox survival , and lasso regression analysis. Combining the GEO dataset, we determined the prognostic value by Kaplan-Meier analysis, univariate and multivariate cox survival analysis. Moreover, principal component analysis (PCA), clinical characterization, nomogram, gene mutation, gene set enrichment analysis (GSEA), immune microenvironment (TME), chemotherapy, intergroup differential gene, and protein-protein interaction (PPI) analysis were conducted to analyze the risk model. Furthermore, the core genes in the sub-module were comprehensively characterized. Results: In this research, abnormally expressed, prognostic and CSC-related lncRNAs were firstly identified. Through the lasso regression model, we obtained a robust risk signature consisting of 4 CCSC-related lncRNAs (ZEB1-AS1，LINC00174，FENDRR and ALMS1-IT1). Then, the risk model was confirmed applicable in both TCGA and GEO cohorts. Further verification, the signature can be verified as a independent prognostic factor for CRC. Based on the CCSC-related lncRNA model, the high- and low-risk groups exhibited different stemness statuses, including gene expression, mutation status, signaling pathways, TME and chemotherapy response. The HOX family and HOX4 were centrally located in the PPI interaction and had an influential contribution in CRC. Conclusions: We established a 4 CCSC-related lncRNA signature with a promising prognosis. And the signature can appropriately estimate the gene mutation, TME, and chemotherapy outcomes for CRC patients. Furthermore, the CCSC-related lncRNAs and HOX4 can serve as noble biomarkers and promote the management of therapy clinically.

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.

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. 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. 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. 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.

Purpose The objective of this study was to identify the potential regulatory mechanisms, diagnostic biomarkers, and therapeutic drugs for heart failure (HF). Methods Differentially expressed genes (DEGs) between HF and non-failing donors were screened from the GSE57345, GSE5406, and GSE3586 datasets. Database for Annotation Visualization and Integrated Discovery and Metascape were used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses respectively. The GSE57345 dataset was used for weighted gene co-expression network analysis (WGCNA). The intersecting hub genes from the DEGs and WGCNA were identified and verified with the GSE5406 and GSE3586 datasets. The diagnostic value of the hub genes was calculated through receiver operating characteristic analysis and net reclassification index (NRI). Gene set enrichment analysis (GSEA) was used to filter out the signaling pathways associated with the hub genes. SYBYL 2.1 was used for molecular docking of hub targets and potential HF drugs obtained from the connection map. Results Functional annotation of the DEGs showed enrichment of negative regulation of angiogenesis, endoplasmic reticulum stress response, and heart development. PTN, LUM, ISLR, and ASPN were identified as the hub genes of HF. GSEA showed that the key genes were related to the transforming growth factor-β (TGF-β) and Wnt signaling pathways. Sirolimus, LY-294002, and wortmannin have been confirmed as potential drugs for HF. Conclusion We identified new hub genes and candidate therapeutic drugs for HF, which are potential diagnostic, therapeutic and prognostic targets and warrant further investigation.

Histone deacetylases 1 (HDAC1), an enzyme that functions to remove acetyl molecules from ε-NH3 groups of lysine in histones, eliminates the histone acetylation at the promoter regions of tumor suppressor genes to block their expression during tumorigenesis. However, it remains unclear why HDAC1 fails to impair oncogene expression. Here we report that HDAC1 is unable to occupy at the promoters of oncogenes but maintains its occupancy with the tumor suppressors due to its interaction with CREPT (cell cycle-related and expression-elevated protein in tumor, also named RPRD1B), an oncoprotein highly expressed in tumors. We observed that CREPT competed with HDAC1 for binding to oncogene (such as CCND1, CLDN1, VEGFA, PPARD and BMP4) promoters but not the tumor suppressor gene (such as p21 and p27) promoters by a chromatin immunoprecipitation (ChIP) qPCR experiment. Using immunoprecipitation experiments, we deciphered that CREPT specifically occupied at the oncogene promoter via TCF4, a transcription factor activated by Wnt signaling. In addition, we performed a real-time quantitative PCR (qRT-PCR) analysis on cells that stably over-expressed CREPT and/or HDAC1, and we propose that HDAC1 inhibits CREPT to activate oncogene expression under Wnt signaling activation. Our findings revealed that HDAC1 functions differentially on tumor suppressors and oncogenes due to its interaction with the oncoprotein CREPT.

背景 食管胃结合部腺癌(adenocarcinoma of esophagogastric junction,AEG)的位置特殊,生物学行为不稳定,患者预后较差.脉管癌栓是影响AEG预后的独立危险因素,术前了解AEG脉管癌栓情况,有助于临床医生制定更加合理的个体化治疗方案.目的 探讨CT影像组学预测AEG脉管癌栓的应用价值.方法 选取2015年1月-2019年7月于解放军总医院第一医学中心普外二科诊断明确并接受手术治疗的79例AEG患者的CT影像图像,应用3D Slicer软件在静脉期CT影像最大病灶层面的图像上提取影像组学特征,并通过Lasso回归降维筛选出影像组学特征构建预测模型.计算患者的影像组学评分,将影像组学评分和临床指标作为参数构建列线图,通过受试者工作特征曲线(receiver operator characteristic curve,ROC)评价影像组学模型和列线图术前预测AEG脉管癌栓效能.结果 79例患者中,男性68例,女性11例,年龄30～80(63.8±9.5)岁.无脉管癌栓54例,有脉管癌栓25例,脉管癌栓的发生率为31.6％.在每例患者的增强CT图像中均提取873个特征,通过Lasso回归分析并10折交差验证最终确定2D平面最大直径等7个影像组学特征构建了影像组学模型,最大曲线下面积(area under the curve,AUC)达到0.852,敏感度为0.880,特异性为0.849.列线图的AUC为0.885,敏感度为0.880,特异性为0.892.随机抽样2/3样本行ROC分析,结果 与所建模型非常接近.结论 基于增强CT影像组学评分和临床指标的列线图对AEG脉管癌栓有较好的预测效能,可以为临床医生提供较准确的诊断和决策支持.

Although substantial achievements in the tumor microenvironment (TME) of hepatocellular carcinoma (HCC) have led to fundamental improvements both in the basic research and clinical management, the potential mechanisms and regulatory relationships between m6A regulators and the TME are still unknown. We first conducted unsupervised clustering on the samples according to the core m6A expression, and then compared the signaling pathways, differential genes (DEGs), and TME between the m6A phenotypes, and re-validated the relationship between m6A regulators and TME by single cell sequencing. Then, the geneCluster was obtained by another unsupervised clustering of the DEGs, and the clinical as well as TME traits were evaluated among the geneClusters. Finally, the m6A scores of individual patients were calculated by principal component analysis (PCA) to verify the correlation from multiple perspectives, including survivals, clinical characters, mutations, TME, immunotherapy, and chemotherapy. Through a comprehensive analysis of 729 samples, we classified HCC patients into three m6A clusters and three geneClusters. Each group exhibited remarkable variations in terms of signaling pathways, clinical traits, and survival expectations. Notably, the m6A phenotypes corresponded to three different types of TME, namely immune-inflamed, immune-excluded, and immune-desert, respectively. In addition, the m6A regulator can accurately reflect the individualized microenvironment in HCC, and present supreme expression levels in the stromal microenvironment. However, the m6A score system is able to make accurate predictions not only in terms of clinical traits, survival prediction, and TME mentioned above, but also in the sensitivity of HCC patients to immunotherapy and chemotherapy. This study revealed the uniqueness and pluripotency of m6A regulators in the TME of HCC by combining single-cell sequencing and bulk sequencing. The quantified m6A modification indices were able to accurately predict patient survival expectations, clinical traits, TME, and sensitivity to immunotherapy and chemotherapy.

Abstract Background: To characterize the anatomical subtypes of ureteropelvic junction obstruction (UPJO) caused by crossing vessels (CVs) and demonstrate the Individualized operation procedures for these cases.Methods: From March 2015 to July 2019, 51 consecutive adult patients underwent treatment of primary UPJO via a retroperitoneal laparoscopic approach. The clinical data, iconography inspection results, and surgical procedures for each patient were retrospectively reviewed by our team. The diagnosis of etiological CV was confirmed during the operation in 13 patients (25.49%), which included 7 men and 6 women. Results: The mean surgical age was 30±11.66 years. The operating time was approximately 233±62.76 minutes, and there were one open conversions. In the follow-up period (mean, 27.23±15.46 months), all patients had a full recovery in the CV group. However, 3 patients without CV did not completely recover from uronephrosis, as determined on iconography inspection, and there was no improvement in the renal colic symptoms. In the CV group, none of the patients had lithiasis whereas 25% of the patients without CV had lithiasis. Conclusion: CV accounts for approximately 25.49% of the UPJO cases. Based on the anatomical position of the UPJ and CVs, we identified two types of abnormalities, and 84.62% of the CVs were located anterior to the UPJ. The retroperitoneal approach for treating CVs had particular advantages. A comprehensive understanding and interoperative analysis of the anastomosis between the CVs and UPJ is crucial for at least 4 individual treatments. After dismembered pyeloplasty, suspension of the CVs is recommended in approximately 40% of the cases. The follow-up showed good prognosis in the long term.

Prostate cancer (PCa) is a highly malignant tumor, with increasing incidence and mortality rates worldwide. The aim of this study was to identify the prognostic lncRNAs and construct an lncRNA signature for PCa diagnosis by the interaction network between lncRNAs and protein-coding genes (PCGs). The differentially expressed lncRNAs (DElncRNAs) and PCGs (DEPCGs) between PCa and normal prostate tissues were screened from The Cancer Genome Atlas (TCGA) database. The DEPCGs were functionally annotated in terms of the enriched pathways. Weighted gene co-expression network analysis (WGCNA) of 104 PCa samples identified 15 co-expression modules, of which the Turquoise module was negatively correlated with cancer and included 5 key lncRNAs and 47 PCGs. KEGG pathway analyses of the core 47 PCGs showed significant enrichment in classic PCa-related pathways, and overlapped with the enriched pathways of the DEPCGs. LINC00857, LINC00900, LINC00908, LINC00900, SNHG3 and FENDRR were significantly associated with the survival of PCa and have not been reported previously. Finally, Multivariable Cox regression analysis was used to establish a prognostic risk formula, and the patients were accordingly stratified into the low- and high-risk groups. The latter had significantly worse OS compared to the low-risk group (P < 0.01), and the area under the receiver operating characteristic curve (ROC) of 14-year OS was 0.829. The accuracy of our prediction model was determined by calculating the corresponding concordance index (C-index) and risk curves. In conclusion, we established a 5-lncRNA prognostic signature that provides insights into the biological and clinical relevance of lncRNAs in PCa.