S100A16 is a potential target for reshaping the tumor microenvironment in the hypoxic context of liver cancer.

BACKGROUND: The research on the S100 family has garnered significant attention; however, there remains a dearth of understanding regarding the precise role of S100A16 in the tumor microenvironment of liver cancer. METHOD: Comprehensive analysis was conducted on the expression of S100A16 in tumor tissues and its correlation with hypoxia genes. Furthermore, an investigation was carried out to examine the association between S100A16 and infiltration of immune cells in tumors as well as immunotherapy. Relevant findings were derived from the analysis of single cell sequencing data, focusing on the involvement of S100A16 in both cellular differentiation and intercellular communication. Finally, we validated the expression of S100A16 in liver cancer by Wuhan cohort and multiplexed immunofluorescence to investigate the correlation between S100A16 and hypoxia. RESULT: Tumor tissues displayed a notable increase in the expression of S100A16. A significant correlation was observed between S100A16 and genes associated with hypoxic genes. Examination of immune cell infiltration revealed an inverse association between T cell infiltration and the level of S100A16 expression. The high expression group of S100A16 exhibited a decrease in the expression of genes related to immune cell function. Single-cell sequencing data analysis revealed that non-immune cells predominantly expressed S100A16, and its expression levels increased along with the trajectory of cell differentiation. Additionally, there were significant variations observed in hypoxia genes as cells underwent differentiation. Cellular communication identified non-immune cells interacting with immune cells through multiple signaling pathways. The Wuhan cohort verified that S100A16 expression was increased in liver cancer. The expression of S100A16 and HIF was simultaneously elevated in endothelial cells. CONCLUSION: The strong association between S100A16 and immune cell infiltration is observed in the context of hypoxia, indicating its regulatory role in shaping the hypoxic tumor microenvironment in liver cancer.

Single-cell and bulk RNA sequencing analysis of B cell marker genes in TNBC TME landscape and immunotherapy.

Objective: This study amied to investigate the prognostic characteristics of triple negative breast cancer (TNBC) patients by analyzing B cell marker genes based on single-cell and bulk RNA sequencing. Methods: Utilizing single-cell sequencing data from TNBC patients, we examined tumor-associated B cell marker genes. Transcriptomic data from The Cancer Genome Atlas (TCGA) database were used as the foundation for predictive modeling. Independent validation set was conducted using the GSE58812 dataset. Immune cell infiltration into the tumor was assessed through various, including XCELL, TIMER, QUANTISEQ, CIBERSORT, CIBERSORT-ABS, and ssGSEA. The TIDE score was utilized to predict immunotherapy outcomes. Additional investigations were conducted on the immune checkpoint blockade gene, tumor mutational load, and the GSEA enrichment analysis. Results: Our analysis encompassed 22,106 cells and 20,556 genes in cancerous tissue samples from four TNBC patients, resulting in the identification of 116 B cell marker genes. A B cell marker gene score (BCMG score) involving nine B cell marker genes (ZBP1, SEL1L3, CCND2, TNFRSF13C, HSPA6, PLPP5, CXCR4, GZMB, and CCDC50) was developed using TCGA transcriptomic data, revealing statistically significant differences in survival analysis (P<0.05). Functional analysis demonstrated that marker genes were predominantly associated with immune-related pathways. Notably, substantial differences between the higher and lower- BCMG score groups were observed in terms of immune cell infiltration, immune cell activity, tumor mutational burden, TIDE score, and the expression of immune checkpoint blockade genes. Conclusion: This study has established a robust model based on B-cell marker genes in TNBC, which holds significant potential for predicting prognosis and response to immunotherapy in TNBC patients.

Analysis of M2 macrophage-associated risk score signature in pancreatic cancer TME landscape and immunotherapy.

Background: M2 macrophages perform an influential role in the progression of pancreatic cancer. This study is dedicated to explore the value of M2 macrophage-related genes in the treatment and prognosis of pancreatic cancer. Methods: RNA-Seq and clinical information were downloaded from TCGA, GEO and ICGC databases. The pancreatic cancer tumour microenvironment was revealed using the CIBERSORT algorithm. Weighted gene co-expression network analysis (WGCNA) was used to detect M2 macrophage-associated gene modules. Univariate Cox regression, Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression were applied to develop the prognostic model. The modelling and validation cohorts were divided into high-risk and low-risk groups according to the median risk score. The nomogram predicting survival was constructed based on risk scores. Correlations between risk scores and tumour mutational load, clinical variables, immune checkpoint blockade, and immune cells were further explored. Finally, potential associations between different risk models and chemotherapeutic agent efficacy were predicted. Results: The intersection of the WGCNA results from the TCGA and GEO data screened for 317 M2 macrophage-associated genes. Nine genes were identified by multivariate COX regression analysis and applied to the construction of risk models. The results of GSEA analysis revealed that most of these genes were related to signaling, cytokine receptor interaction and immunodeficiency pathways. The high and low risk groups were closely associated with tumour mutational burden, immune checkpoint blockade related genes, and immune cells. The maximum inhibitory concentrations of metformin, paclitaxel, and rufatinib lapatinib were significantly differences on the two risk groups. Conclusion: WGCNA-based analysis of M2 macrophage-associated genes can help predict the prognosis of pancreatic cancer patients and may provide new options for immunotherapy of pancreatic cancer.

New treatment insights into pancreatic acinar cell carcinoma: case report and literature review.

Pancreatic acinar cell carcinoma (PACC) is a rare pancreatic malignancy with unique clinical, molecular, and morphologic features. The long-term survival of patients with PACC is substantially better than that of patients with ductal adenocarcinoma of the pancreas. Surgical resection is considered the first choice for treatment; however, there is no standard treatment option for patients with inoperable disease. The patient with metastatic PACC reported herein survived for more than 5 years with various treatments including chemotherapy, radiotherapy, antiangiogenic therapy and combined immunotherapy.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on NK cell marker genes to predict prognosis and immunotherapy response in hepatocellular carcinoma.

BACKGROUND: Prognostic modeling of NK cell marker genes in patients with hepatocellular carcinoma based on single cell sequencing and transcriptome data analysis. METHODS: Marker genes of NK cells were analyzed according to single cell sequencing data of hepatocellular carcinoma. Univariate Cox regression, lasso regression analysis, and multivariate Cox regression were performed to estimate the prognostic value of NK cell marker genes. TCGA, GEO and ICGC transcriptomic data were applied to build and validate the model. Patients were divided into high and low risk groups based on the median risk score. XCELL, timer, quantitative sequences, MCP counter, EPIC, CIBERSORT and CIBERSORT-abs were performed to explore the relationship between risk score and tumor microenvironment in hepatocellular carcinoma. Finally the sensitivity of the model to chemotherapeutic agents was predicted. RESULTS: Single-cell sequencing identified 207 marker genes for NK cells in hepatocellular carcinoma. Enrichment analysis suggested that NK cell marker genes were mainly involved in cellular immune function. Eight genes were selected for prognostic modeling after multifactorial COX regression analysis. The model was validated in GEO and ICGC data. Immune cell infiltration and function were higher in the low-risk group than in the high-risk group. The low-risk group was more suitable for ICI and PD-1 therapy. Half-maximal inhibitory concentrations of Sorafenib, Lapatinib, Dabrafenib, and Axitinib were significantly different on the two risk groups. CONCLUSION: A new signature of hepatocyte NK cell marker genes possesses a powerful ability to predict prognosis and immunotherapeutic response in patients with hepatocellular carcinoma.

SETD2-H3K36ME3: an important bridge between the environment and tumors.

Epigenetic regulation plays an important role in the occurrence, development and treatment of tumors. The histone methyltransferase SET-domain-containing 2 (SETD2) plays a key role in mammalian epigenetic regulation by catalyzing histone methylation and interacting with RNA polymerase II to mediate transcription elongation and mismatch repair. As an important bridge between the environment and tumors, SETD2-H3K36me3 plays an important role in the occurrence and development of tumors. Many tumors, including renal cancer, gastric cancer, lung cancer, are closely related to SETD2 gene mutations. As a key component of common tumor suppressor mechanisms, SETD2-H3K36me3is an important target for clinical disease diagnosis and treatment. Here, we reviewed the structure and function of the SETD2 and how SETD2-H3K36me3 functions as a bridge between the environment and tumors to provide an in-depth understanding of its role in the occurrence and development of various tumors, which is of great significance for future disease diagnosis and treatment.

Effect of radiotherapy interruption on nasopharyngeal cancer.

Nasopharyngeal carcinoma (NPC) is a malignant tumor originating from the epithelial cells of the nasopharynx with a unique geographic distribution, and is particularly prevalent in East and Southeast Asia. Due to its anatomical location, the surgery is difficult to access and the high sensitivity of nasopharyngeal cancer to radiotherapy (RT) makes it the main treatment modality. Radical radiotherapy is the first-line treatment for early-stage nasopharyngeal carcinoma and the cornerstone of multidisciplinary treatment for patients with locally advanced nasopharyngeal carcinoma. Nevertheless, radiotherapy interruption is inevitable as a consequence of unavoidable factors such as public holidays, machine malfunction, patient compliance, and adverse response to treatment, which in turn leads to a reduction in bioactivity and causes sublethal loss of tumor cells to repair. Unirradiated tumor cells are more likely to repopulate at or near their original fastest growth rate during this interval. If no measures are taken after the radiotherapy interruption, such as increasing the dose of radiotherapy and systemic therapy, the tumor is most likely to go uncontrolled and then progress. This review describes the effects of radiotherapy interruption on nasopharyngeal carcinoma, the mechanism of the effect, and explores the measures that can be taken in response to such interruption.

Redox-associated messenger RNAs identify novel prognostic values and influence the tumor immune microenvironment of lung adenocarcinoma.

Background: An imbalance of redox homeostasis participates in tumorigenesis, proliferation, and metastasis, which results from the production of reactive oxygen species (ROS). However, the biological mechanism and prognostic significance of redox-associated messenger RNAs (ramRNAs) in lung adenocarcinoma (LUAD) still remain unclear. Methods: Transcriptional profiles and clinicopathological information were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) of LUAD patients. A total of 31 overlapped ramRNAs were determined, and patients were separated into three subtypes by unsupervised consensus clustering. Biological functions and tumor immune-infiltrating levels were analyzed, and then, differentially expressed genes (DEGs) were identified. The TCGA cohort was divided into a training set and an internal validation set at a ratio of 6:4. Least absolute shrinkage and selection operator regression were used to compute the risk score and determine the risk cutoff in the training set. Both TCGA and GEO cohort were distinguished into a high-risk or low-risk group at the median cutoff, and then, relationships of mutation characteristics, tumor stemness, immune differences, and drug sensitivity were investigated. Results: Five optimal signatures (ANLN, HLA-DQA1, RHOV, TLR2, and TYMS) were selected. Patients in the high-risk group had poorer prognosis, higher tumor mutational burden, overexpression of PD-L1, and lower immune dysfunction and exclusion score compared with the low-risk group. Cisplatin, docetaxel, and gemcitabine had significantly lower IC50 in the high-risk group. Conclusion: This study constructed a novel predictive signature of LUAD based on redox-associated genes. Risk score based on ramRNAs served as a promising biomarker for prognosis, TME, and anti-cancer therapies of LUAD.

Establishment and validation of a prognostic nomogram for extrahepatic cholangiocarcinoma.

Simple summary: Accurately estimate the prognosis of patients with ECCA is important. However, the TNM system has some limitations, such as low accuracy, exclusion of other factors (e.g., age and sex), and poor performance in predicting individual survival risk. In contrast, a nomogram-based clinical model related to a comprehensive analysis of all risk factors is intuitive and straightforward, facilitating the probabilistic analysis of tumor-related risk factors. Simultaneously, a nomogram can also effectively drive personalized medicine and facilitate clinicians for prognosis prediction. Therefore, we construct a novel practical nomogram and risk stratification system to predict CSS in patients with ECCA. Background: Accurately estimate the prognosis of patients with extrahepatic cholangiocarcinoma (ECCA) was important, but the existing staging system has limitations. The present study aimed to construct a novel practical nomogram and risk stratification system to predict cancer-specific survival (CSS) in ECCA patients. Methods: 3415 patients diagnosed with ECCA between 2010 and 2015 were selected from the SEER database and randomized into a training cohort and a validation cohort at 7:3. The nomogram was identified and calibrated using the C-index, receiver operating characteristic curve (ROC), and calibration plots. Decision curve analysis (DCA), net reclassification index (NRI), integrated discrimination improvement (IDI) and the risk stratification were used to compare the nomogram with the AJCC staging system. Results: Nine variables were selected to establish the nomogram. The C-index (training cohort:0.785; validation cohort:0.776) and time-dependent AUC (>0.7) showed satisfactory discrimination. The calibration plots also revealed that the nomogram was consistent with the actual observations. The NRI (training cohort: 1-, 2-, and 3-year CSS:0.27, 0.27,0.52; validation cohort:1-,2-,3-year CSS:0.48,0.13,0.34), IDI (training cohort: 1-, 2-, 3-year CSS:0.22,0.18,0.16; validation cohort: 1-,2-,3-year CSS:0.18,0.16,0.17), and DCA indicated that the established nomogram significantly outperformed the AJCC staging system (P<0.05) and had better recognition compared to the AJCC staging system. Conclusions: We developed a practical prognostic nomogram to help clinicians assess the prognosis of patients with ECCA.

Low-grade hepatocellular carcinoma characteristics, a practical nomogram and risk stratification system: a SEER population-based study.

BACKGROUND: The purpose of this study is to establish a nomogram and risk stratification system to predict OS in patients with low-grade HCC. RESEARCH DESIGN AND METHODS: Data were extracted from the SEER database. C-index, time-dependent AUCs, and calibration plots were used to evaluate the effective performance of the nomogram. NRI, IDI, and DCA curves were adopted to compare the clinical utility of nomogram with AJCC. RESULTS: 3415 patients with low-grade HCC were available. The C-indices for the training and validation cohorts were 0.773 and 0.772. The time-dependent AUCs in the training cohort were 0.821, 0.817, and 0.846 at 1, 3 and 5 years. Calibration plots for 1-, 3- and 5-year OS showed good consistency between actual observations and that predicted by the nomogram. The values of NRI at 1, 3, and 5 years were 0.37, 0.66, and 0.64. The IDI values at 1, 3, and 5 years were 0.11, 0.16, and 0.23 (P< 0.001). DCA curves demonstrated that the nomogram showed better ability of predicting 1-, 3-, and 5-year OS probabilities than AJCC. CONCLUSIONS: A nomogram and risk stratification system for predicting OS in patients with low-grade HCC were established and validated.

Clinical features and prognostic factors in patients with microvascular infiltration of hepatocellular carcinoma: Development and validation of a nomogram and risk stratification based on the SEER database.

Background: The goal is to establish and validate an innovative prognostic risk stratification and nomogram in patients of hepatocellular carcinoma (HCC) with microvascular invasion (MVI) for predicting the cancer-specific survival (CSS). Methods: 1487 qualified patients were selected from the Surveillance, Epidemiology and End Results (SEER) database and randomly assigned to the training cohort and validation cohort in a ratio of 7:3. Concordance index (C-index), area under curve (AUC) and calibration plots were adopted to evaluate the discrimination and calibration of the nomogram. Decision curve analysis (DCA) was used to quantify the net benefit of the nomogram at different threshold probabilities and compare it to the American Joint Committee on Cancer (AJCC) tumor staging system. C-index, net reclassification index (NRI) and integrated discrimination improvement (IDI) were applied to evaluate the improvement of the new model over the AJCC tumor staging system. The new risk stratifications based on the nomogram and the AJCC tumor staging system were compared. Results: Eight prognostic factors were used to construct the nomogram for HCC patients with MVI. The C-index for the training and validation cohorts was 0.785 and 0.776 respectively. The AUC values were higher than 0.7 both in the training cohort and validation cohort. The calibration plots showed good consistency between the actual observation and the nomogram prediction. The IDI values of 1-, 3-, 5-year CSS in the training cohort were 0.17, 0.16, 0.15, and in the validation cohort were 0.17, 0.17, 0.17 (P<0.05). The NRI values of the training cohort were 0.75 at 1-year, 0.68 at 3-year and 0.67 at 5-year. The DCA curves indicated that the new model more accurately predicted 1-year, 3-year, and 5-year CSS in both training and validation cohort, because it added more net benefit than the AJCC staging system. Furthermore, the risk stratification system showed the CSS in different groups had a good regional division. Conclusions: A comprehensive risk stratification system and nomogram were established to forecast CSS for patients of HCC with MVI.

Design, synthesis and mechanism of action of novel 1,9-disubstituted ß-carboline derivatives as antitumor agents.

A range of novel 1,9-disubstituted ß-carboline derivatives was designed, synthesized and evaluated as potential anticancer agents. The preliminary study suggested that compounds 6a, 6b, 6c, 6d, 6e, 6f, 6g, and 6h tested in this study exerted potent antiproliferative effects on ten selected human tumor cell lines, with compound 6e being the most effective antiproliferative agent against the BGC-823, A375 and HT-29 cell lines, with IC50 values of 23.9, 9.3, and 3.6 µM, respectively. In addition, the antitumor capability of compound 6e was also evaluated in vivo, which demonstrated that compound 6e distinctly inhibited colorectal tumor growth in syngeneic BALB/c mice. Further research into the fundamental mechanism revealed that compound 6e inhibited colorectal cancer growth through the ATG5 (autophagy-related-5)/ATG7 (autophagy-related-7)-dependent autophagy pathway. This research can contribute to further clinical application of ß-carboline derivatives as new antitumor drugs.

A Practical Nomogram and Risk Stratification System Predicting Cancer-Specific Survival for Hepatocellular Carcinoma Patients With Severe Liver Fibrosis.

Objective: Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related deaths worldwide. This study aims to construct a novel practical nomogram and risk stratification system to predict cancer-specific survival (CSS) in HCC patients with severe liver fibrosis. Methods: Data on 1,878 HCC patients with severe liver fibrosis in the period 1975 to 2017 were extracted from the Surveillance, Epidemiology, and End Results database (SEER). Patients were block-randomized (1,316 training cohort, 562 validation cohort) by setting random seed. Univariate and multivariate COX regression analyses were employed to select variables for the nomogram. The consistency index (C-index), the area under time-dependent receiver operating characteristic curve (time-dependent AUC), and calibration curves were used to evaluate the performance of the nomogram. Decision curve analysis (DCA), the C-index, the net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to compare the nomogram with the AJCC tumor staging system. We also compared the risk stratification of the nomogram with the American Joint Committee on Cancer (AJCC) staging system. Results: Seven variables were selected to establish the nomogram. The C-index (training cohort: 0.781, 95%CI: 0.767-0.793; validation cohort: 0.793, 95%CI = 95%CI: 0.779-0.798) and the time-dependent AUCs (the training cohort: the values of 1-, 3-, and 5 years were 0.845, 0.835, and 0.842, respectively; the validation cohort: the values of 1-, 3-, and 5 years were 0.861, 0.870, and 0.876, respectively) showed satisfactory discrimination. The calibration plots also revealed that the nomogram was consistent with the actual observations. NRI (training cohort: 1-, 2-, and 3-year CSS: 0.42, 0.61, and 0.67; validation cohort: 1-, 2-, and 3-year CSS: 0.26, 0.52, and 0.72) and IDI (training cohort: 1-, 3-, and 5-year CSS:0.16, 0.20, and 0.22; validation cohort: 1-, 3-, and 5-year CSS: 0.17, 0.26, and 0.30) indicated that the established nomogram significantly outperformed the AJCC staging system (P < 0.001). Moreover, DCA also showed that the nomogram was more practical and had better recognition. Conclusion: A nomogram for predicting CSS for HCC patients with severe liver fibrosis was established and validated, which provided a new system of risk stratification as a practical tool for individualized treatment and management.

A Practical Nomogram and Risk Stratification System Predicting the Cancer-Specific Survival for Patients With Advanced Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) has the highest cancer-related mortality rate. This study aims to create a nomogram to predict the cancer-specific survival (CSS) in patients with advanced hepatocellular carcinoma. Methods: Patients diagnosed with advanced HCC (AJCC stage III and IV) during 1975 to 2018 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Qualified patents were randomized into training cohort and validation cohort at a ratio of 7:3. The results of univariate and multivariate Cox regression analyses were used to construct the nomogram. Consistency index (C-index), area under the time-dependent receiver operating characteristic (ROC) curve [time-dependent area under the curve (AUC)], and calibration plots were used to identify and calibrate the nomogram. The net reclassification index (NRI), integrated discrimination improvement (IDI), and C-index, and decision curve analysis DCA were adopted to compare the nomogram's clinical utility with the AJCC criteria. Results: The 3,103 patients with advanced hepatocellular carcinoma were selected (the training cohort: 2,175 patients and the validation cohort: 928 patients). The C-index in both training cohort and validation cohort were greater than 0.7. The AUC for ROC in the training cohort was 0.781, 0.771, and 0.791 at 1, 2, and 3 years CSS, respectively. Calibration plots showed good consistency between actual observations and the 1-, 2-, and 3-year CSS predicted by the nomogram. The 1-, 2-, and 3-year NRI were 0.77, 0.46, and 0.48, respectively. The 1-, 2-, and 3-year IDI values were 0.16, 0.15, and 0.12 (P < 0.001), respectively. DCA curves in both the training and validation cohorts demonstrated that the nomogram showed better predicted 1-, 2-, and 3-year CSS probabilities than AJCC criteria. Conclusions: This study established a practical nomogram for predicting CSS in patients with advanced HCC and a risk stratification system that provided an applicable tool for clinical management.

Glycosylation-Related Genes Predict the Prognosis and Immune Fraction of Ovarian Cancer Patients Based on Weighted Gene Coexpression Network Analysis (WGCNA) and Machine Learning.

Background: Ovarian cancer (OC) is a malignancy exhibiting high mortality in female tumors. Glycosylation is a posttranslational modification of proteins but research has failed to demonstrate a systematic link between glycosylation-related signatures and tumor environment of OC. Purpose: This study is aimed at developing a novel model with glycosylation-related messenger RNAs (GRmRNAs) to predict the prognosis and immune function in OC patients. Methods: The transcriptional profiles and clinical phenotypes of OC patients were collected from the Gene Expression Omnibus and The Cancer Genome Atlas databases. A weighted gene coexpression network analysis and machine learning were performed to find the optimal survival-related GRmRNAs. Least absolute shrinkage and selection operator regression (LASSO) and Cox regression were carried out to calculate the coefficients of each GRmRNA and compute the risk score of each patient as well as develop a prognostic model. A nomogram model was constructed, and several algorithms were used to investigate the relationship between risk subtypes and immune-infiltrating levels. Results: A total of four signatures (ALG8, DCTN4, DCTN6, and UBB) were determined to calculate the risk scores, classifying patients into the high-and low-risk groups. High-risk patients exhibited significantly poorer survival outcomes, and the established nomogram model had a promising prediction for OC patients' prognosis. Tumor purity and tumor mutation burden were negatively correlated with risk scores. In addition, risk scores held statistical associations with pathway signatures such as Wnt, Hippo, and reactive oxygen species, and nonsynonymous mutation counts. Conclusion: The currently established risk scores based on GRmRNAs can accurately predict the prognosis, the immune microenvironment, and the immunotherapeutic efficacy of OC patients.

Quantifying total suspended matter (TSM) in waters using Landsat images during 1984-2018 across the Songnen Plain, Northeast China.

Understanding the spatiotemporal dynamics of total suspended matter (TSM) in waters is necessary to promote efficient water resource management. In our study, we have estimated the spatiotemporal pattern of TSM with the combination of time-series Landsat images and field survey. Among various remote sensing-derived parameters, the red/blue band turns to be robust and the most sensitive to the TSM from field measurements. In Songnen Plain, the mean annual TSM in 60.5% of the water bodies decreased from 1984 to 2018. The decreasing of TSM is likely due to the increasing of vegetation in the area. The TSM concentration in waters declined from April to July, and then increased from September onwards. We also found the TSM in water bodies in Songnen Plain has very high spatial variation. Our results indicated that the meteorological factors such as wind and precipitation may affect the variation of TSM. Our results demonstrate that long-term Landsat data are useful to examine TSM in inland waters. Our findings can support for water resource management under human activities and climate change.