Sex-Based Differences in Melanoma Survival Improvement from 2004 to 2018.

Background: Melanoma is the deadliest form of skin cancer and its incidence and mortality vary by sex, age, race, and socioeconomic status. Relatively few studies, however, have characterized disparities in survival improvement across these demographic groups in melanoma. METHODS: Survival data from the Surveillance, Epidemiology, and End Results (SEER) database were obtained from 2004 to 2018. The compiled data were analyzed for cancer-specific survival (CSS) to produce multivariable Cox regressions that estimate sex-based survival disparities across patient demographic groups. Additionally, time-to-progression and survival analyses were conducted for a cohort of patients with carcinoma-in situ (CIS) that developed into melanoma. RESULTS: In both female and male patients, melanoma diagnosis in more recent years (2014-2018 versus 2004-2008) was associated with an improved CSS, with females demonstrating an HR of 0.55 (95% CI: 0.49-0.60) and males demonstrating an HR of 0.49 (0.46-0.53). The trend remained consistent upon analyzing the effects of both sex and race on survival improvement for White and Hispanic males and females, but the results were not significant for Black and Asian patients. Joint sex and age analysis demonstrated significant reductions in HR across all age groups for female and male patients with a diagnosis in more recent years. Analysis of lesions progressing from CIS to melanoma (high-risk CIS) demonstrated an increased OR for males over females (OR: 1.70; 95% CI: 1.55-1.85), while survival analysis demonstrated no difference between sexes in the HR. Finally, for male patients, high-risk CIS demonstrated worse CSS compared to female patients with high-risk CIS (OR: 1.43; 95% CI: 1.15-1.79). CONCLUSION: Overall, melanoma survival has improved in recent years, though some patient subgroups have experienced a lower improvement in survival from 2004 to 2018.

DeepCG: A cell graph model for predicting prognosis in lung adenocarcinoma.

Lung cancer is the first leading cause of cancer-related death in the United States, with lung adenocarcinoma as the major subtype accounting for 40% of all cases. To improve patient survival, image-based prognostic models were developed due to the ready availability of pathological images at diagnosis. However, the application of these models is hampered by two main challenges: the lack of publicly available image datasets with high-quality survival information and the poor interpretability of conventional convolutional neural network models. Here, we integrated matched transcriptomic and H&E staining data from TCGA (The Cancer Genome Atlas) to develop an image-based prognostic model, termed Deep-learning based Cell Graph (DeepCG) model. Instead of survival data, we used a gene signature to predict patient prognostic risks, which was then used as labels for training DeepCG. Importantly, by employing graph structures to capture cell patterns, DeepCG can provide cell-level interpretation, which was more biologically relevant than previous region-level insights. We validated the prognostic values of DeepCG in independent datasets and demonstrated its ability to identify prognostically informative cells in images.

Effect of telehealth interventions on adherence to endocrine therapy among patients with breast cancer: a systematic review and meta-analysis.

PURPOSE: To evaluate the effect of telehealth interventions on adherence to endocrine therapy among patients with breast cancer. METHODS: A systematic search of five English databases (PubMed, Web of Science, Embase, the American Psychological Association PsycNet, and the Cochrane Library) and four Chinese databases (Chinese National Knowledge Infrastructure, SinoMed, WanFang Data, and WeiPu Data) was performed from inception to March 31, 2023. Two investigators independently screened the available studies for eligibility and extracted relevant data. Quality assessment was conducted using the Cochrane Risk of Bias Tool. The effect size was computed based on the risk ratio for dichotomous data and standardized mean difference for continuous data using Review Manager 5.4. RESULTS: A total of 1,780 participants from eight randomized controlled trials were included. These studies involved treatment with aromatase inhibitors only (n = 3) or aromatase inhibitors plus tamoxifen (n = 5). Telehealth interventions involved web-based interventions, telephone-based interventions, interventions via mobile applications, and interventions based on technology. In three studies, subjective measures were used, while objective measures were utilized in another three. Two studies incorporated a combination of both subjective and objective measures. The duration of the interventions varied among studies, ranging from a week to 36 months. The follow-up duration ranged from 4 weeks to 36 months. The quality of included studies was moderate to high. The meta-analysis of the five studies reporting dichotomous data showed that telehealth interventions had a significant effect on adherence to endocrine therapy (RR = 0.86, 95% CI = 0.76-0.97). Moreover, four studies reported continuous data. The meta-analysis demonstrated that telehealth interventions significantly improved adherence to endocrine therapy at 1 month (SMD = 0.50, 95% CI = 0.10-0.90), 3 months (SMD = 0.58, 95% CI = 0.17-0.99), and 6 months (SMD = 0.27, 95% CI = 0.08-0.47) of follow-up. CONCLUSION: Telehealth interventions may facilitate adherence to endocrine therapy among patients with breast cancer. Further research should adopt a theory-based design and explore the longer-term effects.

Uncertainty Evaluation of Soil Heavy Metal(loid) Pollution and Health Risk in Hunan Province: A Geographic Detector with Monte Carlo Simulation.

Research on soil heavy metal(loid) pollution and health risk assessment is extensive, but a notable gap exists in systematically examining uncertainty in this process. We employ the Nemerow index, the health risk assessment model, and the geographic detector model (GDM) to analyze soil heavy metal(loid) pollution, assess health risks, and identify driving factors in Hunan Province, China. Furthermore, the Monte Carlo simulation (MCS) method is utilized to quantitatively evaluate the uncertainties associated with the sampling point positions, model parameters, and classification boundaries of the driving factors in these processes. The experimental findings reveal the following key insights: (1) Regions with high levels of heavy metal(loid) pollution, accompanied by low uncertainty, are identified in Chenzhou and Hengyang Cities in Hunan Province. (2) Arsenic (As) and chromium (Cr) are identified as the primary contributors to health risks. (3) The GDM results highlight strong nonlinear enhanced interactions among lithology and other factors. (4) The input GDM factors, such as temperature, river distance, and gross domestic product (GDP), show high uncertainty on the influencing degree of soil heavy metal(loid) pollution. This study thoroughly assesses high heavy metal(loid) pollution in Hunan Province, China, emphasizing uncertainty and offering a scientific foundation for land management and pollution remediation.

Reduced Racial Disparity as a Result of Survival Improvement in Prostate Cancer.

Prostate cancer is a cancer type associated with a high level of racial and socioeconomic disparities as reported by many previous studies. However, the changes in these disparities in the past two decades have not been systematically studied. In this study, we investigated the Surveillance Epidemiology End Results (SEER) data for prostate cancer patients diagnosed during 2004-2018. African Americans and Asians showed significantly better and worse cancer-specific survival (CSS), respectively, compared to non-Hispanic white individuals after adjusting for confounding factors such as age and cancer stage. Importantly, the data indicated that racial disparities fluctuated and reached the highest level during 2009-2013, and thereafter, it showed a substantial improvement. Such a change cannot be explained by the improvement in early diagnosis but is mainly driven by the differential improvement in CSS between races. Compared with Asians and non-Hispanic whites, African American patients achieved a more significant survival improvement during 2014-2018, while no significant improvement was observed for Hispanics. In addition, the SEER data showed that high-income patients had significantly longer CSS than low-income patients. Such a socioeconomic disparity was continuously increasing during 2004-2018, which was caused by the increased survival benefits of the high-income patients with respect to the low-income patients. Our study suggests that more efforts and resources should be allocated to improve the treatment of patients with low socioeconomic status.

TimiGP: Inferring cell-cell interactions and prognostic associations in the tumor immune microenvironment through gene pairs.

Determining the prognostic association of different immune cell types in the tumor microenvironment is critical for understanding cancer biology and developing new therapeutic strategies. However, this is challenging in certain cancer types, where the abundance of different immune subsets is highly correlated. In this study, we develop a computational method named TimiGP to overcome this challenge. Based on bulk gene expression and survival data, TimiGP infers cell-cell interactions that reveal the association between immune cell relative abundance and prognosis. As demonstrated in metastatic melanoma, TimiGP prioritizes immune cells critical in prognosis based on the identified cell-cell interactions. Highly consistent results are obtained by TimiGP when applied to seven independent melanoma datasets and when different cell-type marker sets are used as inputs. Additionally, TimiGP can leverage single-cell RNA sequencing data to delineate the tumor immune microenvironment at high resolutions across a wide range of cancer types.

Nocardia farcinica Pneumonia with Sepsis and a Bronchial Neoplasm in a Healthy Patient: A Case Report.

There are few reports on sepsis caused by infection with Nocardia in people with normal immune function, and there is no report on bronchial tumor caused by Nocardia. This paper describes a case of Nocardia farcinica pneumonia with sepsis and a bronchial neoplasm in a healthy patient.

Binding peptide generation for MHC Class I proteins with deep reinforcement learning.

MOTIVATION: MHC Class I protein plays an important role in immunotherapy by presenting immunogenic peptides to anti-tumor immune cells. The repertoires of peptides for various MHC Class I proteins are distinct, which can be reflected by their diverse binding motifs. To characterize binding motifs for MHC Class I proteins, in vitro experiments have been conducted to screen peptides with high binding affinities to hundreds of given MHC Class I proteins. However, considering tens of thousands of known MHC Class I proteins, conducting in vitro experiments for extensive MHC proteins is infeasible, and thus a more efficient and scalable way to characterize binding motifs is needed. RESULTS: We presented a de novo generation framework, coined PepPPO, to characterize binding motif for any given MHC Class I proteins via generating repertoires of peptides presented by them. PepPPO leverages a reinforcement learning agent with a mutation policy to mutate random input peptides into positive presented ones. Using PepPPO, we characterized binding motifs for around 10 000 known human MHC Class I proteins with and without experimental data. These computed motifs demonstrated high similarities with those derived from experimental data. In addition, we found that the motifs could be used for the rapid screening of neoantigens at a much lower time cost than previous deep-learning methods. AVAILABILITY AND IMPLEMENTATION: The software can be found in https://github.com/minrq/pMHC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Immune checkpoint gene VSIR predicts patient prognosis in acute myeloid leukemia and myelodysplastic syndromes.

BACKGROUND: Immune checkpoint proteins play critical functions during the immune response to cancer and have been targeted by immune checkpoint blockade therapy. V-domain Ig suppressor of T cell activation (VSIR) is one of these immune checkpoint genes and has been investigated extensively in recent years due to its conflicting roles in cancer immunity. Specifically, in acute myeloid leukemia (AML), the prognostic value of VSIR is debated. RESULTS: In both patient tumor samples and cancer cell lines we find that VSIR has the highest expression in AML out of all cancer types and, in AML, has the highest expression out of all other immune checkpoint genes. Survival analysis indicated that AML patients with higher VSIR expression have significantly shorter survival than those patients with lower expression, even within established AML subgroups (e.g., FAB subtypes). Importantly, VSIR expression is predictive of progression from myelodysplastic syndromes (MDS) patients into AML, suggesting its potential role during the very early stage of AML development and progression. In addition to AML, VSIR also demonstrates prognostic values in other cancer types, including multiple myeloma and mesothelioma. CONCLUSION: In summary, our analyses revealed the prognostic value of VSIR and its potential as a target for immunotherapy, especially in AML.

Resveratrol Ameliorates High Altitude Hypoxia-Induced Osteoporosis by Suppressing the ROS/HIF Signaling Pathway.

Hypoxia at high-altitude leads to osteoporosis. Resveratrol (RES), as an antioxidant, has been reported to promote osteoblastogenesis and suppress osteoclastogenesis. However, the therapeutic effect of RES against osteoporosis induced by high-altitude hypoxia remains unclear. Thus, this study was intended to investigate the potential effects of RES on high-altitude hypoxia-induced osteoporosis both in vivo and in vitro. Male Wistar rats were given RES (400 mg/kg) once daily for nine weeks under hypoxia, while the control was allowed to grow under normoxia. Bone mineral density (BMD), the levels of bone metabolism-related markers, and the changes on a histological level were measured. Bone marrow-derived mesenchymal stem cells (BMSCs) and RAW264.7 were incubated with RES under hypoxia, with a control growing under normoxia, followed by the evaluation of proliferation and differentiation. The results showed that RES inhibited high-altitude hypoxia-induced reduction in BMD, enhanced alkaline phosphatase (ALP), osteocalcin (OCN), calcitonin (CT) and runt-related transcription factor 2 (RUNX2) levels, whereas it reduced cross-linked carboxy-terminal telopeptide of type I collagen (CTX-I) levels and tartrate-resistant acid phosphatase (TRAP) activity in vivo. In addition, RES attenuated histological deteriorations in the femurs. In vitro, RES promoted osteoblastogenesis and mineralization in hypoxia-exposed BMSCs, along with promotion in RUNX2, ALP, OCN and osteopontin (OPN) levels, and inhibited the proliferation and osteoclastogenesis of RAW264.7. The promotion effects of RES on osteoblastogenesis were accompanied by the down-regulation of reactive oxygen species (ROS) and hypoxia inducible factor-1α (HIF-1α) induced by hypoxia. These results demonstrate that RES can alleviate high-altitude hypoxia-induced osteoporosis via promoting osteoblastogenesis by suppressing the ROS/HIF-1α signaling pathway. Thus, we suggest that RES might be a potential treatment with minimal side effects to protect against high-altitude hypoxia-induced osteoporosis.

Tumor cell intrinsic and extrinsic features predict prognosis in estrogen receptor positive breast cancer.

Although estrogen-receptor-positive (ER+) breast cancer is generally associated with favorable prognosis, clinical outcome varies substantially among patients. Genomic assays have been developed and applied to predict patient prognosis for personalized treatment. We hypothesize that the recurrence risk of ER+ breast cancer patients is determined by both genomic mutations intrinsic to tumor cells and extrinsic immunological features in the tumor microenvironment. Based on the Cancer Genome Atlas (TCGA) breast cancer data, we identified the 72 most common genomic aberrations (including gene mutations and indels) in ER+ breast cancer and defined sample-specific scores that systematically characterized the deregulated pathways intrinsic to tumor cells. To further consider tumor cell extrinsic features, we calculated immune infiltration scores for six major immune cell types. Many individual intrinsic features are predictive of patient prognosis in ER+ breast cancer, and some of them achieved comparable accuracy with the Oncotype DX assay. In addition, statistical learning models that integrated these features predicts the recurrence risk of patients with significantly better performance than the Oncotype DX assay (our optimized random forest model AUC = 0.841, Oncotype DX model AUC = 0.792, p = 0.04). As a proof-of-concept, our study indicates the great potential of genomic and immunological features in prognostic prediction for improving breast cancer precision medicine. The framework introduced in this work can be readily applied to other cancers.

A network-based integration for understanding racial disparity in prostate cancer.

Compared to Caucasians (CAs), African Americans (AAs) have a higher rate of incidence and mortality in prostate cancer and are prone to be diagnosed at later stages. To understand this racial disparity, molecular features of different types, including gene expression, DNA methylation and other genomic alterations, have been compared between tumor samples from the two races, but led to different disparity associated genes (DAGs). In this study, we applied a network-based algorithm to integrate a comprehensive set of genomic datasets and identified 130 core DAGs. Out of these genes, 78 were not identified by any individual dataset but prioritized and selected through network propagation. We found DAGs were highly enriched in several critical prostate cancer-related signaling transduction and cell cycle pathways and were more likely to be associated with patient prognosis in prostate cancer. Furthermore, DAGs were over-represented in prostate cancer risk genes identified from previous genome wide association studies. We also found DAGs were enriched in kinase and transcription factor encoding genes. Interestingly, for many of these prioritized kinases their association with racial disparity did not manifest from the original genomic/transcriptomic data but was reflected by their differential phosphorylation levels between AA and CA prostate tumor samples. Similarly, the disparity relevance of some transcription factors was not reflected at the mRNA or protein expression level, but at the activity level as demonstrated by their differential ability in regulating target gene expression. Our integrative analysis provided new candidate targets for improving prostate cancer treatment and addressing the racial disparity problem.

[Effects of three Polyphenolic compounds on the intestinal flora of mice exposed simulated intermittent plateau hypoxia].

OBJECTIVE: To investigate the protective effects of three Polyphenolic compounds on intestinal microbial communities in mice exposed intermittent plateau hypoxia. METHODS: In this study, 60 healthy male Balb/c mice were randomly divided into plain control group, plateau control group, primary anthocyanin intervention group, quercetin intervention group and resveratrol intervention group, 12 mice in each group. Primary anthocyanin, quercetin and resveratrol were administrated by gavage at the doses of 50, 100 and 20 mg/kg in pharmacological intervention group, respectively. After exposure of the mice to simulation plateau-condition for 30 days, the serum samples were collected for DAO testing, sterile feces were collected in mice, and the diversity and genus level of the mouse gut bacteria were detected by using 16S rRNA technology. Ileum tissue was fixed and stained with HE. RESULTS: HE staining showed that the plateau control group had significant damage to the intestinal tissue structure compared to the plain control group, and the serum DAO concentration was increased (P＜0.05), but there was no statistical difference in the abundance and diversity of intestinal flora species. Contrast to simulated intermittent plateau hypoxia group, the structure of the intestine tissue and the level of DAO in the quercetin intervention group and resveratrol intervention group were improved(P＜0.05), the abundance and α diversity of the intestinal flora were decreased, the relative abundance of Bacteroidetes was reduced(P＜0.05), and the Firmicutes was increased. Concomitantly, significant decreases in relative abundance were observed for Corynebacterium glutamicum and Lactobacillus reuteri(P＜ 0.05). CONCLUSION: Quercetin and resveratrol showed some degree of protection to mice intestinal microbial communities, and increased the diversity and the abundance of the dominant flora and inhibited the growth of conditional pathogenic bacteria.

Expression and significance of SOX B1 genes in glioblastoma multiforme patients.

The overall survival of glioblastoma multiforme (GBM) patients remains poor. To improve patient outcomes, effective diagnostic and prognostic biomarkers for GBM are needed. In this study, we first applied bioinformatic analyses to identify biomarkers for GBM, focusing on SOX (sex-determining region on the Y chromosome (SRY)-related high mobility group (HMG) box) B1 family members. The ONCOMINE, GEPIA, LinkedOmics and CCLE databases were used to assess mRNA expression levels of the SOX B1 family members in different cancers and normal tissue. Further bioinformatic analysis was performed using the ONCOMINE database in combination with the LinkedOmics data set to identify the prognostic value of SOX B1 family members for GBM. We found mRNA expression levels of all tested SOX B1 genes were significantly increased in GBM. In the LinkedOmics database, increased expression of SOX3 indicated a better overall survival. In GEPIA databases, increased expression of all SOX B1 family members suggested an improved overall survival, but none of them were statistically different. Then, Transwell assays and wound healing were employed to evaluate the motility and invasive captivity of U251 cells when silencing SOX2 and SOX3. We found exogenous inhibition of SOX2 appeared to reduce the migration and invasion of U251 cells in vitro. Collectively, our research suggested that SOX2 might serve as a cancer-promoting gene to identify high-risk GBM patients, and SOX3 had the potential to be a prognostic biomarker for GBM patients.

Deep Learning Predicts EBV Status in Gastric Cancer Based on Spatial Patterns of Lymphocyte Infiltration.

EBV infection occurs in around 10% of gastric cancer cases and represents a distinct subtype, characterized by a unique mutation profile, hypermethylation, and overexpression of PD-L1. Moreover, EBV positive gastric cancer tends to have higher immune infiltration and a better prognosis. EBV infection status in gastric cancer is most commonly determined using PCR and in situ hybridization, but such a method requires good nucleic acid preservation. Detection of EBV status with histopathology images may complement PCR and in situ hybridization as a first step of EBV infection assessment. Here, we developed a deep learning-based algorithm to directly predict EBV infection in gastric cancer from H&E stained histopathology slides. Our model can not only predict EBV infection in gastric cancers from tumor regions but also from normal regions with potential changes induced by adjacent EBV+ regions within each H&E slide. Furthermore, in cohorts with zero EBV abundances, a significant difference of immune infiltration between high and low EBV score samples was observed, consistent with the immune infiltration difference observed between EBV positive and negative samples. Therefore, we hypothesized that our model's prediction of EBV infection is partially driven by the spatial information of immune cell composition, which was supported by mostly positive local correlations between the EBV score and immune infiltration in both tumor and normal regions across all H&E slides. Finally, EBV scores calculated from our model were found to be significantly associated with prognosis. This framework can be readily applied to develop interpretable models for prediction of virus infection across cancers.

Impact of Oncotype DX testing on ER+ breast cancer treatment and survival in the first decade of use.

BACKGROUND: The Oncotype DX breast recurrence score has been introduced more than a decade ago to aid physicians in determining the need for systemic adjuvant chemotherapy in patients with early-stage, estrogen receptor (ER)+, lymph node-negative breast cancer. METHODS: In this study, we utilized data from The Surveillance, Epidemiology, and End Results (SEER) Program to investigate temporal trends in Oncotype DX usage among US breast cancer patients in the first decade after the introduction of the Oncotype DX assay. RESULTS: We found that the use of Oncotype DX has steadily increased in the first decade of use and that this increase is associated with a decreased usage of chemotherapy. Patients who utilized the Oncotype DX test tended to have improved survival compared to patients who did not use the assay even after adjusting for clinical variables associated with prognosis. In addition, chemotherapy usage in patients with high-risk scores is associated with significantly longer overall and breast cancer-specific survival compared to high-risk patients who did not receive chemotherapy. On the contrary, patients with low-risk scores who were treated with chemotherapy tended to have shorter overall survival compared to low-risk patients who forwent chemotherapy. CONCLUSION: We have provided a comprehensive temporal overview of the use of Oncotype DX in breast cancer patients in the first decade after Oncotype DX was introduced. Our results suggest that the use of Oncotype DX is increasing in ER+ breast cancer and that the Oncotype DX test results provide valuable information for patient treatment and prognosis.

Chr20q Amplification Defines a Distinct Molecular Subtype of Microsatellite Stable Colorectal Cancer.

Colorectal cancer is the third leading cause of cancer-related death in the United States. About 15% of colorectal cancers are associated with microsatellite instability (MSI) due to loss of function in the DNA mismatch repair pathway. This subgroup of patients has better survival rates and is more sensitive to immunotherapy. However, it remains unclear whether microsatellite stable (MSS) patients with colorectal cancer can be further stratified into subgroups with differential clinical characteristics. In this study, we analyzed The Cancer Genome Atlas data and found that Chr20q amplification is the most frequent copy number alteration that occurs specifically in colon (46%) and rectum (61%) cancer and is mutually exclusive with MSI. Importantly, MSS patients with Chr20q amplification (MSS-A) were associated with better recurrence-free survival compared with MSS patients without Chr20q amplification (MSS-N; P = 0.03). MSS-A tumors were associated with high level of chromosome instability and low immune infiltrations. In addition, MSS-A and MSS-N tumors were associated with somatic mutations in different driver genes, with high frequencies of mutated TP53 in MSS-A and mutated KRAS and BRAF in MSS-N. Our results suggest that MSS-A and MSS-N represent two subtypes of MSS colorectal cancer, and such stratification may be used to improve therapeutic treatment in an individualized manner. SIGNIFICANCE: This study shows that chromosome 20q amplification occurs predominately in microsatellite-stable colorectal cancer and defines a distinct subtype with good prognosis, high chromosomal instability, distinct mutation profiles, and low immune infiltrations.