Immune response and inflammation in cancer health disparities

SLC2A3 is a Potential Factor for Head and Neck Squamous Cancer Development through Tumor Microenvironment Alteration

INTRODUCTION

Tumor immunity has garnered increasing attention in cancer treatment and progression. However, there is still a challenge in understanding the mechanisms of specific molecules affecting the clinical prognosis and tumor microenvironment (TME).

METHODS

Here, we applied the ESTIMATE algorithm to calculate the immune and stromal scores in 504 HNSC cases from TCGA. Patients were grouped according to the median value of the immune and stromal. Clinicopathological characteristics and differentially expressed genes (DEG) were analyzed. Subsequently, LASSO, COX regression, survival analysis, and clinicopathological characteristics were conducted. Subsequently, SLC2A3 was determined as a predictive factor that high expression of SLC2A3 at the mRNA and protein levels predicted a worse clinical prognosis. GSEA25099 was utilized for external validation of immune infiltration, while tissue PCR, IHC, and Western Blot were used to confirm the expression levels of SLC2A3.

RESULTS

A series of immune-infiltration analyses showed that SLC2A3 expression was negatively correlated with CD8+ T cells, significantly affecting the survival prognosis of HNSC. In the GSEA analysis, the high expression of SLC2A3 was mainly enriched for immune-related biological processes. Meanwhile, high expression of SLC2A3 possessed higher TIDE scores and was also strongly positively correlated with a series of immune checkpoints affecting survival prognosis, thus causing greater susceptibility to immune escape.

CONCLUSION

Conclusively, SLC2A3 is a potential oncogene and factor of HNSC development, notably by an altered state of the immune microenvironment, immune-suppressive regulation, and immune escape.

Income disparities have a significant impact on thyroid cancer recurrence and survival

PURPOSE

Income inequality profoundly impacts cancer outcomes, yet its specific effects on thyroid cancer remain unclear. Elucidating the influence of socioeconomic disparities is imperative to advance health equity and optimize patient care. This study evaluates associations between median household income and thyroid cancer recurrence and survival using national cancer registry data.

METHODS

139,302 thyroid cancer patients undergoing surgery from 2000 to 2019 were analyzed from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were categorized by median annual household income at the county level (> or <$75,000). Multivariable regression determined the impact of income on recurrence and overall mortality.

RESULTS

Higher-income patients had 26 % lower recurrence odds (OR 0.74, 95 %CI 0.55-0.99, p=0.042) and longer median survival (18.1 vs 17.7 years, p<0.001) compared to lower-income patients. On multivariate analysis, high income remained an independent predictor of reduced mortality after adjusting for demographics, tumor factors, and treatment (adjusted HR=0.84, 95 %CI=0.81-0.87, p<0.001). Cancer-directed surgery (HR=0.28, 95 %CI=0.26-0.30, p<0.001) and radioactive iodine (HR=0.69, 95 %CI=0.66-0.71, p<0.001) were associated with lower mortality risk.

CONCLUSIONS

Income disparities have a significant influence on thyroid cancer outcomes, including lower recurrence and reduced mortality. Targeting socioeconomic inequity could substantially reduce recurrence, improve survival, and promote health equity for all patients.

Early administration of Wumei Wan inhibit myeloid-derived suppressor cells via PI3K/Akt pathway and amino acids metabolism to prevent colitis-associated colorectal cancer

ETHNOPHARMACOLOGICAL RELEVANCE

Wumei Wan (WMW), a traditional Chinese medicine prescription, has been proved to be effective in treating Colitis-associated colorectal cancer (CAC), but it has not been proven to be effective in different stages of CAC.

AIM OF THE STUDY

The purpose of our study is to investigate the therapeutic effect and mechanism of WMW on the progression of CAC.

MATERIALS AND METHODS

Azioximethane (AOM) and dextran sulfate sodium (DSS) were used to treat mice for the purpose of establishing CAC models. WMW was administered in different stages of CAC. The presentative chemical components in WMW were confirmed by LC-MS/MS under the optimized conditions. The detection of inflammatory cytokines in the serum and colon of mice were estimated by qRT-PCR and ELISA. The changes of T cells and myeloid-derived suppressor cells (MDSCs) in each group were detected by flow cytometry. The metabolic components in serum of mice were detected by UPLC-MS/MS. Expression of genes and proteins were detected by eukaryotic transcriptomics and Western blot to explore the key pathway of WMW in preventing CAC.

RESULTS

WMW had significant effect on inhibiting inflammatory responses and tumors during the early development stage of CAC when compared to other times. WMW increased the length of mice's colons, reduced the level of IL-1β, IL-6, TNF-α in colon tissues, and effectively alleviated colonic inflammation, and improved the pathological damage of colon tissues. WMW could significantly reduce the infiltration of MDSCs in the spleen, increase CD4+ T cells and CD8+ T cells in the spleen of CAC mice, and effectively reform the immune microenvironment in CAC mice. Transcriptomics analysis revealed that 2204 genes had different patterns of overlap in the colon tissues of mice between control group, AOM + DSS group, and early administration of WMW group. And KEGG enrichment analysis showed that PI3K/Akt signaling pathway, ECM-receptor interaction, IL-17 signaling pathway, MAPK signaling pathway, pancreatic secretion, thermogenesis, and Rap1 signaling pathway were all involved. The serum metabolomics results of WMW showed that the metabolic compositions of the control group, AOM + DSS group and the early stage of WMW were different, and 42 differential metabolites with the opposite trends of changes were screened. The metabolic pathways mainly included pyrimidine metabolism, glycine, serine and threonine metabolism, tryptophan metabolism, and purine metabolism. And amino acids and related metabolites may play an important role in WMW prevention of CAC.

CONCLUSION

WMW can effectively prevent the occurrence and development of CAC, especially in the initial stage. WMW can reduce the immune infiltration of MDSCs in the early stage. Early intervention of WMW can improve the metabolic disorder caused by AOM + DSS, especially correct the amino acid metabolism. PI3K/Akt signaling pathway was inhabited in early administration of WMW, which can regulate the amplification and function of MDSCs.

Integrative multi-omics analysis reveals the role of tumor-associated endothelial cells and their signature in prognosis of intrahepatic cholangiocarcinoma

This study aims to investigate the interplay between tumor-associated endothelial cells (TECs) and immune cells within the tumor microenvironment (TME) and its impact on tumor prognosis. We conducted single-cell RNA sequencing (scRNA-seq) of tumor, normal, and lymph node tissues obtained from intrahepatic cholangiocarcinoma (ICC) patients to reveal the role of TECs in tumor angiogenesis and their significant heterogeneity. Meanwhile, we identified genes highly expressed in TECs and constructed TEC signatures (TEC.Sig). Next, we calculated TEC scores of samples based on TEC.Sig. Patients with higher TEC scores exhibited a higher frequency of KRAS mutations, which was associated with increased infiltration of neutrophils and immature dendritic cells (iDCs), and decreased numbers of natural killer (NK), CD4 + T, and CD8 + T effector memory (Tem) cells, indicating an inflammation-dominated immunosuppressive phenotype. In contrast, BAP1 mutations and CXCL12 overexpression showed a contrasting trend. Spatial transcriptomics analysis and histological experiments further confirmed that TECs interacted with various tumor-killing immune cells through the CXCL12/CXCR4 axis. Multiple tumor immunotherapy datasets confirmed that the TEC.Sig could predict patient responses to immunotherapy. The TEC score is a promising and reliable biomarker for predicting genetic mutations and prognosis in ICC patients. Enhancing the regulation of the CXCL12/CXCR4 signaling pathway may represent a potential novel therapeutic target for ICC treatment.

Systemic immune-inflammation and prognostic immune nutritional index in oral squamous cell carcinoma patients

Aim: To investigate the systemic immune-inflammation index and prognostic immune nutritional index in the prognostic evaluation of oral squamous cell carcinoma.Materials & methods: We analyzed retrospectively the relationship between systemic immune-inflammation index, prognostic immune nutritional index and clinicopathological variables and the overall survival of 262 patients who underwent radical surgery.Results: Multivariate analysis showed high systemic immune-inflammation index (Hazard ratio = 3.062, 95% CI: 1.021-8.251), low prognostic immune nutritional index (Hazard ratio = 0.297, 95% CI: 0.139-0.636), tumor node metastasis classification 3-4 (Hazard ratio = 9.862, 95% CI: 4.658-20.880) patients have worse overall survival.Conclusion: Preoperative systemic immune-inflammation index and prognostic immune nutritional index are independent risk factors for prognostic survival status in oral squamous cell carcinoma.

Anti-inflammatory diets

ABSTRACT

This article reviews inflammatory versus anti-inflammatory foods, autoimmune and inflammatory disorders, the benefits of specific anti-inflammatory diets, and strategies for nurses to partner with individuals, while considering culture and food preferences, to promote healthy eating habits and prevent diseases.

Comparative immune profiling of pancreatic ductal adenocarcinoma progression among South African patients

BACKGROUND

Pancreatic Ductal Adenocarcinoma (PDAC) is an aggressive cancer characterized by an immunosuppressive microenvironment. Patients from specific ethnicities and population groups have poorer prognoses than others. Therefore, a better understanding of the immune landscape in such groups is necessary for disease elucidation, predicting patient outcomes and therapeutic targeting. This study investigated the expression of circulating key immune cell markers in South African PDAC patients of African ancestry.

METHODS

Blood samples were obtained from a total of 6 healthy volunteers (HC), 6 Chronic Pancreatitis (CP) and 34 PDAC patients consisting of 22 resectable (RPC), 8 locally advanced (LAPC) and 4 metastatic (MPC). Real-time Quantitative Polymerase Chain reactions (RT-qPCR), Metabolomics, Enzyme-Linked Immunosorbent Assay (ELISA), Reactive Oxygen Species (ROS), and Immunophenotyping assays were conducted. Statistical analysis was conducted in R (v 4.3.2). Additional analysis of single-cell RNA data from 20 patients (16 PDAC and 4 controls) was conducted to interrogate the distribution of T-cell and Natural Killer cell populations.

RESULTS

Granulocyte and neutrophil levels were significantly elevated while lymphocytes decreased with PDAC severity. The total percentages of CD3 T-cell subpopulations (helper and double negative T-cells) decreased when compared to HC. Although both NK (p = 0.014) and NKT (p < 0.001) cell levels increased as the disease progressed, their subsets: NK CD56dimCD16- (p = 0.024) and NKTs CD56+ (p = 0.008) cell levels reduced significantly. Of note is the negative association of NK CD56dimCD16- (p < 0.001) cell levels with survival time. The gene expression analyses showed no statistically significant correlation when comparing the PDAC groups with the controls. The inflammatory status of PDAC was assessed by ROS levels of serum which were elevated in CP (p = 0.025), (RPC (p = 0.003) and LAPC (p = 0.008)) while no significant change was observed in MPC, compared to the HC group. ROS was shown to be positively correlated with GlycA (R = 0.45, p = 0.0096). Single-cell analyses showed a significant difference in the ratio of NKT cells per total cell counts in LAPC (p < 0.001) and MPC (p < 0.001) groups compared with HC, confirming observations in our sample group.

CONCLUSION

The expression of these immune cell markers observed in this pilot study provides insight into their potential roles in tumour progression in the patient group and suggests their potential utility in the development of immunotherapeutic strategies.

The immune cells have complex causal regulation effects on cancers

BACKGROUND

There was a large body of evidence linking immune cells to cancer risk. However, the causal relationship between immune cells, cancer, and what genes play an important role is unclear.

METHODS

In this study, we performed comprehensive two-sample Mendelian randomization analysis (TSMR) to determine the causal relationship between immune cells and common cancers. We also performed Multimarker Analysis of Genomic Annotation (MAGMA) on immune cells causally associated with cancer to identify their relevant genes and used data summary-based MR (SMR) analysis to investigate the causal relationship between their gene expression, methylation, and cancer, and further used drug prediction and molecular docking to validate the medicinal value of the targets. Finally, reverse TSMR analysis was performed on cancer and immune cells to rule out reverse causality.

RESULTS

After FDR correction (PFDR < 0.05), the results showed that 2 immune cells were associated with lung cancer risk, and 1 immune cell was significantly associated with pancreatic cancer risk. The expression of OSBPL10, CHD4, SMDT1, PHETA2, and NAGA was positively and causally related to the risk of lung cancer by SMR analysis and HEIDI test. We also found that increased expression of ANP32E decreased the risk of pancreatic cancer and that the methylation level of OSBPL10, CHD4, SULF2, CENPM, and CYP2D6 had a causal association with lung cancer. The methylation level of FCGR3A was causally associated with pancreatic cancer. The results of molecular docking indicated a strong affinity between the drugs and proteins that possessed existing structural information.

CONCLUSION

This data-driven Mendelian randomization (MR) study demonstrates the causal role of immune cells in cancers. In addition, this study identifies candidate genes that may be potential anti-cancer drug targets.

The therapeutic effect and targets of herba Sarcandrae on breast cancer and the construction of a prognostic signature consisting of inflammation-related genes

Background

The prevalence of breast cancer (BRCA), which is common among women, is on the rise. This study applied network pharmacology to explore the potential mechanism of action of herba sarcandrae in BRCA and construct a prognostic signature composed of inflammation-related genes.

Methods

The active ingredients of herba sarcandrae were screened using the SymMap, TCMID, and TCMSP platforms, and the molecular targets were determined in the UniProt database. The "drug-active compound-potential target" network was established with Cytoscape 3.7.2. The molecular targets were subjected to disease ontology, gene ontology (GO), and Kyoto Encyclopedia of Genes (KEGG) analyses. AutoDock software was used for molecular docking. Differentially expressed genes (DEGs) related to inflammation were obtained from the BRCA Cancer Genome Atlas (TCGA) database. In the training cohort, the univariate Cox regression model was applied to preliminarily screen prognostic genes. A multigene signature was built by the least absolute shrinkage and selection operator (LASSO) regression model, followed by validation through Kaplan‒Meier, Cox, and receiver operating characteristic (ROC) analyses.

Results

Forty-one active compounds were identified, and 265 therapeutic targets for herba sarcandrae were predicted. GO enrichment results revealed significant enrichment of biological processes, such as response to xenobiotic stimuli, response to nutrient levels, and response to lipopolysaccharide. KEGG analysis revealed significant enrichment of pathways such as AGE-RAGE and chemical carcinogenesis receptor activation signaling pathways. In addition, the herbs Marc-Andre and rutin were shown to mediate BRCA cell proliferation and apoptosis via the interferon regulatory factor 1 (IRF1)/signal transducer and activator of transcription 3 (STAT3)/programmed death-ligand 1 (PD-L1) pathway. Sixteen inflammatory signatures, including BST2, GPR132, IL12B, IL18, IL1R1, IL2RB, IRF1, and others, were constructed, and the risk score was found to be a strong independent prognostic factor for overall survival in BRCA patients. The 16-inflammation signature was associated with several clinical features (age, clinical stage, T, and N classifications) and could reflect immune cell infiltration in tumor microenvironments with different immune cells.

Conclusions

Herba sarcandrae and rutin were shown to mediate BRCA cell proliferation and apoptosis via the IRF1/STAT3/PD-L1 pathway, and the 16-member inflammatory signature might be a novel biomarker for predicting BRCA patient prognosis, providing more accurate guidance for clinical treatment prognosis evaluation and having important reference value for individualized treatment selection.

Low TYROBP expression predicts poor prognosis in multiple myeloma

BACKGROUND

Multiple myeloma (MM) is the second most common refractory hematologic cancer. Searching for new targets and prognostic markers for MM is significant.

METHODS

GSE39754, GSE6477 and GSE24080 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in MM versus healthy people from GSE39754 and GSE6477 were screened using limma package, and MM-related module genes were chosen with the use of Weighted gene co-expression network analysis (WGCNA), and the two were intersected using ggVennDiagram for obtaining MM-related DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out. Then, protein-protein interactions (PPI) analysis in String database was used to obtain hub genes, while prognosis was analyzed by survival package in GSE24080. Receiver operating characteristic (ROC) curve was adopted for evaluating diagnostic value of hub genes. Besides, univariable/multivariable Cox regression were employed to screen independent prognostic biomarkers. Gene set enrichment analysis (GSEA) was used to find possible mechanism. Finally, western-blotting and reverse transcription-polymerase chain reaction (RT-PCR) verify TYROBP expression within MM and healthy people. We performed cell adhesion and transwell assays for investigating TYROBP function in MM cell adhesion and migration.

RESULTS

Through differential analyses, 92 MM-related DEGs were obtained. 10 hub genes were identified by PPI and CytoHubba. Their diagnostic and prognostic significance was analyzed. Down-regulation of genes like TYROBP, ELANE, MNDA, and MPO related to dismal MM prognosis. Upon univariable/multivariable Cox regression, TYROBP independently predicted MM prognosis. GSEA pathway was enriched, indicating that TYROBP expression affected MM development via cell adhesion molecular pathway. Upon Western-blotting and RT-PCR assays, TYROBP expression among MM patients decreased relative to healthy donors. Cell adhesion and transwell migration assays revealed increased MM cell adhesion and decreased migration upon TYROBP up-regulation.

CONCLUSION

In summary, TYROBP is a potential prognostic marker for MM.

Population-enriched innate immune variants may identify candidate gene targets at the intersection of cancer and cardio-metabolic disease

Both cancer and cardio-metabolic disease disparities exist among specific populations in the US. For example, African Americans experience the highest rates of breast and prostate cancer mortality and the highest incidence of obesity. Native and Hispanic Americans experience the highest rates of liver cancer mortality. At the same time, Pacific Islanders have the highest death rate attributed to type 2 diabetes (T2D), and Asian Americans experience the highest incidence of non-alcoholic fatty liver disease (NAFLD) and cancers induced by infectious agents. Notably, the pathologic progression of both cancer and cardio-metabolic diseases involves innate immunity and mechanisms of inflammation. Innate immunity in individuals is established through genetic inheritance and external stimuli to respond to environmental threats and stresses such as pathogen exposure. Further, individual genomes contain characteristic genetic markers associated with one or more geographic ancestries (ethnic groups), including protective innate immune genetic programming optimized for survival in their corresponding ancestral environment(s). This perspective explores evidence related to our working hypothesis that genetic variations in innate immune genes, particularly those that are commonly found but unevenly distributed between populations, are associated with disparities between populations in both cancer and cardio-metabolic diseases. Identifying conventional and unconventional innate immune genes that fit this profile may provide critical insights into the underlying mechanisms that connect these two families of complex diseases and offer novel targets for precision-based treatment of cancer and/or cardio-metabolic disease.

A novel machine learning prediction model for metastasis in breast cancer

BACKGROUND

Breast cancer (BC) metastasis is the common cause of high mortality. Conventional prognostic criteria cannot accurately predict the BC metastasis risk. The machine learning technologies can overcome the disadvantage of conventional models.

AIM

We developed a model to predict BC metastasis using the random survival forest (RSF) method.

METHODS

Based on demographic data and routine clinical data, we used RSF-recursive feature elimination to identify the predictive variables and developed a model to predict metastasis using RSF method. The area under the receiver operating characteristic curve (AUROC) and Kaplan-Meier survival (KM) analyses were plotted to validate the predictive effect when C-index was plotted to assess the discrimination and Brier scores was plotted to assess the calibration of the predictive model.

RESULTS

We developed a metastasis prediction model comprising three variables (pathological stage, aspartate aminotransferase, and neutrophil count) selected by RSF-recursive feature elimination. The model was reliable and stable when assessed by the AUROC (0.932 in training set and 0.905 in validation set) and KM survival analyses (p < .0001). The C-indexes (0.959) and Brier score (0.097) also validated the good predictive ability of this model.

CONCLUSIONS

This model relies on routine data and examination indicators in real-time clinical practice and exhibits an accurate prediction performance without increasing the cost for patients. Using this model, clinicians can facilitate risk communication and provide precise and efficient individualized therapy to patients with breast cancer.

A nomogram based on peripheral lymphocyte for predicting 8-year survival in patients with prostate cancer: a single-center study using LASSO-cox regression

PURPOSE

The purpose of this study was to develop a functional clinical nomogram for predicting 8-year overall survival (OS) of patients with prostate cancer (PCa) primary based on peripheral lymphocyte.

PATIENTS AND METHODS

Using data from a single-institutional registry of 94 patients with PCa in China, this study identified and integrated significant prognostic factors for survival to build a nomogram. The discriminative ability was measured by concordance index (C-index) and ROC curves (Receiver Operating Characteristic Curves). And the predictive accuracy was measured by the calibration curves. Decision curve analyses (DCA) was used to measure the clinical usefulness.

RESULTS

A total of 94 patients were included for analysis. Five independent prognostic factors were identified by LASSO-Cox regression and incorporated into the nomogram: age, the T stage, the absolute counts of peripheral CD3(+)CD4(+) T lymphocytes, CD3(-)CD16(+)CD56(+) NK cells and CD4(+)/CD8(+) ratio. The area under the curve (AUC) values of the predictive model for 5-, 8-, and 10-year overall survival were 0.81, 0.76, and 0.73, respectively. The calibration curves for probability of 5-,8- and 10-year OS showed optimal agreement between nomogram prediction and actual observation. The stratification into different risk groups allowed significant distinction. DCA indicated the good clinical application value of the model.

CONCLUSION

We developed a novel nomogram that enables personalized prediction of OS for patients diagnosed with PCa. This finding revealed a relative in age and survival rate in PCa, and a more favorable prognosis in patients exhibiting higher levels of CD4 + T, CD4+/CD8 + ratio and CD3(-)CD16(+)CD56(+) NK cells specifically. This clinically applicable prognostic model exhibits promising predictive capabilities, offering valuable support to clinicians in informed decision-making process.

Clinical Implications of Naples Prognostic Score for Patients with Resected Cholangiocarcinoma: A Real-World Experience

Purpose

The Nutritional Prognostic Score (NPS) is a composite indicator that effectively reflects the preoperative nutritional and inflammation status of patients. Its prognostic value has been extensively confirmed in various types of tumors. Our study aims to investigate the clinical implications of the NPS in the postoperative patients with cholangiocarcinoma (CCA).

Patients and Methods

Data on clinicopathological characteristics were collected from CCA patients who underwent radical surgery between 2014 and 2019 at Harbin Medical University Cancer Hospital. NPS was calculated using relevant indicators to categorize the patients, and association of NPS with clinicopathological characteristics and survival outcomes were analyzed. To assess differences in survival rates between different groups, we utilized the Kaplan-Meier method. Independent prognostic risk factors were identified by Cox regression analysis. A CONomogram was created, and its accuracy in survival prediction was evaluated using receiver operating characteristic (ROC) curves. Independent verification was conducted in the validation group.

Results

For this study, a cohort of 232 patients was enlisted and subsequently divided into training group (N=162) and validation group (N=70). An evident correlation was detected between NPS and preoperative malnutrition. Patients with higher NPS exhibited a worse overall survival (OS), with 5-year OS rates of 79.1%, 33.1%, and 10.6%. Multivariate analysis revealed that NPS was an independent risk factor for OS in resected CCA patients (P<0.001). The NPS-based Nomogram was developed to accurately assess the risk of patients.

Conclusion

The NPS was identified as a significant risk factor that impacts the prognosis of patients with resected CCA. In order to improve prognosis management, the NPS-based Nomogram has been demonstrated to be a precise and efficient tool.

sPDGFRβ and neuroinflammation are associated with AD biomarkers and differ by race: The ASCEND Study

INTRODUCTION

There remains an urgent need to identify preclinical pathophysiological mechanisms of Alzheimer's disease (AD) development in high-risk, racially diverse populations. We explored the relationship between cerebrospinal fluid (CSF) markers of vascular injury and neuroinflammation with AD biomarkers in middle-aged Black/African American (B/AA) and non-Hispanic White (NHW) participants.

METHODS

Adults (45-65 years) with a parental history of AD were enrolled (n = 82). CSF and blood biomarkers were collected at baseline and year 2.

RESULTS

CSF total tau (t-tau), phosphorylated tau (p-tau), and amyloid beta (Aβ)40 were elevated at year 2 compared to baseline. CSF soluble platelet-derived growth factor receptor β (sPDGFRβ) levels, a marker of pericyte injury, correlated positively with t-tau, p-tau, Aβ40 markers of vascular injury, and cytokines at baseline and year 2. CSF sPDGFRβ and tau were significantly lower in B/AA than NHW.

DISCUSSION

Vascular dysfunction and neuroinflammation may precede cognitive decline and disease pathology in the very early preclinical stages of AD, and there are race-related differences in these relationships.

HIGHLIGHTS

Cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarkers changed over 2 years in high-risk middle-aged adults. Markers of vascular dysfunction were associated with the CSF biomarkers amyloid beta and tau. AD biomarkers were lower in Black compared to non-Hispanic White individuals. Markers of vascular dysfunction were lower among Black individuals.

Construction and Validation of a Nomogram to Predict the Postoperative Venous Thromboembolism Risk in Patients with HGSOC

Venous thromboembolism (VTE) is a common complication in patients with high-grade serous ovarian cancer (HGSOC) after surgery. This study aims to establish a comprehensive risk assessment model to better identify the potential risk of postoperative VTE in HGSOC. Clinical data from 587 HGSOC patients who underwent surgical treatment were retrospectively collected. Univariate and multivariate logistic regression analyses were performed to identify independent factors influencing the occurrence of postoperative VTE in HGSOC. A nomogram model was constructed in the training set and further validated in the verification set. Logistic regression identified age (odds ratio [OR] = 1.063, P = .002), tumor size (OR = 3.815, P < .001), postoperative transfusion (OR = 5.646, P = .001), and postoperative D-dimer (OR = 1.246, P = .003) as independent risk factors for postoperative VTE in HGSOC patients. A nomogram was constructed using these factors. The receiver operating characteristic curve showed an area under the curve (AUC) of 0.840 (95% confidence interval [CI]: 0.782, 0.898) in the training set and 0.793 (95% CI: 0.704, 0.882) in the validation set. The calibration curve demonstrated a good consistency between model predictions and actual results. The decision curve analysis indicated the model benefits at a threshold probability of less than 70%. A nomogram predicting postoperative VTE in HGSOC was established and validated. This model will assist clinicians in the early identification of high-risk patients, enabling the implementation of appropriate preventive measures.

Role of CdTe quantum dots on peripheral Immunocytes and selenoprotein P: immunotoxicity at the molecular and cellular levels

The extensive product and application of cadmium-quantum dots (Cd-QDs), one kind of semiconductor nanomaterials, lead to prolonged exposure to the environment. Cd-QDs have shown good properties in biomedical and imaging-related fields; the safety of Cd-QDs limits the application of these materials and technologies, however. The systematic distribution of CdTe QDs in organisms has been ascertained in previous studies. Nevertheless, it is relatively less reported about the toxicity of CdTe QDs to immune macromolecules and organs. Based on this, immunocytes (including lymphocyte subsets-CD4+ T and CD8+ T cells, splenocytes) and selenoprotein P (SelP) were chosen as targets for CdTe QDs immunotoxicity studies. Results indicate that CdTe QDs induced cytotoxicity to CD4+ T cells, CD8+ T cells and splenocytes by reducing cell viability and causing apoptosis as CdTe QDs and Cd2+ enter cells. At the molecular level, the direct interaction between CdTe QDs and SelP is proved by multispectral measurements, which demonstrated the alteration of protein structure. The combined results show that CdTe QDs induced adverse effects on the immune system at the cellular and molecular levels. This research contributes to a better understanding of CdTe QDs cause harmful damage to the immune system and provides new strategies for the inhibition and treatment of health damages caused by CdTe QDs.

Identification of KIFC1 as an independent prognostic marker in renal clear cell carcinoma correlates with tumor proliferation and immune infiltration

Renal clear cell carcinoma (ccRCC) is the world's most common form of cancer. Up to a third will develop metastases; the 5-year survival rate of the patients was only 14%. Practical prognostic markers remain to be discovered. Kinesin-like protein (KIFC1), a critical factor in maintaining the stability of the microtubule system, has significant prognostic value in some tumors. We analyzed the prognostic value, associated signaling pathways, and regulatory mechanisms of KIFC1 in ccRCC through bioinformatics and proteomics. Concretely, both mRNA and protein expression levels of KIFC1 were dramatically upregulated. KIFC1 is an independent prognostic factor for ccRCC. The expression of KIFC1 showed a significant positive correlation (Spearman coefficient > 0.7) with tumor proliferation-related pathways (tumor proliferation, G2/M checkpoint, and DNA replication) and tumor inflammation. Further, intratumoral immune cell analysis revealed that high expression of KIFC1 predicted more infiltration of CD8 + T and CD4 + T cells (p < 0.001). However, there was a significant positive relationship between CD8 + T cells and numerous immune checkpoint genes. CD8 + T cells in tumors from the KIFC1 high expression group were at the dysregulated state. High expression of KIFC1 may predict a poor immunotherapy outcome. By proteomics, we analyzed proteins interacting with KIFC1; spliceosome proteins had the most significant enrichment, indicating the new directions for KIFC1 investigation. In conclusion, our study identified KIFC1 as an independent prognostic factor in renal clear cell carcinoma, and the associated processes involved tumor proliferation and immune infiltration. KIFC1 had a close relationship with spliceosome proteins; it may be a new research direction.

Development and validation of novel immune-inflammation-based clinical predictive nomograms in HER2-negative advanced gastric cancer

Purpose

To explore the predictive value of multiple immune-inflammatory biomarkers including serum VEGFA and systemic immune-inflammation index (SII) in HER2-negative advanced gastric cancer (AGC) and establish nomograms for predicting the first-line chemotherapeutic efficacy, progression-free survival (PFS) and overall survival (OS) of patients with this fatal disease.

Methods

From November 2017 to April 2022, 102 and 34 patients with a diagnosis of HER2-negative AGC at the First Affiliated Hospital of Bengbu Medical College were enrolled as development and validation cohorts, respectively. Univariate and multivariate analyses were performed to evaluate the clinical value of the candidate indicators. The variables were screened using LASSO regression analysis. Predictive models were developed using significant predictors and are displayed as nomograms.

Results

Baseline VEGFA expression was significantly higher in HER2-negative AGC patients than in nonneoplastic patients and was associated with malignant serous effusion and therapeutic efficacy (all p<0.001). Multivariate analysis indicated that VEGFA was an independent predictor for first-line therapeutic efficacy and PFS (both p<0.01) and SII was an independent predictor for first-line PFS and OS (both p<0.05) in HER2-negative AGC patients. The therapeutic efficacy model had an R2 of 0.37, a Brier score of 0.15, and a Harrell's C-index of 0.82 in the development cohort and 0.90 in the validation cohort. The decision curve analysis indicated that the model added more net benefits than VEGFA assessment alone. The PFS/OS models had Harrell's C-indexes of 0.71/0.69 in the development cohort and 0.71/0.62 in the validation cohort.

Conclusion

The established nomograms integrating serum VEGFA/SII and commonly available baseline characteristics provided satisfactory performance in predicting the therapeutic efficacy and prognosis of HER2-negative AGC patients.

The prognostic and biological importance of chromatin regulation-related genes for lung cancer is examined using bioinformatics and experimentally confirmed

BACKGROUND

The pathogenesis and clinical diagnosis of lung adenocarcinoma (LUAD), a malignant illness with substantial morbidity and mortality, are still being investigated. Genes involved in chromatin regulation are crucial in the biological function of LUAD.

METHODS

The prognostic prediction model for LUAD was developed using multivariables and least absolute shrinkage and selection operator (LASSO) regression. It consisted of 10 chromatin regulators. The LUAD has been divided into two groups, high- and low-risk, using a predictive model. The model was shown to be accurate in predicting survival by the nomogram, receiver operating characteristic (ROC) curves, and principal component analysis (PCA). An analysis of differences in immune-cell infiltration, immunologicalfunction, and clinical traits between low- and high-risk populations was conducted. Protein-protein interaction (PPI) networks and Gene Ontology (GO) pathways of differentially expressed genes (DEGs) in the high versus low risk group were also examined to investigate the association between genes and biological pathways. The biological roles of chromatin regulators (CRs) in LUAD were finally estimated using colony formation and cell movement. The important genes' mRNA expression has been measured using real-time polymerase chain reaction (RT-PCR).

RESULTS AND CONCLUSION

Risk score and stage based on the model could be seen as separate prognostic indicators for patients with LUAD. The main signaling pathway difference across various risk groups was in cell cycle. The immunoinfiltration profile of the tumor microenvironment (TME) and individuals with different risk levels were correlated, suggesting that the interaction of immune cells with the tumor led to the creation of a favorable immunosuppressive microenvironment. These discoveries aid in the creation of individualized therapies for LUAD patients.

Development and validation of a clinic machine-learning nomogram for the prediction of risk stratifications of prostate cancer based on functional subsets of peripheral lymphocyte

BACKGROUND

Non-invasive risk stratification contributes to the precise treatment of prostate cancer (PCa). In previous studies, lymphocyte subsets were used to differentiate between low-/intermediate-risk and high-risk PCa, with limited clinical value and poor interpretability. Based on functional subsets of peripheral lymphocyte with the largest sample size to date, this study aims to construct an easy-to-use and robust nomogram to guide the tripartite risk stratifications for PCa.

METHODS

We retrospectively collected data from 2039 PCa and benign prostate disease (BPD) patients with 42 clinical characteristics on functional subsets of peripheral lymphocyte. After quality control and feature selection, clinical data with the optimal feature subset were utilized for the 10-fold cross-validation of five Machine Learning (ML) models for the task of predicting low-, intermediate- and high-risk stratification of PCa. Then, a novel clinic-ML nomogram was constructed using probabilistic predictions of the trained ML models via the combination of a multivariable Ordinal Logistic Regression analysis and the proposed feature mapping algorithm.

RESULTS

197 PCa patients, including 56 BPD, were enrolled in the study. An optimal subset with nine clinical features was selected. Compared with the best ML model and the clinic nomogram, the clinic-ML nomogram achieved the superior performance with a sensitivity of 0.713 (95% CI 0.573-0.853), specificity of 0.869 (95% CI 0.764-0.974), F1 of 0.699 (95% CI 0.557-0.841), and AUC of 0.864 (95% CI 0.794-0.935). The calibration curve and Decision Curve Analysis (DCA) indicated the predictive capacity and net benefits of the clinic-ML nomogram were improved.

CONCLUSION

Combining the interpretability and simplicity of a nomogram with the efficacy and robustness of ML models, the proposed clinic-ML nomogram can serve as an insight tool for preoperative assessment of PCa risk stratifications, and could provide essential information for the individual diagnosis and treatment in PCa patients.

Exploration of prognosis and immunometabolism landscapes in ER+ breast cancer based on a novel lipid metabolism-related signature

Introduction

Lipid metabolic reprogramming is gaining attention as a hallmark of cancers. Recent mounting evidence indicates that the malignant behavior of breast cancer (BC) is closely related to lipid metabolism. Here, we focus on the estrogen receptor-positive (ER+) subtype, the most common subgroup of BC, to explore immunometabolism landscapes and prognostic significance according to lipid metabolism-related genes (LMRGs).

Methods

Samples from The Cancer Genome Atlas (TCGA) database were used as training cohort, and samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), Gene Expression Omnibus (GEO) datasets and our cohort were applied for external validation. The survival-related LMRG molecular pattern and signature were constructed by unsupervised consensus clustering and least absolute shrinkage and selection operator (LASSO) analysis. A lipid metabolism-related clinicopathologic nomogram was established. Gene enrichment and pathway analysis were performed to explore the underlying mechanism. Immune landscapes, immunotherapy and chemotherapy response were further explored. Moreover, the relationship between gene expression and clinicopathological features was assessed by immunohistochemistry.

Results

Two LMRG molecular patterns were identified and associated with distinct prognoses and immune cell infiltration. Next, a prognostic signature based on nine survival-related LMRGs was established and validated. The signature was confirmed to be an independent prognostic factor and an optimal nomogram incorporating age and T stage (AUC of 5-year overall survival: 0.778). Pathway enrichment analysis revealed differences in immune activities, lipid biosynthesis and drug metabolism by comparing groups with low- and high-risk scores. Further exploration verified different immune microenvironment profiles, immune checkpoint expression, and sensitivity to immunotherapy and chemotherapy between the two groups. Finally, arachidonate 15-lipoxygenase (ALOX15) was selected as the most prominent differentially expressed gene between the two groups. Its expression was positively related to larger tumor size, more advanced tumor stage and vascular invasion in our cohort (n = 149).

Discussion

This is the first lipid metabolism-based signature with value for prognosis prediction and immunotherapy or chemotherapy guidance for ER+ BC.

A novel predictive model based on inflammatory response-related genes for predicting endometrial cancer prognosis and its experimental validation

Inflammatory response is an important feature of most tumors. Local inflammation promotes tumor cell immune evasion and chemotherapeutic drug resistance. We aimed to build a prognostic model for endometrial cancer patients based on inflammatory response-related genes (IRGs). RNA sequencing and clinical data for uterine corpus endometrial cancer were obtained from TCGA datasets. LASSO-penalized Cox regression was used to obtain the risk formula of the model: the score = e^{sum(corresponding coefficient × each gene's expression)}. The "ESTIMATE" and "pRRophetic" packages in R were used to evaluate the tumor microenvironment and the sensitivity of patients to chemotherapy drugs. Data sets from IMvigor210 were used to evaluate the efficacy of immunotherapy in cancer patients. For experimental verification, 37 endometrial cancer and 43 normal endometrial tissues samples were collected. The mRNA expression of the IRGs was measured using qRT-PCR. The effects of IRGs on the malignant biological behaviors of endometrial cancer were detected using CCK-8, colony formation, Transwell invasion, and apoptosis assays. We developed a novel prognostic signature comprising 13 IRGs, which is an independent prognostic marker for endometrial cancer. A nomogram was developed to predict patient survival accurately. Three key IRGs (LAMP3, MEP1A, and ROS1) were identified in this model. Furthermore, we verified the expression of the three key IRGs using qRT-PCR. Functional experiments also confirmed the influence of the three key IRGs on the malignant biological behavior of endometrial cancer. Thus, a characteristic model constructed using IRGs can predict the survival, chemotherapeutic drug sensitivity, and immunotherapy response in patients with endometrial cancer.

G-MDSC-derived exosomes mediate the differentiation of M-MDSC into M2 macrophages promoting colitis-to-cancer transition

BACKGROUNDS

In inflammatory bowel disease microenvironment, transdifferentiation of myeloid-derived suppressor cells (MDSCs) and M2 macrophage accumulation are crucial for the transition of colitis-to-cancer. New insights into the cross-talk and the underling mechanism between MDSCs and M2 macrophage during colitis-to-cancer transition are opening new avenues for colitis-associated cancer (CAC) prevention and treatment.

METHODS

The role and underlying mechanism that granulocytic MDSCs (G-MDSCs) or exosomes (Exo) regulates the differentiation of monocytic MDSCs (M-MDSCs) into M2 macrophages were investigated using immunofluorescence, FACS, IB analysis, etc, and employing siRNA and antibodies. In vivo efficacy and mechanistic studies were conducted with dextran sulfate sodium-induced CAC mice, employed IL-6 Abs and STAT3 inhibitor.

RESULTS

G-MDSCs promote the differentiation of M-MDSC into M2 macrophages through exosomal miR-93-5 p which downregulating STAT3 activity in M-MDSC. IL-6 is responsible for miR-93-5 p enrichment in G-MDSC exosomes (GM-Exo). Mechanistically, chronic inflammation-driven IL-6 promote the synthesis of miR-93-5 p in G-MDSC via IL-6R/JAK/STAT3 pathway. Early use of IL-6 Abs enhances the effect of STAT3 inhibitor against CAC.

CONCLUSIONS

IL-6-driven secretion of G-MDSC exosomal miR-93-5 p promotes the differentiation of M-MDSC into M2 macrophages and involves a STAT3 signaling mechanism that promote colitis-to-cancer transition. Combining STAT3 inhibitors with strategies that inhibit IL-6-mediated G-MDSC exosomal miR-93-5 p production is beneficial for the prevention and treatment of CAC.

Development and validation of a new prognostic immune–inflammatory–nutritional score for predicting outcomes after curative resection for intrahepatic cholangiocarcinoma: A multicenter study

Background

Immune function, nutrition status, and inflammation influence tumor initiation and progression. This was a retrospective multicenter cohort study that investigated the prognostic value and clinical relevance of immune-, inflammatory-, and nutritional-related biomarkers to develop a novel prognostic immune–inflammatory–nutritional score (PIIN score) for patients with intrahepatic cholangiocarcinoma (ICC).

Methods

The clinical data of 571 patients (406 in the training set and 165 in the validation set) were collected from four large hepato-pancreatico-biliary centers of patients with ICC who underwent surgical resection between January 2011 and September 2017. Twelve blood biomarkers were collected to develop the PIIN score using the LASSO Cox regression model. The predictive value was further assessed using validation datasets. Afterward, nomograms combining the PIIN score and other clinicopathological parameters were developed and validated based on the calibration curve, time-dependent AUC curves, and decision curve analysis (DCA). The primary outcomes evaluated were overall survival (OS) and recurrence-free survival (RFS) from the day of primary resection of ICC.

Results

Based on the albumin–bilirubin (ALBI) grade, neutrophil- to- lymphocyte ratio (NLR), prognostic nutritional index (PNI), and systemic immune- inflammation index (SII) biomarkers, the PIIN score that classified patients into high-risk and low-risk groups could be calculated. Patients with high-risk scores had shorter OS (training set, p &lt; 0.001; validation set, p = 0.003) and RFS (training set, p &lt; 0.001; validation set, p = 0.002) than patients with low-risk scores. The high PIIN score was also associated with larger tumors (≥5 cm), lymph node metastasis (N1 stage), multiple tumors, and high tumor grade or TNM (tumor (T), nodes (N), and metastases (M)) stage. Furthermore, the high PIIN score was a significant independent prognostic factor of OS and RFS in both the training (p &lt; 0.001) and validation (p = 0.003) cohorts, respectively. A PIIN-nomogram for individualized prognostic prediction was constructed by integrating the PIIN score with the clinicopathological variables that yielded better predictive performance than the TNM stage.

Conclusion

The PIIN score, a novel immune–inflammatory–nutritional-related prognostic biomarker, predicts the prognosis in patients with resected ICC and can be a reliable tool for ICC prognosis prediction after surgery. Our study findings provide novel insights into the role of cancer-related immune disorders, inflammation, and malnutrition.

Brain-body pathways linking racism and health

Racial disparities in health are a major public health problem in the United States, especially when comparing chronic disease morbidity and mortality for Black versus White Americans. These health disparities are primarily due to insidious anti-Black racism that permeates American history, current culture and institutions, and interpersonal interactions. But how does racism get under the skull and the skin to influence brain and bodily processes that impact the health of Black Americans? In the present article, we present a model describing the possible neural and inflammatory mechanisms linking racism and health. We hypothesize that racism influences neural activity and connectivity in the salience and default mode networks of the brain and disrupts interactions between these networks and the executive control network. This pattern of neural functioning in turn leads to greater sympathetic nervous system signaling, hypothalamic-pituitary-adrenal axis activation, and increased expression of genes involved in inflammation, ultimately leading to higher levels of proinflammatory cytokines in the body and brain. Over time, these neural and physiological responses can lead to chronic physical and mental health conditions, disrupt well-being, and cause premature mortality. Given that research in this area is underdeveloped to date, we emphasize opportunities for future research that are needed to build a comprehensive mechanistic understanding of the brain-body pathways linking anti-Black racism and health. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Fermented natural product targeting gut microbiota regulate immunity and anti-inflammatory activity: A possible way to prevent COVID-19 in daily diet

Low immune function makes the body vulnerable to being invaded by external bacteria or viruses, causing influenza and inflammation of various organs, and this trend is shifting to the young and middle-aged group. It has been pointed out that natural products fermented by probiotic have benign changes about their active ingredients in some studies, and it have shown strong nutritional value in anti-oxidation, anti-aging, regulating lipid metabolism, anti-inflammatory and improving immunity. In recent years, the gut microbiota plays a key role and has been extensively studied in improving immunity and anti-inflammation activity. By linking the relationship between natural products fermented by probiotic, gut microbiota, immunity, and inflammation, this review presents the modulating effects of probiotics and their fermented natural products on the body, including immunity-enhancing and anti-inflammatory activities by modulating gut microbiota, and it is discussed that the current understanding of its molecular mechanisms. It may become a possible way to prevent COVID-19 through consuming natural products fermented by probiotic in our daily diet.

The correlation between neutrophil-to-lymphocyte ratio, carcinoembryonic antigen, and carbohydrate antigen 153 levels with chemotherapy-related cognitive impairment in early-stage breast cancer patients

Background

The changes in inflammation and tumor biomarkers are associated with the anti-tumor immunological processes. Early detection and intervention are of great significance to the clinical management of cancer-related diseases. Peripheral blood biomarkers [e.g., neutrophil-to-lymphocyte ratio (NLR), carcinoembryonic antigen (CEA), and carbohydrate antigen 153 (CA153)] are obtained in real-timely, conveniently, and less invasively, and proved to availably predicted the disease states and prognosis of various cancers, including breast cancer (BC). Inflammation and poor disease management promote cognitive impairment. Chemotherapy-related cognitive impairment (CRCI) hazard long-term survival and quality of life (QOL) of BC patients, but its correlation with NLR, CEA, and CA153 is not clear.

Purpose

This study aimed to investigate changes in NLR, CEA, and CA153 levels before and after chemotherapy and their correlation with CRCI in patients with early-stage BC.

Materials and methods

The 187 patients with BC who were measured for NLR, CEA, and CA153 values within the first 24 hours of admission, were assigned into two groups: the before/after chemotherapy group (BCG/ACG). The ACG was assigned into two subgroups based on the cognitive assessment results: the cognitive normal/impaired group (CNG/CIG). Patients’ self-perceived cognitive impairments were evaluated using a mini-mental state examination (MMSE), prospective and retrospective memory (PM and RM) questionnaire (PRMQ), and functional assessment of cancer therapy-cognitive function version 3 (FACT-Cog, version 3, including CogPCI, CogOth, CogPCA, and CogQOL). Their QOL was also evaluated.

Results

The NLR and CA153 levels were elevated after chemotherapy (BCG vs ACG: Z = −1.996 and −1.615, P = 0.046 and 0.106, respectively), and significantly elevated in patients with CRCI (BCG vs CIG: Z = −2.444 and -2.293, P = 0.015 and 0.022; respectively). However, there was not reach significant difference in CEA levels between the four groups. In addition, there was a weak to moderate correlation between peripheral blood biomarkers (NLR, CEA, and CA153) levels and CRCI (r = −0.404, −0.205, −0.322; respectively; P < 0.001). Cognitive impairment scores (MMSE, PM, RM, and FACT-Cog) had a strong correlation with QOL in patients with early-stage BC (r = −0.786, 0.851, 0.849, and 0.938; respectively; P < 0.001).

Conclusion

NLR and CA153 m be valuable diagnostic adjuncts of CRCI, and CRCI has a strong correlation with QOL in patients with early-stage BC.

Cancer Health Disparities Research Training: A Qualitative Report

The Research Education Core of the Pacific Islands Partnership for Cancer Health Equity (PIPCHE) conducted a systematic review of participant learning. All students from both the University of Guam and the University of Hawai'i who have completed the program were asked two open-ended questions, which were then thematically analyzed. (1) What impact did the training have on your career? (2) What did you learn about cancer health disparities? Findings include themes such as expanding social networks, building professional skills, providing opportunities and funding, inspiring a future career in research, and giving back to the community. The results also indicate that students learned that cancer disparities research was complex and diverse, required cultural sensitivity, different areas of cancer research and education, the importance of mentor and peer relationships. Trainees spoke very favorably about the weekly seminar format. These findings are consistent with studies in other similar programs. The authors recommend future educational outcome research.