Molecular subtype identification and prognosis stratification based on lysosome-related genes in breast cancer.

Background: Lysosomes are known to have a significant impact on the development and recurrence of breast cancer. However, the association between lysosome-related genes (LRGs) and breast cancer remains unclear. This study aims to explore the potential role of LRGs in predicting the prognosis and treatment response of breast cancer. Methods: Breast cancer gene expression profile data and clinical information were downloaded from TCGA and GEO databases, and prognosis-related LRGs were screened for consensus clustering analysis. Lasso Cox regression analysis was used to construct risk features derived from LRGs, and immune cell infiltration, immune therapy response, drug sensitivity, and clinical pathological feature differences were evaluated for different molecular subtypes and risk groups. A nomogram based on risk features derived from LRGs was constructed and evaluated. Results: Our study identified 176 differentially expressed LRGs that are associated with breast cancer prognosis. Based on these genes, we divided breast cancer into two molecular subtypes with significant prognostic differences. We also found significant differences in immune cell infiltration between these subtypes. Furthermore, we constructed a prognostic risk model consisting of 7 LRGs, which effectively divides breast cancer patients into high-risk and low-risk groups. Patients in the low-risk group have better prognostic characteristics, respond better to immunotherapy, and have lower sensitivity to chemotherapy drugs, indicating that the low-risk group is more likely to benefit from immunotherapy and chemotherapy. Additionally, the risk score based on LRGs is significantly correlated with immune cell infiltration, including CD8 T cells and macrophages. This risk score model, along with age, chemotherapy, clinical stage, and N stage, is an independent prognostic factor for breast cancer. Finally, the nomogram composed of these factors has excellent performance in predicting overall survival of breast cancer. Conclusions: In conclusion, this study has constructed a novel LRG-derived breast cancer risk feature, which performs well in prognostic prediction when combined with clinical pathological features.

Risk factors for the development of severe breast cancer-related lymphedema: a retrospective cohort study.

BACKGROUND: Severe lymphedema presents a challenge in terms of treatment due to the significant formation of scar tissue that accompanies it. The aim of this study was to identify intraoperative and preoperative risk factors of severe lymphedema and to develop a nomogram for estimating the risk of severe lymphedema within 3 years of surgery. METHOD: Data was collected from a retrospective cohort of 326 patients with BCRL at the Zhejiang Cancer Hospital from November 2015 to November 2018. Univariate and multivariate logistic regression analysis was conducted to identify predictive indicators of severe lymphedema. A nomogram was developed to further improve the clinical applicability. RESULTS: In the retrospective cohort, the ratio of severe/non-severe lymphedema within 3 years of surgery was 1:3. Independent risk factors for severe lymphedema were determined to be age, positive lymph nodes, interpectoral (Rotter's) lymph nodes (IPNs) dissection, and educational level. IPNs dissection was found to contribute greatly to the development of severe lymphedema with a higher odds ratio (7.76; 95% CI: 3.87-15.54) than other risk factors. A nomogram was developed by integrating age, positive lymph nodes, IPNs dissection, and educational level, which yielded a C-index of 0.810 and 0.681 in the training and validation cohort, respectively. This suggested a moderate performance of the nomogram in predicting the risk of severe lymphedema within 3 years of surgery. The cut-off values of the low-, medium- and high-risk probabilities were 0.0876 and 0.3498, and the severe lymphedema exhibited a significantly higher risk probability as compared with the non-severe lymphedema. CONCLUSION: This study identified the risk factors of severe lymphedema and highlighted the substantial contribution of IPNs dissection to the severity of lymphedema.

Accurate Diagnosis and Survival Prediction of Bladder Cancer Using Deep Learning on Histological Slides.

(1) Background: Early diagnosis and treatment are essential to reduce the mortality rate of bladder cancer (BLCA). We aimed to develop deep learning (DL)-based weakly supervised models for the diagnosis of BLCA and prediction of overall survival (OS) in muscle-invasive bladder cancer (MIBC) patients using whole slide digitized histological images (WSIs). (2) Methods: Diagnostic and prognostic models were developed using 926 WSIs of 412 BLCA patients from The Cancer Genome Atlas cohort. We collected 250 WSIs of 150 BLCA patients from the Renmin Hospital of Wuhan University cohort for external validation of the models. Two DL models were developed: a BLCA diagnostic model (named BlcaMIL) and an MIBC prognostic model (named MibcMLP). (3) Results: The BlcaMIL model identified BLCA with accuracy 0.987 in the external validation set, comparable to that of expert uropathologists and outperforming a junior pathologist. The C-index values for the MibcMLP model on the internal and external validation sets were 0.631 and 0.622, respectively. The risk score predicted by MibcMLP was a strong predictor independent of existing clinical or histopathologic indicators, as demonstrated by univariate Cox (HR = 2.390, p < 0.0001) and multivariate Cox (HR = 2.414, p < 0.0001) analyses. The interpretability of DL models can help in the analysis of critical regions associated with tumors to enrich the information obtained from WSIs. Furthermore, the expression of six genes (ANAPC7, MAPKAPK5, COX19, LINC01106, AL161431.1 and MYO16-AS1) was significantly associated with MibcMLP-predicted risk scores, revealing possible potential biological correlations. (4) Conclusions: Our study developed DL models for accurately diagnosing BLCA and predicting OS in MIBC patients, which will help promote the precise pathological diagnosis of BLCA and risk stratification of MIBC to improve clinical treatment decisions.

ThPOK inhibits the immune escape of gastric cancer cells by inducing STPG1 to inactivate the ERK pathway.

BACKGROUND: Gastric cancer is the second most frequently diagnosed cancer worldwide. Weak immunogenicity helps cancer cells escape from immune elimination and grow into predominant subpopulations. This study aimed to investigate the effect of Zinc finger and BTB domain containing 7B (Zbtb7b, Alias ThPOK) on T cell activation after coculture with gastric cancer cells. METHODS: Cell Counting Kit-8 assay (CCK-8) was performed to explore the viability of gastric cancer cells. Flow cytometry analysis was used to measure CD3+ T cell proliferation and the ratio of activated IFN-γ+ T cells which were co-incubated with gastric cancer cells (HGC-27, SNU-1). The binding between ThPOK and the promoter of its target sperm tail PG-rich repeat containing 1 (STPG1) was explored using ChIP and luciferase reporter assays. Relative gene expression was quantified using RT-qPCR. RESULTS: ThPOK was expressed at a low level in gastric cancer tissues and cells at mRNA and protein levels. Gastric cancer patients with lower ThPOK expression had poorer prognosis. ThPOK overexpression suppressed gastric cancer cell viability and increased T cell activation. ThPOK served as a transcription factor for STPG1. STPG1 expression was also at a low level in the tissues and cells of gastric cancer. ThPOK positively regulated the mRNA and protein levels of STPG1 in gastric cancer cells. Moreover, ThPOK was demonstrated to bind with STPG1 promoter. STPG1 upregulation also exerted inhibitory effects on gastric cancer cell viability and T cell activation. Additionally, ThPOK and STPG1 were revealed to inactivate the ERK pathway in gastric cancer cells. CONCLUSION: ThPOK inhibits gastric cancer cell viability and increases T cell activation by inducing STPG1 to inactivate the ERK pathway.

ThPOK transcriptionally inactivates TNFRSF12A to increase the proliferation of T cells with the involvement of the NF-kB pathway.

Gastric cancer (GC), originated from gastric mucosa, is a malignant tumor causing numerous deaths globally. The present study used the coculture of T cells with supernatant of the GC cells (HGC-27, SNU-1) and investigated the function and regulatory mechanism of Zinc finger and BTB domain containing 7B (ZBTB7B, alias ThPOK) on T cell proliferation. Flow cytometry analysis was used to measure the proliferation of CD3+ T cells and IFN-γ+ T cells. We found that low level of ThPOK was associated with poor prognosis in GC patients. ThPOK was lowly expressed in GC cells at the mRNA and protein levels. ThPOK overexpression inhibited GC cell viability and promoted proliferation of T cells. ThPOK was identified to function as a transcription factor for TNFRSF12A. TNFRSF12A was upregulated in GC tissues and cells and high level of TNFRSF12A was associated with poor prognosis in GC patients. ThPOK knockdown elevated TNFRSF12A level in GC cells. ThPOK was revealed to bind with the promoter of TNFRSF12A. TNFRSF12A silencing also inhibited GC cell viability and promoted T cell activation and proliferation. Additionally, ThPOK was demonstrated to inactivate the NF-kB pathway by downregulating TNFRSF12A in GC cells. Overall, ThPOK suppresses cell viability in GC and increases the activation and proliferation of T cells by targeting TNFRSF12A to inactivate the NF-kB pathway.

A near-threshold, 0.16 nJ/b OOK-transmitter with 0.18 nJ/b noise-cancelling super-regenerative receiver for the medical implant communications service.

A 0.16 nJ/b MICS transmitter and 0.18 nJ/b super-regenerative receiver are demonstrated, where each is specifically designed to operate in the near-threshold region. The low-VDD transmitter utilizes a sub-harmonic injection-locked ring oscillator, edge combiner for frequency multiplication, and class-C power amplifier. The low-VDD receiver introduces a replica super-regenerative receiver as a method to reject common-mode noise sources, such as supply/substrate coupling, thereby reducing undesired self-oscillations and improving BER. Designed in a 90-nm CMOS process, the test-chip measurements show a sensitivity of -80 dBm at 500 kb/s and -65 dBm at 1 Mb/s, respectively, at a BER less than 10⁻³, with 340 µW total power.