Multi-omics analysis of macrophage-associated receptor and ligand reveals a strong prognostic signature and subtypes in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a significant contributor to morbidity and mortality worldwide. The interaction between receptors and ligands is the primary mode of intercellular signaling and plays a vital role in the progression of HCC. This study aimed to identify the macrophage-related receptor ligand marker genes associated with HCC and further explored the molecular immune mechanisms attributed to altered biomarkers. Single-cell RNA sequencing data containing primary and recurrent samples were downloaded from the China National GeneBank. Cell types were first identified to explore differences between immune cells from different sample sources. CellChat analysis was used to infer and analyze intercellular communication networks quantitatively. Three molecular subtypes were constructed based on the screened twenty macrophage-associated receptor ligand genes. Bulk RNA-Seq data were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. After the screening, the minor absolute shrinkage and selection operator (LASSO) regression model was employed to identify key markers. After collecting peripheral blood and clinical information from patients, an enzyme-linked immunosorbent assay (ELISA) was used to detect the correlation between key markers and IL-10, one of the macrophage markers. After developing a new HCC risk adjustment model and conducting analysis, it was found that there were significant differences in immune status and gene mutations between the high-risk and low-risk groups of patients based on macrophage-associated receptor and ligand genes. This study identified SPP1, ANGPT2, and NCL as key biological targets for HCC. The drug-gene interaction network analysis identified wortmannin, ribavirin, and tarnafloxin as potential therapeutic drugs for the three key markers. In a clinical cohort study, patients with immune checkpoint inhibitor (ICI) resistance had significantly higher expression levels of OPN, ANGPT2, NCL, and IL-10 than patients with ICI-responsiveness. These three key markers were positively correlated with the expression level of IL-10. The signature based on macrophage-associated receptor and ligand genes can accurately predict the prognosis of patients with HCC and the sensitivity to immunotherapy. These results may help guide the development of targeted prevention and personalized treatment of HCC.

Simultaneous SERS Sensing of Cysteine and Homocysteine in Blood Based on the CBT-Cys Click Reaction: Toward Precisive Diagnosis of Schizophrenia.

At present, there is a lack of sufficiently specific laboratory diagnostic indicators for schizophrenia. Serum homocysteine (Hcy) levels have been found to be related to schizophrenia. Cysteine (Cys) is a demethylation product in the metabolism of Hcy, and they always coexist with highly similar structures in vivo. There are few reports on the use of Cys as a diagnostic biomarker for schizophrenia in collaboration with Hcy, mainly because the rapid, economical, accurate, and high-throughput simultaneous detection of Cys and Hcy in serum is highly challenging. Herein, a click reaction-based surface-enhanced Raman spectroscopy (SERS) sensor was developed for simultaneous and selective detection of Cys and Hcy. Through the efficient and specific CBT-Cys click reaction between the probe containing cyan benzothiazole and Cys/Hcy, the tiny methylene difference between the molecular structures of Cys and Hcy was converted into the difference between the ring skeletons of the corresponding products that could be identified by plasmonic silver nanoparticle enhanced molecular fingerprint spectroscopy to realize discriminative detection. Furthermore, the SERS sensor was successfully applied to the detection in related patient serum samples, and it was found that the combined analysis of Cys and Hcy can improve the diagnostic accuracy of schizophrenia compared to a single indicator.

A pan-cancer analysis of anti-proliferative protein family genes for therapeutic targets in cancer.

The recently discovered APRO (anti-proliferative protein) family encodes a group of trans-membrane glycoproteins and includes 6 members: TOB1, TOB2, BTG1, BTG2, BTG3 and BTG4. The APRO family is reportedly associated with the initiation and progression of cancers. This study aims to undertake a comprehensive investigation of the APRO family of proteins as a prognostic biomarker in various human tumors. We performed a pan-cancer analysis of the APRO family based on The Cancer Genome Atlas (TCGA). With the bioinformatics methods, we explored the prognostic value of the APRO family and the correlation between APRO family expression and tumor mutation burden (TMB), microsatellite instability (MSI), drug sensitivity, and immunotherapy in numerous cancers. Our results show that the APRO family was primarily down-regulated in cancer samples. The expression of APRO family members was linked with patient prognosis. In addition, APRO family genes showed significant association with immune infiltrate subtypes, tumor microenvironment, and tumor cell stemness. Finally, our study also demonstrated the relationship between APRO family genes and drug sensitivity. This study provides comprehensive information to understand the APRO family's role as an oncogene and predictor of survival in some tumor types.

ATRX is a predictive marker for endocrinotherapy and chemotherapy resistance in HER2-/HR+ breast cancer through the regulation of the AR, GLI3 and GATA2 transcriptional network.

Drug resistance in breast cancer (BC) is a clinical challenge. Exploring the mechanism and identifying a precise predictive biomarker for the drug resistance in BC is critical. Three first-line drug (paclitaxel, doxorubicin and tamoxifen) resistance datasets in BC from GEO were merged to obtain 1,461 differentially expressed genes for weighted correlation network analysis, resulting in identifying ATRX as the hub gene. ATRX is a chromatin remodelling protein, therefore, ATRX-associated transcription factors were explored, thereby identifying the network of AR, GLI3 and GATA2. GO and KEGG analyses revealed immunity, transcriptional regulation and endocrinotherapy/chemotherapy resistance were enriched. Moreover, CIBERSORT revealed immunity regulation was inhibited in the resistance group. ssGSEA showed a significantly lower immune status in the ATRX-Low group compared to the ATRX-High group. Furthermore, the peaks of H3K9me3 ChIP-seq on the four genes were higher in normal tissues than in BC tissues. Notably, the frequency of ATRX mutation was higher than BRCA in BC. Moreover, depressed ATRX revealed worse overall survival and disease-free survival in the human epidermal growth factor receptor 2 (HER2)-/hormone receptor (HR)+ BC. Additionally, depressed ATRX predicted poor results for patients who underwent endocrinotherapy or chemotherapy in the HER2-/HR+ BC subgroup. A nomogram based on ATRX, TILs and ER exhibited a significantly accurate survival prediction ability. Importantly, overexpression of ATRX significantly inhibited the IC50 of the three first-line drugs on MCF-7 cell. Thus, ATRX is an efficient predictive biomarker for endocrinotherapy and chemotherapy resistance in HER2-/HR+ BC and acts by suppressing the AR, GLI3 and GATA2 transcriptional network.

Raman encoding for security labels: a review.

Owing to its excellent multiplexing ability, high stability, and molecular fingerprint characteristics, Raman encoding has been widely used in security labels for medical safety, jewelry identification and food supervision. Various growing demands have promoted the anti-counterfeiting mode of security labels based on Raman encoding from the classic one that relies on specific patterns to the more secure one that depends on random patterns. As impressive progress has been made in Raman encoding for security labels in recent years, this review attempts to comprehensively cover security labels based on Raman encoding, from label preparation to image verification. For the labels with different anti-counterfeiting modes, the different basic elements they need are summarized, and the role of Raman encoding in different modes is introduced. In addition, security labels based on Raman encoding still have some drawbacks. Therefore, suggestions on how to improve its anti-counterfeiting performance are also discussed, as well as future challenges and prospects.

"Dramatic Growth" of Microbial Aerosols for Visualization and Accurate Counting of Bioaerosols.

While the global COVID-19 pandemic has subsided, microbial aerosol detection has become of high concern. Timely, accurate, and highly sensitive monitoring of microbial aerosols in indoor air is the basis for effective prevention and control of infectious diseases. At present, no commercial equipment or reliable technology can simultaneously control the detection time and limit at 6 h and 102 CFU/mL, respectively. Based on the "safety size range" of particulate matter in the air, we propose a new method of microbial dilation detection, which enables the pathogen to grow rapidly and dramatically into a polymeric microsphere, larger in size than the coexisting aerosol particles. "Like a crane standing among chickens", the microorganism can be easily visualized and counted. Different from routine chemical and biological sensing technologies, this method can achieve absolute counting of microbial particles, and the simple principles can be developed into devices for different life scenarios.

A cooling-driven self-adaptive and removable hydrogel coupled with combined chemo-photothermal sterilization for promoting infected wound healing.

Hydrogel dressings that can fit irregular wounds, promote wound healing, and detach from wounds without damage represent the development trend of modern medical dressings. Herein, a novel composite hydrogel with excellent wound shape matching and painless removability via a gel-sol phase transition is constructed through dynamic borate ester bonds between phenylboronic acid-grafted F127 (PF127) and polydopamine-coated reduced graphene oxide/silver nanoparticles (rGO@PDA/Ag NPs). After contact with the skin tissues, the administered liquid-like sols gradually transform into solid-like gels, robustly adhering to the wound. The hydrogel dressings containing near-infrared (NIR)-responsive rGO@PDA and in situ formed Ag NPs can generate localized heat and gradually release Ag+ to realize safe, effective, and durable photothermal-chemical combined sterilization. In addition, catechol-rich PDA endows the hydrogel dressings with good antioxidant activity and adhesiveness. In vivo study results indicate that the hydrogel dressings can significantly accelerate full-thickness skin infected wound healing by eliminating bacteria, promoting collagen deposition and angiogenesis, as well as reducing inflammation. Collectively, the thermoreversible rGO@PDA/Ag-PF127 hydrogel dressings with an improved self-adapting ability, superior antimicrobial activity, and tunable adhesion appear to be a promising candidate for the treatment of infected wounds.

Cancer survival: left truncation and comparison of results from hospital-based cancer registry and population-based cancer registry.

Background: Cancer survival is an important indicator for evaluating cancer prognosis and cancer care outcomes. The incidence dates used in calculating survival differ between population-based registries and hospital-based registries. Studies examining the effects of the left truncation of incidence dates and delayed reporting on survival estimates are scarce in real-world applications. Methods: Cancer cases hospitalized at Nantong Tumor Hospital during the years 2002-2017 were traced with their records registered in the Qidong Cancer Registry. Survival was calculated using the life table method for cancer patients with the first visit dates recorded in the hospital-based cancer registry (HBR) as the diagnosis date (OSH), those with the registered dates of population-based cancer (PBR) registered as the incidence date (OSP), and those with corrected dates when the delayed report dates were calibrated (OSC). Results: Among 2,636 cases, 1,307 had incidence dates registered in PBR prior to the diagnosis dates of the first hospitalization registered in HBR, while 667 cases with incidence dates registered in PBR were later than the diagnosis dates registered in HBR. The 5-year OSH, OSP, and OSC were 36.1%, 37.4%, and 39.0%, respectively. The "lost" proportion of 5-year survival due to the left truncation for HBR data was estimated to be between 3.5% and 7.4%, and the "delayed-report" proportion of 5-year survival for PBR data was found to be 4.1%. Conclusion: Left truncation of survival in HBR cases was demonstrated. The pseudo-left truncation in PBR should be reduced by controlling delayed reporting and maximizing completeness. Our study provides practical references and suggestions for evaluating the survival of cancer patients with HBR and PBR.

Blood-Glucose-Depleting Hydrogel Dressing as an Activatable Photothermal/Chemodynamic Antibacterial Agent for Healing Diabetic Wounds.

Poorly healing and nonhealing diabetic wounds are challenging to treat as the rapid growth of bacteria due to the high local glucose content can lead to persistent inflammation and poor angiogenesis. Herein, a smart hydrogel dressing composed of 3,3',5,5'-tetramethylbenzidine/ferrous ion/Pluronic F-127/glucose oxidase (TMB/Fe2+/PF127/GOx) is designed and demonstrated to consume blood glucose while accelerating wound healing by generating antibacterial agents in situ. The loaded GOx degrades blood glucose to provide hydrogen peroxide (H2O2) and gluconic acid to support the Fe2+-based Fenton reaction, and the generated hydroxyl radical (·OH) facilitates the oxidation of TMB. The color change from colorless to green caused by the oxidation of TMB in the blood glucose range between 1 and 10 mM can be monitored visually. Simultaneously, this process induced chemodynamic therapy (CDT) by the specific generation of hydroxyl radical (·OH) for killing bacteria. Moreover, the oxidized TMB shows strong absorption in the near infrared (NIR) region so that NIR light can be converted into heat efficiently for photothermal therapy (PTT). As a result, nearly 100% of Staphylococcus aureus and Escherichia coli are killed by synergistic PTT/CDT, and the infected skin wounds undergo complete repair along with downregulation of interleukin-6 (IL-6) and upregulation of the vascular endothelial growth factor (VEGF) and matrix metallopeptidase-2 (MMP-2). Different from traditional wound dressings that can give rise to secondary injury, the excellent thermosensitive properties arising from the sol/gel phase transition render the hydrogel dressing materials injectable, self-reparable, and removable on demand. The multifunctional hydrogel with hypoglycemic, chemodynamic, photothermal, antibacterial, and on-demand thermosensitive properties has immense potential in the treatment of diabetic wounds.

Succinylation-associated lncRNA signature to predict the prognosis of colon cancer based on integrative bioinformatics analysis.

Colon cancer (CC) has a poor 5-year survival rate though the treatment techniques and strategies have been improved. Succinylation and long noncoding RNAs (lncRNAs) have prognostic value for CC patients. We analyzed and obtained succinylation-related lncRNA by co-expression in CC. A novel succinylation-related lncRNA model was developed by univariate and Least absolute shrinkage and selection operator (Lasso) regression analysis and we used principal component analysis (PCA), functional enrichment annotation, tumor immune environment, drug sensitivity and nomogram to verify the model, respectively. Six succinylation-related lncRNAs in our model were finally confirmed to distinguish the survival status of CC and showed statistically significant differences in training set, testing set, and entire set. The prognosis of with this model was associated with age, gender, M0 stage, N2 stage, T3 + T4 stage and Stage III + IV. The high-risk group showed a higher mutation rate than the low-risk group. We constructed a model to predict overall survival for 1-, 3-, and 5-year with AUCs of 0.694, 0.729, and 0.802, respectively. The high-risk group was sensitive to Cisplatin and Temozolomide compounds. Our study provided novel insights into the value of the succinylation-related lncRNA signature as a predictor of prognosis, which had high clinical application value in the future.

Engineering a SERS Sensing Nanoplatform with Self-Sterilization for Undifferentiated and Rapid Detection of Bacteria.

The development of a convenient, sensitive, rapid and self-sterilizing biosensor for microbial detection is important for the prevention and control of foodborne diseases. Herein, we designed a surface-enhanced Raman scattering (SERS) sensing nanoplatform based on a capture-enrichment-enhancement strategy to detect bacteria. The gold-Azo@silver-cetyltrimethylammonium bromide (Au-Azo@Ag-CTAB) SERS nanotags were obtained by optimizing the synthesis process conditions. The results showed that the modification of CTAB enabled the nanotags to bind to different bacteria electrostatically. This SERS sensing nanoplatform was demonstrated to be fast (15 min), accurate and sensitive (limit of detection (LOD): 300 and 400 CFU/mL for E. coli and S. aureus, respectively). Of note, the excellent endogenous antibacterial activity of CTAB allowed the complete inactivation of bacteria after the assay process, thus effectively avoiding secondary contamination.

Silent Raman imaging of highly effective anti-bacterial activity synchronous with biofilm breakage using poly(4-cyanostyrene)@silver@polylysine nanocomposites.

Biofilms are known to be a great challenge for their anti-bacterial activity as they obstruct drug action for deeper and more thorough bacteria-killing effects. Therefore, developing highly effective antibacterial agents to destroy biofilms and eradicate bacteria is of great significance. Herein, a new type of nanocomposites (denoted as poly(4-cyanostyrene)@silver@polylysine) is proposed, in which polylysine (PLL) could rapidly capture the biofilms and exhibit excellent antibacterial efficacy together with decorated silver (Ag) nanoparticles (NPs) through the charge effect and Ag+ release. Notably, nearly 100% antibacterial rates against Gram-positive bacterium (Staphylococcus aureus, S. aureus) and Gram-negative bacterium (Escherichia coli, E. coli) were achieved. More importantly, poly(4-cyanostyrene) with biological silent Raman imaging capacity is able to illustrate the relationship between antibacterial efficiency and biofilm breakage. In short, such novel nanocomposites can improve the bioavailability of each component and display tremendous potential in antibacterial applications.

Significance of Spliceosome-Related Genes in the Prediction of Prognosis and Treatment Strategies for Lung Adenocarcinoma.

Background: The leading cause of cancer-related fatalities globally is lung cancer; lung adenocarcinoma (LUAD) is the most common histological type in it. The spliceosome plays an important role in a majority of malignancies. However, it is yet unclear how spliceosome-related genes affect patients with LUAD in terms of treatment course and prognosis. Methods: Spliceosome-related genes were assessed from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database to obtain clinical information and gene expression in patients with LUAD. A spliceosome-related gene signature and prognostic model were constructed by using the least absolute shrinkage and selection operator (LASSO), time-dependent receiver operating characteristic (ROC), and nomogram. Immune infiltrate levels, mutation analysis, and pathway enrichment were predicted potential mechanisms of the signature by using single-sample gene set enrichment analysis (ssGSEA), Gene Set Cancer Analysis (GSCA) database, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) database. Then, a protein-protein interaction (PPI) network and transcription factor- (TF-) hub gene and drug mining network were also established by Cytoscape software. Results: Firstly, we constructed a prognostic model for 11 spliceosome signature genes. Based on the prognostic risk score, we stratified patients with LUAD into high- and low-risk groups. The high- and low-risk groups were closely related to the OS, tumor immune infiltration level, immune checkpoint molecules, and tumor mutation burden (TMB) of LUAD patients. Based on PPI networks, we also predict relevant TF genes that may regulate signature prognostic genes. Finally, drugs including oxaliplatin, arsenic trioxide, cisplatin, and sunitinib were excavated for the treatment of the 11 spliceosome signature genes in LUAD patients. Conclusion: In conclusion, this study is the first to explore the importance of spliceosome-related genes in the prognosis and treatment of LUAD. Through our study, we have innovatively provided potential prognosis genes and new therapeutic drug targets for the treatment of LUAD patients.

Serum alanine aminotransferase to hemoglobin ratio and radiological features predict the prognosis of postoperative adjuvant TACE in patients with hepatocellular carcinoma.

Objective: To explore the prognostic value of radiological features and serum indicators in patients treated with postoperative adjuvant transarterial chemoembolization (PA-TACE) and develop a prognostic model to predict the overall survival (OS) of patients with hepatocellular carcinoma (HCC) treated with PA-TACE. Method: We enrolled 112 patients (75 in the training cohort and 37 in the validation cohort) with HCC treated with PA-TACE after surgical resection at the Affiliated Hospital of Nantong University between January 2012 and June 2015. The independent OS predictors were determined using univariate and multivariate regression analyses. Decision curve analyses and time-dependent receiver operating characteristic curve analysis was used to verify the prognostic performance of the different models; the best model was selected to establish a multi-dimensional nomogram for predicting the OS of HCC patients treated with PA-TACE. Result: Multivariate regression analyses indicated that rim-like arterial phase enhancement (IRE), peritumor capsule (PTC), and alanine aminotransferase to hemoglobin ratio (AHR) were independent predictors of OS after PA-TACE. The combination of AHR had the best clinical net benefit and we constructed a prognostic nomogram based on IRE, PTC, and AHR. The calibration curve showed good fit between the predicted nomogram's curve and the observed curve. Conclusion: Our preliminary study confirmed the prognostic value of AHR, PTC, and IRE and established a nomogram that can predict the OS after PA-TACE treatment in patients with HCC.

Development and Validation of a Novel Circadian Rhythm-Related Signature to Predict the Prognosis of the Patients with Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most important causes of cancer-related deaths and remains a major public health challenge worldwide. Considering the extensive heterogeneity of HCC, more accurate prognostic models are imperative. The circadian genes regulate the daily oscillations of key biological processes, such as nutrient metabolism in the liver. Circadian rhythm disruption has recently been recognized as an independent risk factor for cancer. In this study, The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were compared and 248 differentially expressed genes (DEGs) of the circadian rhythm were identified. HCC was classified into two subtypes based on these DEGs. The prognostic value of each circadian rhythm-associated gene (CRG) for survival was assessed by constructing a multigene signature from TCGA cohort. A 6-gene signature was created by applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, and all patients in TCGA cohort were divided into high- and low-risk groups according to their risk scores. The survival rate of patients with HCC in the low-risk group was significantly higher than that in the high-risk group (p < 0.001). The patients with HCC in the Gene Expression Omnibus (GEO) cohort were also divided into two risk subgroups using the risk score of TCGA cohort, and the overall survival time (OS) was prolonged in the low-risk group (p = 0.012). Based on the clinical characteristics, the risk score was an independent predictor of OS in the patients with HCC. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that multiple metabolic pathways, cell cycle, etc., were enhanced in the high-risk group. Using the metabolic pathway single-sample gene set enrichment analysis (ssGSEA), it was found that the metabolic pathways in the high- and low-risk groups between TCGA and GEO cohorts were altered essentially in the same way. In conclusion, the circadian genes play an important role in HCC metabolic rearrangements and can be further used to predict the prognosis the patients with HCC.

Pan-Cancer Analysis Predicts the Immunological and Prognostic Role of ZC3H12C in KIRC.

ZC3H12C is an important member of the CCCH-zinc finger protein family and is mainly involved in host immune and inflammatory diseases. However, its abnormal expression and prognostic value in cancer have not yet been established. Through comparative analysis of the Cancer Genome Atlas (TCGA) database, we found that ZC3H12C is the most relevant to the prognosis, grade, and stage of renal clear cell carcinoma (ccRCC) across 33 cancers. With the help of patient transcription and clinical data from the TCGA and GEO (GSE53757, GSE36895) databases, we determined that in the immune environment of patients with ccRCC, ZC3H12C was clearly negatively correlated with Tregs and was significantly positively correlated with monocytes. In addition, protein phosphorylation and DNA methylation analysis also showed that ZC3H12C negatively regulates the role of cancer in ccRCC. Our research may provide new insights into ccRCC immunotherapy and bring forth novel biomarkers and therapeutic targets.

Laser tweezers Raman spectroscopy combined with machine learning for diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is a common nervous system disease to affect mostly elderly people over the age of 65 years. However, the diagnosis of AD is mainly depend on the imaging examination, clinical assessments and neuropsychological tests, which may get error diagnosis results and are not able to detect early AD. Here, a rapid, non-invasive, and high accuracy diagnostic method for AD especially early AD is provided based on the laser tweezers Raman spectroscopy (LTRS) combined with machine learning algorithms. AD platelets from different 3xTg-AD transgenic rats at different stages of disease are captured to collect high signal-to-noise ratio Raman signals without contact by LTRS, which is then combined with partial least squares discriminant analysis (PLS-DA), support vector machine (SVM) and principal component analysis (PCA)-canonical discriminate function (CDA) for classification. The results show that the normal and diseased platelets at 3-, 6- and 12-month AD are successfully distinguished and the accuracy is 91%, 68% and 97% respectively, which demonstrates the suggested method can provide a precise detection for AD diagnosis at early, middle and advanced stages.

Based on the Development and Verification of a Risk Stratification Nomogram: Predicting the Risk of Lung Cancer-Specific Mortality in Stage IIIA-N2 Unresectable Large Cell Lung Neuroendocrine Cancer Compared With Lung Squamous Cell Cancer and Lung Adenocarcinoma.

Background: The purpose of this study is to predict overall survival (OS) and lung cancer-specific survival (LCSS) in patients with stage IIIA-N2 unresectable lung squamous cell cancer (LUSC), lung adenocarcinoma (LUAD), and large cell neuroendocrine cancer (LCNEC) by constructing nomograms and to compare risk and prognostic factors affecting survival outcomes in different histological subtypes. Methods: We included 11,505 unresectable NSCLC patients at stage IIIA-N2 between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Moreover, competition models and nomograms were developed to predict prognostic factors for OS and LCSS. Results: Analysis of the SEER database identified 11,505 NSCLC patients, of whom 5,559 (48.3%) have LUAD, 5,842 (50.8%) have LUSC, and 104 (0.9%) have LCNEC. Overall, both OS and LCSS were significantly better in stage IIIA-N2 unresectable LUAD than in LCNEC, while there was no statistically significant difference between LUSC and LCNEC. Age, gender, T stage, chemotherapy, and radiotherapy were significantly associated with OS rates in LUAD and LUSC. However, chemotherapy was the only independent factor for LCNEC (p < 0.01).From competitive risk models, we found that older age, larger tumors, non-chemotherapy and non-radiotherapy were associated with a increased risk of death from LUAD and LUSC. Unlike prognostic factors for OS, our study showed that both chemotherapy and radiotherapy were all LCNEC-specific survival factors for both LCSS and non-LCSS LCNEC. Conclusion: Our study reports that unresectable patients with stage IIIA-N2 LCNEC and LUSC have worse LCSS than LUAD. The study's first prognostic nomogram constructed for patients with unresectable stage IIIA-N2 NSCLC can accurately predict the survival of different histological types, which may provide a practical tool to help clinicians assess prognosis and stratify these prognostic risks to determine which patients should be given an optimized individual treatment strategy based on histology.

Predictive value of indicator of CA125 combined with D-dimer (ICD) for lymph node metastasis in patients with ovarian cancer: A two center cohort study.

Background: The clinical serum markers CA125 and D-dimer have been reported to predict lymph node metastasis(LNM) in several malignant tumors, but the reports in ovarian cancer(OC) are still absent. The purpose of this study was to explore the value of indicator CA125 combined with D-dimer (ICD) in predicting LNM in patients with OC. Methods: A total of 447 patients diagnosed with OC from January 2008 to June 2019 were included in this retrospective study as the training set. A total of 284 patients were included in the validation set. The optimal cut-off critical value of ICD was evaluated by the receiver operating characteristic curve (ROC), and the maximum Youden index (sensitivity + specificity-1). Univariate and multivariate analysis were used to evaluate ICD as a predictor of LNM in OC. Results: According to ROC curve, area under curve (AUC) of ICD (AUC=0.706, p<0.001) was significantly larger than that of CA125 (AUC=0.671, p<0.001) and D-dimer (AUC=0.562, p=0.022) alone. Multivariate analysis showed that ICD (HR 2.651, 95% CI 1.273-5.520, p=0.009) was an independent predictor of LNM and overall survival (OS) in OC. It has also been verified in another medical center. Conclusion: ICD is an independent predictor of LNM in ovarian cancers, which is helpful for clinicians to draw up individual treatment plans.

Identification and Validation of Necroptosis-Related LncRNA Signature in Hepatocellular Carcinoma for Prognosis Estimation and Microenvironment Status.

Background: Hepatocellular carcinoma (HCC) is among malignancies with the highest fatality toll globally and minimal therapeutic options. Necroptosis is a programmed form of necrosis or inflammatory cell death, which can affect prognosis and microenvironmental status of HCC. Therefore, we aimed to explore the prognostic value of necroptosis-related lncRNAs (NRLs) in HCC and the role of the tumor microenvironment (TME) in immunotherapy. Methods: The RNA-sequencing data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). NRLs were identified by Pearson correlation analysis. The signature was constructed using the LASSO-Cox regression analysis and evaluated using the receiver operating characteristic curve (ROC) and the area under the Kaplan-Meier curve. The nomogram was built based on clinical information and risk score. Gene set enrichment analysis (GSEA), immunoassay, half-maximum inhibitory concentration (IC50) analysis of the risk group, and the HCC subtype identification based on NRLs were also carried out. Finally, we detected the expression of lncRNAs in HCC tissues and cell lines in vitro. Results: A total of 508 NRLs were screened out, and seven NRLs were constructed as a risk stratification system to classify patients into distinct low- and high-risk groups. Patients in the high-risk group had a significantly lower overall survival (OS) than those in the low-risk group. Using multivariate Cox regression analysis, we found that the risk score was an independent predictor of OS. Functional analysis showed that the immune status of different patients was different. The IC50 analysis of chemotherapy demonstrated that patients in the high-risk group were more sensitive to commonly prescribed drugs. qRT-PCR showed that three high-risk lncRNAs were upregulated in drug-resistant cells, and the expression in HCC tissues was higher than that in adjacent tissues. Conclusion: The prediction signature developed in this study can be used to assess the prognosis and microenvironment of HCC patients, and serve as a new benchmark for HCC treatment selection.