Comprehensive evaluation of clinical outcomes in hepatic epithelioid hemangioendothelioma subsets: insights from SEER Database and departmental cohort analysis.

Background: Epithelioid hemangioendothelioma (EHE), is an uncommon, intermediate-grade malignant vascular tumor that can manifest in diverse organs, including the liver, lungs, and bones. Given its unique malignancy profile and rarity, there lacks a consensus on a standardized treatment protocol for EHE, particularly for hepatic epithelioid hemangioendothelioma (HEHE). This study aims to elucidate factors influencing the clinical prognosis of EHE by analyzing data from the SEER database, complemented with insights from a departmental cohort of 9 HEHE cases. Through this, we hope to shed light on potential clinical outcomes and therapeutic strategies for HEHE. Methods: Using SEER data from 22 registries, we analyzed 313 liver cancer patients with ICD-O-3 9130 and 9133 histology. Twelve variables were examined using Cox regression and mlr3 machine learning. Significant variables were identified and compared. Clinical data, imaging characteristics, and treatment methods of nine patients from our cohort were also presented. Result: In univariate and multivariate Cox regression analyses, Age, Sex, Year of diagnosis, Surgery of primary site, Chemotherapy, and Median household income were closely related to survival outcomes. Among the ten survival-related machine learning models, CoxPH, Flexible, Mboost, and Gamboost stood out based on Area Under the Curve(AUC), Decision Curve Analysis(DCA), and Calibration Curve Metrics. In the feature importance analysis of these four selected models, Age and Surgery of primary site were consistently identified as the most critical factors influencing prognosis. Additionally, the clinical data of nine patients from our cohort not only demonstrated unique imaging characteristics of HEHE but also underscored the importance of surgical intervention. Conclusion: For patients with resectable HEHE, surgical treatment is currently a highly important therapeutic approach.

Exploration of immune cell heterogeneity by single-cell RNA sequencing and identification of secretory leukocyte protease inhibitor as an oncogene in pancreatic cancer.

Clinical outcomes remain unsatisfactory in patients with pancreatic cancer (PAC). In this study, through single-cell sequencing, we identified eight cell subpopulations in the tumor microenvironment (TME). Redimensional clustering of epithelial cells, myeloid cells, and cancer-associated fibroblasts (CAFs) revealed heterogeneity in the TME of PAC. Intercellular communication analysis showed strong direct interactions between matrix CAFs, inflammatory CAFs, and epithelial cells. Additionally, we found that the SPP1-associated pathway was activated in monocytes, whereas the vascular endothelial growth factor-associated pathway was activated in epithelial cells. These results improve the understanding of the TME of pancreatic cancer and provide a foundation for further studies on intratumoral heterogeneity. In addition, differentially expressed gene secretory leukocyte protease inhibitor (SLPI) was identified in pancreatic cancer, and functional experiments showed that SLPI had a strong impact on cell viability and apoptosis, which offers a potential therapy target for pancreatic cancer.

Prognostic Value and Clinical Significance of LIPH in Breast Cancer.

BACKGROUND: Lipase member H (LIPH), a novel member of the mammalian triglyceride lipase family, is localized on human chromosome 3q27-q28. Exploration of the importance of the new cancer-related gene LIPH in several carcinomas has been reported in previous studies. Our study aims to systematically assess the expression pattern of LIPH in breast cancer. METHODS: Our study explored 2994 breast cancer samples with transcriptome data from the Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets. We systematically evaluated the mRNA expression of LIPH in breast cancer and the overall survival (OS) of patients. The protein expression of LIPH in breast cancer was evaluated with the Human Protein Atlas. We also explored the relationship between LIPH and the immune microenvironment in pan-cancer. RESULTS: Both mRNA and protein expression LIPH were found to be upregulated in breast cancer tumors. The overall survival rate of patients with high LIPH expression was lower than those of patients with low LIPH expression in both the TCGA dataset (p=0.0067) and METABRIC dataset (p<0.0001). Outcomes of the multivariate analysis found that the level of LIPH expression was an independent prognostic factor in both TCGA (p=0.001) and METABRIC (p=0.019) databases. The outcomes of the univariate analysis showed that LIPH was an important prognostic factor (p=0.01 in TCGA dataset, p=0.001 in METABRIC dataset). In the TCGA dataset, outcomes showed that LIPH expression was negatively correlated with the AJCC (American Joint Committee on Cancer) stage (p=2.3e-05) and triple-negative breast cancer (TNBC) tissues (p=3.1e-10). High LIPH expression showed lower OS in the TNBC subtype (P=0.011). CONCLUSION: Compared to normal tissues, the expression of LIPH was higher in breast cancer tissues in both mRNA and protein levels. This study showed that the high level of LIPH expression might be related to the worse prognosis of breast cancer.

Research Progresses in Immunological Checkpoint Inhibitors for Breast Cancer Immunotherapy.

Tumor immune escape refers to the phenomenon in which tumor cells escape the recognition and attack of the body's immune system through various mechanisms so that they can survive and proliferate in vivo. The imbalance of immune checkpoint protein expression is the primary mechanism for breast cancer to achieve immune escape. Cytotoxic T lymphocyte antigen 4 (CTLA4) and programmed cell death protein 1 (PD-1)/programmed cell death protein-ligand 1 (PD-L1) are critical immune checkpoints for breast cancer. Immune checkpoint inhibitors block the checkpoint and relieve its inhibition effect on immune cells, reactivate T-cells and destroy cancer cells and restore the body's ability to resist tumors. At present, immunological checkpoint inhibitors have made significant progress in breast cancer immunotherapy, and it is expected to become a new treatment for breast cancer.

Effects of dezocine on morphine tolerance and opioid receptor expression in a rat model of bone cancer pain.

BACKGROUND: Clinically, the coadministration of opioids to enhance antinociception and decrease tolerance has attracted increasing research attention. We investigated the effects of dezocine, a mu- and kappa-opioid receptor agonist/antagonist, on morphine tolerance and explored the involvement of opioid receptor expression in a rat model of bone cancer pain. METHODS: Thermal nociceptive thresholds were measured after the subcutaneous injection of morphine (10 mg/kg) alone or combined with dezocine (10 or 1 mg/kg) for 7 consecutive days. Real-time PCR and western blot analysis were used to examine opioid receptor expression in the periaqueductal gray (PAG) and spinal cord. RESULTS: The analgesic effect was significantly decreased after 4 days of morphine administration. We observed that low-dose dezocine significantly attenuated morphine tolerance without reducing the analgesic effect of morphine. Low-dose dezocine coadministration significantly reversed the downregulated expression of mu (MOR) and delta (DOR) opioid receptors in the PAG and the upregulated expression of kappa (KOR) and DOR in the spinal cord induced by morphine. Moreover, low-dose dezocine coadministered with morphine significantly inhibited KOR expression in both the PAG and spinal cord. CONCLUSIONS: The combination of low-dose dezocine with morphine may prevent or delay the development of morphine tolerance in a rat model of bone cancer pain. The regulation of opioid receptor expression in the PAG and spinal cord may be part of the mechanism.

NCCBM, a Nomogram Prognostic Model in Breast Cancer Patients With Brain Metastasis.

Purpose: Nomogram prognostic models could greatly facilitate risk stratification and treatment strategies for cancer patients. We developed and validated a new nomogram prognostic model, named NCCBM, for breast cancer patients with brain metastasis (BCBM) using a large BCBM cohort from the SEER (Surveillance, Epidemiology, and End Results) database. Patients and Methods: Clinical data for 975 patients diagnosed from 2011 to 2014 were used to develop the nomogram prognostic model. The predictive accuracy and discriminative ability of the nomogram were determined by concordance index (C-index) and calibration curve. The results were validated using an independent cohort of 542 BCBM patients diagnosed from 2014 to 2015. Results: The following variables were selected in the final prognostic model: age, race, surgery, radiation therapy, chemotherapy, laterality, grade, molecular subtype, and extracranial metastatic sites. The C-index for the model described here was 0.69 (95% CI, 0.67 to 0.71). The calibration curve for probability of survival showed good agreement between prediction by nomogram and actual observation. The model was validated in an independent validation cohort with a C-index of 0.70 (95% CI, 0.68 to 0.73). Conclusion: We developed and validated a nomogram prognostic model for BCBM patients, and the proposed nomogram resulted in good performance.

JAK2 expression is correlated with the molecular and clinical features of breast cancer as a favorable prognostic factor.

Janus kinases are a family of non-receptor tyrosine kinases involved in autoimmune diseases and malignancies. In breast cancer, the immune related role of JAK2 remains unclear. We aimed to investigate its role at transcriptome level and its relationship with the clinical outcome of breast cancer. This study enrolled a total of 2994 breast cancer samples with transcriptome data, including 1090 samples from The Cancer Genome Atlas (TCGA) and 1904 from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). JAK2 expression was significantly upregulated in both PR positive group (P < 0.01) and HER2 negative group (P < 0.01), and was correlated with American Joint Committee on Cancer (AJCC) stage and tumor malignancies of breast cancer. Functional enrichment analysis revealed that genes correlated with JAK2 were mainly involved in essential functions associated with immune response. Intriguingly, we investigated the association between JAK2 and immune modulators in pan-cancer, JAK2 expression was positively correlated with most of these immune modulators. In clinical aspect, higher expression of JAK2 was an independent indicator of favorable prognosis in breast cancer patients. The expression of JAK2 is tightly related to the pathology and molecular pathology of breast cancer, and synergistic with other checkpoint members thereby playing a specific role in regulating tumor immune microenvironment. To our knowledge, this is the largest and most comprehensive study characterizing the expression pattern of JAK2 and its special immune functions together with its prognostic values in breast cancer. These findings might shed novel sights for future research in cancer immunotherapy by targeting immune checkpoint molecules.

Conditional Survival of female patients with operable invasive Breast Cancer in US: A population-based study.

Background: Conditional survival (CS) is used to describe the dynamic possibility of survival, considering the changes of death risk with time lapsing. This study aimed to estimate the conditional disease-specific survival (CDS) for the female with operable invasive breast cancer. Methods: The data was obtained from Surveillance, Epidemiology, and End Result Program of the National Cancer Institute. The hazard rate was calculated using kernel density smoothing method. The disease-specific survival (DSS) rates were estimated and compared using Kaplan-Meier method and log-rank test. The Cox regression model was used to adjust confounding factors. The CDS was calculated by CDS(y|x)=DSS(x + y)/DSS(x), where DSS(x) representatives the DSS at x year. Results: The 5-year, 10-year, and 15-year DSS was 88.7%, 82.0%, and 78.3%, respectively. The hazard rate after surgery increased initially and peaked at about 1.5 years, then decreased gradually. Meanwhile, the CDS decreased just after surgery then increased continuously, which showed a contrary trend with hazard rate. Patients with high risk factors had greater survival gap between cumulative DSS and CDS. The changing trend of CDS in patients with high risk factors was more significant, and the CDS gap between low-risk patients and high-risk patients gradually decreased over time. Conclusion: CS could provide a more precise long-term prognostic evaluation compared to traditional cumulative survival, especially for long-time survivors with high risk.

A systematic review: comparative analysis of the effects of propofol and sevoflurane on postoperative cognitive function in elderly patients with lung cancer.

BACKGROUND: The potential risk for cognitive impairment following surgery and anesthesia is a common concern, especially in the elderly and more fragile patients. The risk for various neurocognitive effects is thus an area of importance. The independent impact of surgery and anesthesia is still not known. Likewise, the independent effect of different drugs used during anesthesia is a matter of debate, as is the number and amounts of drugs used and the "depth of anesthesia." So, understanding the drug-related phenomenon and mechanisms for postoperative cognitive impairment is essential. This meta-analysis aims to compare the effects of propofol and sevoflurane anesthesia on postoperative cognitive function in elderly patients with lung cancer. METHODS: This study is a systematic review and meta-analysis for controlled clinical studies. Public-available online databases were searched to identify eligible randomized placebo-controlled trials or prospective cohort studies concerning the effects of propofol and sevoflurane on postoperative cognitive function. The primary endpoints are postoperative mini-mental state examination (MMSE) scores at various time points; the secondary endpoint is the serum S100beta concentration 24 h after surgery. Standard mean differences (SMDs) along with 95% confidence intervals (CIs) were extracted and analyzed using random or fixed-effects models. Analyses regarding heterogeneity, risk of bias assessment, and sensitivity were performed. RESULTS: We searched 1626 eligible publications and 14 studies of 1404 patients were included in the final analysis. The majority of included studies had been undertaken in Asian populations. Results suggested that propofol has a greater adverse effect on cognitive function in the elderly patients with lung cancer than sevoflurane. There were significant differences in issues of MMSE 6 h (11 studies; SMD -1.391, 95% CI -2.024, - 0.757; p < 0.001), MMSE 24 h (14 studies; SMD -1.106, 95% CI -1.588, - 0.624; p < 0.001), MMSE 3d (11 studies; SMD -1.065, 95% CI -1.564, - 0.566; p < 0.001), MMSE 7d (10 studies; SMD -0.422, 95% CI -0.549, - 0.295; p < 0.001), and serum S100beta concentration at 1 day after surgery (13 studies; SMD 0.746, 95% CI 0.475, 1.017; p < 0.001). CONCLUSION: Propofol has a more significant adverse effect on postoperative cognitive function in elderly patients with lung cancer than sevoflurane.

Artificial intelligence: a key to relieve China's insufficient and unequally-distributed medical resources.

In this manuscript, we firstly reviewed the challenges faced by China in its health care reform. Though Chinese governments have made tremendous efforts, problems like the difficulties and high expense in medical care and the nervous doctor-patient relationship have been reported a lot, whose key problem is the insufficiency of high-quality medical resource and the supply-demand imbalance. Presently, it's almost old news: artificial intelligence will overturn the existing medical model. Artificial intelligence technology will transform the medical sector and trigger an estimated $147 billion market during the next 20 years. We hereby pointed out the strengths of medical artificial intelligence and its potentials to relieve China's insufficient and unequally-distributed medical resources. Also, we analyzed China's advantages in developing medical AI due to its huge medical big data and China government's powerful promotion policy. Finally, we put forward some challenges for China to practice this.

LINC01355 suppresses breast cancer growth through FOXO3-mediated transcriptional repression of CCND1.

Previously, several protein-coding tumor suppressors localized at 1p36 have been reported. In the present work, we focus on functional long non-coding RNAs (lncRNAs) embedded in this locus. Small interfering RNA was used to identify lncRNA candidates with growth-suppressive activities in breast cancer. The mechanism involved was also explored. LINC01355 were downregulated in breast cancer cells relative to non-malignant breast epithelial cells. Overexpression of LINC01355 significantly inhibited proliferation, colony formation, and tumorigenesis of breast cancer cells. LINC01355 arrested breast cancer cells at the G0/G1 phase by repressing CCND1. Moreover, LINC01355 interacted with and stabilized FOXO3 protein, leading to transcriptional repression of CCND1. Importantly, LINC01355-mediated suppression of breast cancer growth was reversed by knockdown of FOXO3 or overexpression of CCND1. Clinically, LINC01355 was downregulated in breast cancer specimens and correlated with more aggressive features. There was a negative correlation between LINC01355 and CCND1 expression in breast cancer samples. LINC01355 acts as a tumor suppressor in breast cancer, which is ascribed to enhancement of FOXO3-mediated transcriptional repression of CCND1. Re-expression of LINC01355 may provide a potential therapeutic strategy to block breast cancer growth and progression.