Mutation impact on mRNA versus protein expression across human cancers.

Background: Cancer mutations are often assumed to alter proteins, thus promoting tumorigenesis. However, how mutations affect protein expression-in addition to gene expression-has rarely been systematically investigated. This is significant as mRNA and protein levels frequently show only moderate correlation, driven by factors such as translation efficiency and protein degradation.

Master regulators governing protein abundance across ten human cancer types.

Protein abundance correlates only moderately with mRNA levels, and are modulated post-transcriptionally by a network of regulators including ribosomes, RNA-binding proteins (RBPs), and the proteasome. Here, we identified ster rotein abundance egulators (MaPRs) across ten cancer types by devising a new computational pipeline that jointly analyzed transcriptomes and proteomes from 1,305 tumor samples. We identified 232 to 1,394 MaPRs per cancer type, mediating up to 79% of post-transcriptional regulatory networks.

Expression-Driven Genetic Dependency Reveals Targets for Precision Medicine.

Cancer cells are heterogeneous, each harboring distinct molecular aberrations and are dependent on different genes for their survival and proliferation. While successful targeted therapies have been developed based on driver DNA mutations, many patient tumors lack druggable mutations and have limited treatment options. Here, we hypothesize that new precision oncology targets may be identified through "expression-driven dependency", whereby cancer cells with high expression of a targeted gene are more vulnerable to the knockout of that gene.

Deletions Rate-Limit Breast and Ovarian Cancer Initiation.

Optimizing prevention and early detection of cancer requires understanding the number, types and timing of driver mutations. To quantify this, we exploited the elevated cancer incidence and mutation rates in germline and carriers. Using novel statistical models, we identify genomic deletions as the likely rate-limiting mutational processes, with 1-3 deletions required to initiate breast and ovarian tumors.

Associations of Circulating Biomarkers with Disease Risks: a Two-Sample Mendelian Randomization Study.

Background: Circulating biomarkers play a pivotal role in personalized medicine, offering potential for disease screening, prevention, and treatment. Despite established associations between numerous biomarkers and diseases, elucidating their causal relationships is challenging. Mendelian Randomization (MR) can address this issue by employing genetic instruments to discern causal links.

Associations of Circulating Biomarkers with Disease Risks: A Two-Sample Mendelian Randomization Study.

Circulating biomarkers play a pivotal role in personalized medicine, offering potential for disease screening, prevention, and treatment. Despite established associations between numerous biomarkers and diseases, elucidating their causal relationships is challenging. Mendelian Randomization (MR) can address this issue by employing genetic instruments to discern causal links.

Get Free Copy: a multi-repository search platform for biomedical publications.

We introduce Get Free Copy (https://getfreecopy.com), a web-based platform designed to streamline the search for biomedical literature across major repositories like arXiv, bioRxiv, medRxiv, and PubMed Central (PMC). Addressing challenges posed by paywalls and fragmented databases, it offers a unified interface for efficient retrieval of free, legitimate copies of biomedical literature.

Machine learning models identify predictive features of patient mortality across dementia types.

Background: Dementia care is challenging due to the divergent trajectories in disease progression and outcomes. Predictive models are needed to flag patients at risk of near-term mortality and identify factors contributing to mortality risk across different dementia types.

Methods: Here, we developed machine-learning models predicting dementia patient mortality at four different survival thresholds using a dataset of 45,275 unique participants and 163,782 visit records from the U.

Multiscale protein networks systematically identify aberrant protein interactions and oncogenic regulators in seven cancer types.

Global proteomic data generated by advanced mass spectrometry (MS) technologies can help bridge the gap between genome/transcriptome and functions and hold great potential in elucidating unbiased functional models of pro-tumorigenic pathways. To this end, we collected the high-throughput, whole-genome MS data and conducted integrative proteomic network analyses of 687 cases across 7 cancer types including breast carcinoma (115 tumor samples; 10,438 genes), clear cell renal carcinoma (100 tumor samples; 9,910 genes), colorectal cancer (91 tumor samples; 7,362 genes), hepatocellular carcinoma (101 tumor samples; 6,478 genes), lung adenocarcinoma (104 tumor samples; 10,967 genes), stomach adenocarcinoma (80 tumor samples; 9,268 genes), and uterine corpus endometrial carcinoma UCEC (96 tumor samples; 10,768 genes). Through the protein co-expression network analysis, we identified co-expressed protein modules enriched for differentially expressed proteins in tumor as disease-associated pathways.

Mutation Impact on mRNA Versus Protein Expression across Human Cancers.

Cancer mutations are often assumed to alter proteins, thus promoting tumorigenesis. However, how mutations affect protein expression has rarely been systematically investigated. We conduct a comprehensive analysis of mutation impacts on mRNA- and protein-level expressions of 953 cancer cases with paired genomics and global proteomic profiling across six cancer types.

Calsarcin-2 May Play a Compensatory Role in the Development of Obese Sarcopenia.

Although obese sarcopenia is a major public health problem with increasing prevalence worldwide, the factors that contribute to the development of obese sarcopenia are still obscure. In order to clarify this issue, a high-fat-diet-induced obese sarcopenia mouse model was utilized. After being fed with a high-fat diet for 24 weeks, decreased motor functions and muscle mass ratios were found in the C57BL/6 mice.

Tumor-intrinsic LKB1-LIF signaling axis establishes a myeloid niche to promote immune evasion and tumor growth.

Tumor mutations can influence the surrounding microenvironment leading to suppression of anti-tumor immune responses and thereby contributing to tumor progression and failure of cancer therapies. Here we use genetically engineered lung cancer mouse models and patient samples to dissect how mutations accelerate tumor growth by reshaping the immune microenvironment. Comprehensive immune profiling of -mutant vs wildtype tumors revealed dramatic changes in myeloid cells, specifically enrichment of Arg1 interstitial macrophages and SiglecF neutrophils.

Predictive Models and Features of Patient Mortality across Dementia Types.

Dementia care is challenging due to the divergent trajectories in disease progression and outcomes. Predictive models are needed to identify patients at risk of near-term mortality. Here, we developed machine learning models predicting survival using a dataset of 45,275 unique participants and 163,782 visit records from the U.

Using EGFR amplification to stratify recurrent glioblastoma treated with immune checkpoint inhibitors.

Purpose: While immune checkpoint inhibitors (ICI) have had success with various malignancies, their efficacy in brain cancer is still unclear. Retrospective and prospective studies using PD-1 inhibitors for recurrent glioblastoma (GBM) have not established survival benefit. This study evaluated if ICI may be effective for select patients with recurrent GBM.

Multiethnic Investigation of Risk and Immune Determinants of COVID-19 Outcomes.

Background: Disparate COVID-19 outcomes have been observed between Hispanic, non-Hispanic Black, and White patients. The underlying causes for these disparities are not fully understood.

Methods: This was a retrospective study utilizing electronic medical record data from five hospitals within a single academic health system based in New York City.

Analysis of sex-specific risk factors and clinical outcomes in COVID-19.

Background: Sex has consistently been shown to affect COVID-19 mortality, but it remains unclear how each sex's clinical outcome may be distinctively shaped by risk factors.

Methods: We studied a primary cohort of 4930 patients hospitalized with COVID-19 in a single healthcare system in New York City from the start of the pandemic till August 5, 2020, and a validation cohort of 1645 patients hospitalized with COVID-19 in the same healthcare system from August 5, 2020, to January 13, 2021.

Results: Here we show that male sex was independently associated with in-hospital mortality, intubation, and ICU care after adjusting for demographics and comorbidities.

Proteomic Analyses Identify Therapeutic Targets in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the fourth cause of cancer-related mortality worldwide. While many targeted therapies have been developed, the majority of HCC tumors do not harbor clinically actionable mutations. Protein-level aberrations, especially those not evident at the genomic level, present therapeutic opportunities but have rarely been systematically characterized in HCC.

Multi-ethnic Investigation of Risk and Immune Determinants of COVID-19 Outcomes.

Disparate COVID-19 outcomes have been observed between Hispanic, Non-Hispanic Black, and White patients. The underlying causes for these disparities are not fully understood. This was a retrospective study utilizing electronic medical record data from five hospitals within a single academic health system based in New York City.

Modeling COVID-19 dynamic using a two-strain model with vaccination.

Multiple strains of the SARS-CoV-2 have arisen and jointly influence the trajectory of the coronavirus disease (COVID-19) pandemic. However, current models rarely account for this multi-strain dynamics and their different transmission rate and response to vaccines. We propose a new mathematical model that accounts for two virus variants and the deployment of a vaccination program.

Pan-cancer proteogenomic investigations identify post-transcriptional kinase targets.

Identifying genomic alterations of cancer proteins has guided the development of targeted therapies, but proteomic analyses are required to validate and reveal new treatment opportunities. Herein, we develop a new algorithm, OPPTI, to discover overexpressed kinase proteins across 10 cancer types using global mass spectrometry proteomics data of 1,071 cases. OPPTI outperforms existing methods by leveraging multiple co-expressed markers to identify targets overexpressed in a subset of tumors.