Alan Huang

Prostate cancer (PCa) is unique in its tendency to produce osteoblastic (OB) bone metastases. There are no existing therapies that specifically target the OB phase that affects 90% of men with bone metastatic disease. Prostatic acid phosphatase (PAP) is secreted by PCa cells in OB metastases and increases OB growth, differentiation, and bone mineralization. The purpose of this study was to investigate whether PAP effects on OB bone metastases are mediated by autocrine and/or paracrine alterations in the receptor activator of nuclear factor κ-B (RANK)/RANK ligand (RANKL)/osteoprotegerin (OPG) system. To investigate whether PAP modulated these factors and altered the bone reaction, we knocked down PAP expression in VCaP cells and stably overexpressed PAP in PC3M cells, both derived from human PCa bone metastases. We show that knockdown of PAP in VCaP cells decreased OPG while increasing RANK/RANKL expression. Forced overexpression of PAP in PC3M cells had the inverse effect, increasing OPG while decreasing RANK/RANKL expression. Coculture of PCa cells with MC3T3 preosteoblasts also revealed a role for secretory PAP in OB-PCa cross talk. Reduced PAP expression in VCaP cells decreased MC3T3 proliferation and differentiation and reduced their OPG expression. PAP overexpression in PC3M cells altered the bone phenotype creating OB rather than osteolytic lesions in vivo using an intratibial model. These findings demonstrate that PAP secreted by PCa cells in OB bone metastases increases OPG and plays a critical role in the vicious cross talk between cancer and bone cells. These data suggest that inhibition of secretory PAP may be an effective strategy for PCa OB bone lesions.

Cytotoxic T cells expressing cell surface CD8 played a key role in anti-cancer immunotherapy, including kidney renal clear cell carcinoma (KIRC). Here we set out to comprehensively analyze and evaluate the significance of CD8+ T cell-related markers for patients with KIRC. We checked immune cell response in KIRC and identified cell type-specific markers and related pathways in the tumor-infiltrating CD8+ T (TIL-CD8T) cells. We used these markers to explore their prognostic signatures in TIL-CD8+ T by evaluating their prognostic efficacy and group differences at various levels. Through pan-cancer analysis, 12 of 63 up-regulated and 162 of 396 down-regulated genes in CD8+ T cells were found to be significantly correlated with the survival prognosis. Based on our highly integrated multi-platform analyses across multiple datasets, we constructed a 6-gene risk scoring model specific to TIL-CD8T. In this model, high TIL-CD8 sig score was corresponding to a higher incidence frequency of copy number variation and drug sensitivity to sorafenib. Moreover, the prognosis of patients with the same or similar immune checkpoint gene levels could be distinguished from each other by TIL-CD8 sig score.

Fully up to date with recent research and practice, including the most recent AHA guidelines, this model resource for the practising emergency medicine resident allows for a case-based interactive approach to studying for the Oral Boards examination, while also providing an excellent introduction to the field. Featuring 126 cases derived from the Model of Clinical Practice of Emergency Medicine, with an emphasis on EKGs, CT scans, X-rays and ultrasounds, it now includes diagnoses such as nursemaid's elbow, multiple sepsis cases, the suicidal patient, and Cushing's syndrome, as well as a chapter on the scoring of Oral Boards. Practising alone or with a partner, the reader can review critical actions and key clinical pearls for each case. The appendices contain high-yield information on subjects emphasised in the Oral Boards examination, such as pediatric, cardiovascular, traumatic, and toxicological disorders. This book truly allows the reader to feel actively immersed in the case.

The biological functional network of tumor tissues is relatively stable for a period of time and under different conditions, so the impact of tumor heterogeneity is effectively avoided. Based on edge perturbation, functional gene interaction networks were used to reveal the pathological environment of patients with non-small cell carcinoma at the individual level, and to identify cancer subtypes with the same or similar status, and then a multi-dimensional and multi-omics comprehensive analysis was put into practice. Two edge perturbation subtypes were identified through the construction of the background interaction network and the edge-perturbation matrix (EPM). Further analyses revealed clear differences between those two clusters in terms of prognostic survival, stemness indices, immune cell infiltration, immune checkpoint molecular expression, copy number alterations, mutation load, homologous recombination defects (HRD), neoantigen load, and chromosomal instability. Additionally, a risk prediction model based on TCGA for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) was successfully constructed and validated using the independent data set (GSE50081).

Purpose: This study was designed to clarify the prognostic value of tumor microenvironment score and abnormal genomic alterations in TME for breast cancer patients. Method: The TCGA-BRCA data were downloaded from TCGA and analyzed with R software. The results from analyses were further validated using the dataset from GSE96058, GSE124647, and GSE25066. Results: After analyzing the TCGA data and verifying it with the GEO data, we developed a TMEscore model based on the TME infiltration pattern and validated it in 3273 breast cancer patients. The results suggested that our TMEscore model has high prognostic value. TME features with the TMEscore model can help to predict breast cancer patients' response to immunotherapy and provide new strategies for breast cancer treatment. Signature 24 was first found in breast cancer. In focal SCNAs, a total of 95 amplified genes and 169 deletion genes in the TMEscore high group were found to be significantly related to the prognosis of breast cancer patients, while 61 amplified genes and 174 deletion genes in the TMEscore low group were identified. LRRC48, CFAP69, and cg25726128 were first discovered and reported to be related to the survival of breast cancer patients. We identified specific mutation signatures that correlate with TMEscore and prognosis. Conclusion: TMEscore model has high predictive value regarding prognosis and patients' response to immunotherapy. Signature 24 was first found in breast cancer. Specific mutation signatures that correlate with TMEscore and prognosis might be used for providing additional indicators for disease evaluation.