DegronMD: Leveraging Evolutionary and Structural Features for Deciphering Protein-Targeted Degradation, Mutations, and Drug Response to Degrons.

Protein-targeted degradation is an emerging and promising therapeutic approach. The specificity of degradation and the maintenance of cellular homeostasis are determined by the interactions between E3 ubiquitin ligase and degradation signals, known as degrons. The human genome encodes over 600 E3 ligases; however, only a small number of targeted degron instances have been identified so far. In this study, we introduced DegronMD, an open knowledgebase designed for the investigation of degrons, their associated dysfunctional events, and drug responses. We revealed that degrons are evolutionarily conserved and tend to occur near the sites of protein translational modifications, particularly in the regions of disordered structure and higher solvent accessibility. Through pattern recognition and machine learning techniques, we constructed the degrome landscape across the human proteome, yielding over 18,000 new degrons for targeted protein degradation. Furthermore, dysfunction of degrons disrupts the degradation process and leads to the abnormal accumulation of proteins; this process is associated with various types of human cancers. Based on the estimated phenotypic changes induced by somatic mutations, we systematically quantified and assessed the impact of mutations on degron function in pan-cancers; these results helped to build a global mutational map on human degrome, including 89,318 actionable mutations that may induce the dysfunction of degrons and disrupt protein degradation pathways. Multiomics integrative analysis unveiled over 400 drug resistance events associated with the mutations in functional degrons. DegronMD, accessible at https://bioinfo.uth.edu/degronmd, is a useful resource to explore the biological mechanisms, infer protein degradation, and assist with drug discovery and design on degrons.

A Plasmodium falciparum RING Finger E3 Ubiquitin Ligase Modifies the Roles of PfMDR1 and PfCRT in Parasite Drug Responses.

Protein ubiquitination is an important posttranslational regulation mechanism that mediates Plasmodium development and modifies parasite responses to antimalarial drugs. Although mutations in several parasite ubiquitination enzymes have been linked to increased drug tolerance, the molecular mechanisms by which ubiquitination pathways mediate these parasite responses remain largely unknown. Here, we investigate the roles of a Plasmodium falciparum ring finger ubiquitin ligase (PfRFUL) in parasite development and in responses to antimalarial drugs. We engineered a transgenic parasite having the Pfrful gene tagged with an HA-2A-NeoR-glmS sequence to knockdown (KD) Pfrful expression using glucosamine (GlcN). A Western blot analysis of the proteins from GlcN-treated pSLI-HA-NeoR-glmS-tagged (PfRFULg) parasites, relative to their wild-type (Dd2) controls, showed changes in the ubiquitination of numerous proteins. PfRFUL KD rendered the parasites more sensitive to multiple antimalarial drugs, including mefloquine, piperaquine, amodiaquine, and dihydroartemisinin. PfRFUL KD also decreased the protein level of the P. falciparum multiple drug resistance 1 protein (PfMDR1) and altered the ratio of two bands of the P. falciparum chloroquine resistance transporter (PfCRT), suggesting contributions to the changed drug responses by the altered ubiquitination of these two molecules. The inhibition of proteasomal protein degradation by epoxomicin increased the PfRFUL level, suggesting the degradation of PfRFUL by the proteasome pathways, whereas the inhibition of E3 ubiquitin ligase activities by JNJ26854165 reduced the PfRFUL level. This study reveals the potential mechanisms of PfRFUL in modifying the expression of drug transporters and their roles in parasite drug responses. PfRFUL could be a potential target for antimalarial drug development.

Establishment of a Seven-Gene Signature Associated with CD8+ T Cells through the Utilization of Both Single-Cell and Bulk RNA-Sequencing Techniques in Clear Cell Renal Cell Carcinoma.

Tumor immune microenvironment constituents, such as CD8+ T cells, have emerged as crucial focal points for cancer immunotherapy. Given the absence of reliable biomarkers for clear cell renal cell carcinoma (ccRCC), we aimed to ascertain a molecular signature that could potentially be linked to CD8+ T cells. The differentially expressed genes (DEGs) linked to CD8+ T cells were identified through an analysis of single-cell RNA sequencing (scRNA-seq) data obtained from the Gene Expression Omnibus (GEO) database. Subsequently, immune-associated genes were obtained from the InnateDB and ImmPort datasets and were cross-referenced with CD8+ T-cell-associated DEGs to generate a series of DEGs linked to immune response and CD8+ T cells. Patients with ccRCC from the Cancer Genome Atlas (TCGA) were randomly allocated into testing and training groups. A gene signature was established by conducting LASSO-Cox analysis and subsequently confirmed using both the testing and complete groups. The efficacy of this signature in evaluating immunotherapy response was assessed on the IMvigor210 cohort. Finally, we employed various techniques, including CIBERSORT, ESTIMATE, ssGSEA, and qRT-PCR, to examine the immunological characteristics, drug responses, and expression of the signature genes in ccRCC. Our findings revealed 206 DEGs linked to immune response and CD8+ T cells, among which 65 genes were correlated with overall survival (OS) in ccRCC. A risk assessment was created utilizing a set of seven genes: RARRES2, SOCS3, TNFSF14, XCL1, GRN, CLDN4, and RBP7. The group with a lower risk showed increased expression of CD274 (PD-L1), suggesting a more favorable response to anti-PD-L1 treatment. The seven-gene signature demonstrated accurate prognostic prediction for ccRCC and holds potential as a clinical reference for treatment decisions.

Confocal Raman Spectral Imaging Study of DAPT, a γ-secretase Inhibitor, Induced Physiological and Biochemical Reponses in Osteosarcoma Cells.

Confocal Raman microspectral imaging was adopted to elucidate the cellular drug responses of osteosarcoma cells (OC) to N-[N-(3, 5-difluorophenyl acetyl)-L-alanyl]-sphenylglycine butyl ester (DAPT), a γ-secretase inhibitor, by identifying the drug induced subcellular compositional and structural changes. Methods: Spectral information were acquired from cultured osteosarcoma cells treated with 0 (Untreated Group, UT), 10 (10 µM DAPT treated, 10T), 20 µM (20 µM DAPT treated, 20T) DAPT for 24 hours. A one-way ANOVA and Tukey's honest significant difference (HSD) post hoc multiple test were sequentially applied to address spectral features among three groups. Multivariate algorithms such as K-means clustering analysis (KCA) and Principal component analysis (PCA) were used to highlight the structural and compositional differences, while, univariate imaging was applied to illustrate the distribution pattern of certain cellular components after drug treatment. Results: Major biochemical changes in DAPT-induced apoptosis came from changes in the content and structure of proteins, lipids, and nucleic acids. By adopted multivariate algorithms, the drug induced cellular changes was identified by the morphology and spectral characteristics between untreated cells and treated cells, testified that DAPT mainly acted in the nuclear region. With the increase of the drug concentration, the content of main subcellular compositions, such nucleic acid, protein, and lipid decreased. In an addition, DAPT-induced nuclear fragmentation and apoptosis was depicted by the univariate Raman image of major cellular components (nucleic acids, proteins and lipids). Conclusions: The achieved Raman spectral and imaging results illustrated detailed DAPT-induced subcellular compositional and structural variations as a function of drug dose. Such observations can not only explain drug therapeutic mechanisms of OC DAPT treatment, and also provide new insights for accessing the medicine curative efficacy and predicting prognosis.

In vivo cytochrome P450 activity alterations in diabetic nonalcoholic steatohepatitis mice.

Nonalcoholic steatohepatitis (NASH) has been identified as a source of significant interindividual variation in drug metabolism. A previous ex vivo study demonstrated significant changes in hepatic Cytochrome P450 (CYP) activity in human NASH. This study evaluated the in vivo activities of multiple CYP isoforms simultaneously in prominent diabetic NASH mouse models. The pharmacokinetics of CYP selective substrates: caffeine, losartan, and omeprazole changed significantly in a diabetic NASH mouse model, indicating attenuation of the activity of Cyp1a2 and Cyp2c29, respectively. Decreased mRNA expression of Cyp1a2 and Cyp2c29, as well as an overall decrease in CYP protein expression, was found in the diabetic NASH mice. Overall, these data suggest that the diabetic NASH model only partially recapitulates the human ex vivo CYP alteration pattern. Therefore, in vivo determination of the effects of NASH on CYP activity should be conducted in human, and more appropriate models are required for future drug metabolism studies in NASH.

SILAC Mass Spectrometry Profiling: A Psychiatric Disorder Perspective.

Stable isotope labelling by amino acids in cell culture (SILAC) is a technique that allows proteomic profiling of cells. In this chapter we describe a protocol for the identification and quantification of newly synthesised proteins. The methodology can be applied to any cultured cell system with relevance to schizophrenia, affective disorders and autism spectrum conditions including those addressing responses to pharmacological stimuli.

A Propensity Scoring Approach to Characterizing the Effects of Maternal Smoking During Pregnancy on Offspring's Initial Responses to Cigarettes and Alcohol.

When examining the effects of prenatal exposure to maternal smoking during pregnancy (MSDP) on later offspring substance use, it is critical to consider familial environments confounded with MSDP. The purpose of this study was to examine the effect of MSDP on offspring's initial reactions to cigarettes and alcohol, which are indicators of future substance-use related problems. We tested these effects using two propensity score approaches (1) by controlling for confounding using the MSDP propensity score and (2) examining effects of MSDP across the MSDP risk distribution by grouping individuals into quantiles based on their MSDP propensity score. This study used data from 829 unrelated mothers with a reported lifetime history of smoking to determine the propensity for smoking only during their first trimester (MSDP-E) or throughout their entire pregnancy (MSDP-T). Propensity score analyses focused on the offspring (N = 1616 female twins) of a large subset of these mothers. We examined the effects of levels of MSDP-E/T on offspring initial reactions to their first experiences with alcohol and cigarettes, across the distribution of liability for MSDP-E/T. MSDP-E/T emerged as significant predictors of offspring reactions to alcohol and cigarettes, but the effects were confounded by the familial liability for MSDP. Further, the unique MSDP effects that emerged were not uniform across the MSDP familial risk distribution. Our findings underscore the importance of properly accounting for correlated familial risk factors when examining the effects of MSDP on substance related outcomes.

scDrug+: predicting drug-responses using single-cell transcriptomics and molecular structure.

Predicting drug responses based on individual transcriptomic profiles holds promise for refining prognosis and advancing precision medicine. Although many studies have endeavored to predict the responses of known drugs to novel transcriptomic profiles, research into predicting responses for newly discovered drugs remains sparse. In this study, we introduce scDrug+, a comprehensive pipeline that seamlessly integrates single-cell analysis with drug-response prediction. Importantly, scDrug+ is equipped to predict the response of new drugs by analyzing their molecular structures. The open-source tool is available as a Docker container, ensuring ease of deployment and reproducibility. It can be accessed at https://github.com/ailabstw/scDrugplus.

Approaches for identifying and measuring heteroresistance in azole-susceptible Candida isolates.

Heteroresistance to antifungal agents poses a significant challenge in the treatment of fungal infections. Currently, the absence of established methods for detecting and measuring heteroresistance impedes progress in understanding this phenomenon in fungal pathogens. In response to this gap, we present a comprehensive set of new and optimized methods designed to detect and quantify azole heteroresistance in Candida albicans. Here, we define two primary assays for measuring heteroresistance: population analysis profiling, based on growth on solid medium, and single-cell assays, based on growth in liquid culture. We observe good correlations between the measurements obtained with liquid and solid assays, validating their utility for studying azole heteroresistance. We also highlight that disk diffusion assays could serve as an additional tool for the rapid detection of heteroresistance. These methods collectively provide a versatile toolkit for researchers seeking to assess heteroresistance in C. albicans. They also serve as a critical step forward in the characterization of antifungal heteroresistance, providing a framework for investigating this phenomenon in diverse fungal species and in the context of other antifungal agents. Ultimately, these advancements will enhance our ability to effectively measure antifungal drug responses and combat fungal infections.IMPORTANCEHeteroresistance involves varying antimicrobial susceptibility within a clonal population. This phenomenon allows the survival of rare resistant subpopulations during drug treatment, significantly complicating the effective management of infections. However, the absence of established detection methods hampers progress in understanding this phenomenon in human fungal pathogens. We propose a comprehensive toolkit to address this gap in the yeast Candida albicans, encompassing population analysis profiling, single-cell assays, and disk diffusion assays. By providing robust and correlated measurements through both solid and liquid assays, this work will provide a framework for broader applications across clinically relevant Candida species. These methods will enhance our ability to understand this phenomenon and the failure of antifungal therapy.

Male breast cancer differs from female breast cancer in molecular features that affect prognoses and drug responses.

BACKGROUND: Male breast cancer (MBC) is a rare malignancy with a worse prognosis than female breast cancer (FBC). Current MBC treatment strategies are based on those for FBC. However, molecular differences between MBC and FBC with respect to prognosis and drug responses remain unclear. METHODS: After controlling for confounding factors with propensity score matching (PSM), differences between MBC and FBC were comprehensively analyzed using many types of data: survival, immune microenvironments, sex hormone responses, drug sensitivity, transcriptomes, genomes, epigenomes, and proteomes. RESULTS: Overall survival (OS) and cancer-specific survival (CSS) were both worse for MBC than for FBC. Differentially expressed mRNAs were enriched in numerous cancer-related functions and pathways, with SPAG16 and STOX1 being as the most important prognosis-related mRNAs for MBC. Competing endogenous RNA (ceRNA) and transcription factor (TF)-mRNA regulatory networks contain potential prognostic genes. Nine genes had higher mutation frequencies in MBC than in FBC. MBC shows a comparatively poor response to immunotherapy, with five proteins that promote breast cancer progression being highly expressed in MBC. MBC may be more responsive than FBC to estrogen. We detected six United States Food and Drug Administration (FDA)-approved therapeutic target genes as being differentially expressed between MBC and FBC. CONCLUSION: The poor prognosis of MBC compared to FBC is due to numerous molecular differences and resulting drug responses.

Phenotypic plasticity promotes lymph nodes metastasis and drug resistance in lung squamous cell carcinomas.

Background: Phenotypic plasticity (PP) is a major promoter of tumor metastasis and drug resistance. Nevertheless, the molecular features and clinical significance of phenotypic plasticity in lung squamous cell carcinomas (LSCC) remained largely unexplored. Methods: Phenotypic plasticity-related genes (PPRG) and clinical information of LSCC were downloaded from the cancer genome atlas (TCGA). The expression profiles of PPRG were compared between patients with and without lymph node metastasis. The prognostic signature was constructed, and survival analysis was performed based on phenotypic plasticity. Immunotherapy responses, chemotherapeutic drugs and targeted drug responses were investigated. In addition, the results were verified in an external cohort. Results: Patients with and without lymph node metastasis exhibited significantly different genomic characteristics of phenotypic plasticity. Enrichment analysis showed that PP was strongly associated with cell responses and cell contraction. Survival analysis demonstrated that PPRG could serve as independent prognostic factor for overall survival. The phenotypic plasticity-related signature successfully divided patients into high- and low-PP score groups. Patients with low-PP scores were more sensitive to PD-L1, Cisplatin, Gefitinib, Obatoclax. Mesylate, Paclitaxel, Sorafenib and Vinorelbine (all p < 0.05). While, patients with low-PP scores were more sensitive to Axitinib and Camptothecin (all p < 0.05). Consistent with the results from TCGA, the external cohort validated the above findings. Conclusions: Our study revealed that phenotypic plasticity may be involved in the lymph node metastasis in LSCC through regulating cell responses and cell contraction. Evaluation of phenotypic plasticity will assist clinicians in making treatment strategies.

Protocols for Measuring Tolerant and Heteroresistant Drug Responses of Pathogenic Yeasts.

The classic definition of antimicrobial susceptibility to antifungal drugs ignores the persistence of subpopulations that survive in the presence of a drug. Even in entirely clonal populations, small subpopulations of yeast can grow in the presence of a drug, sometimes up to extremely high drug concentrations, such that they may be clinically relevant. Identifying and quantifying the incidence with which these subpopulations arise is an essential step in understanding how pathogenic yeast, such as Candida species (i.e., C. albicans, C. glabrata, C. auris, C. tropicalis, C. parapsilosis, and others) as well as Cryptococcus species, behave in response to antifungal therapeutics. Here we describe simple in vitro protocols for the quantification of drug responses with subpopulation resolution.

Dynamically Crosslinked Poly(ethylene-glycol) Hydrogels Reveal a Critical Role of Viscoelasticity in Modulating Glioblastoma Fates and Drug Responses in 3D.

Glioblastoma multiforme (GBM) is the most prevalent and aggressive brain tumor in adults. Hydrogels have been employed as 3D in vitro culture models to elucidate how matrix cues such as stiffness and degradation drive GBM progression and drug responses. Recently, viscoelasticity has been identified as an important niche cue in regulating stem cell differentiation and morphogenesis in 3D. Brain is a viscoelastic tissue, yet how viscoelasticity modulates GBM fate and drug response remains largely unknown. Using dynamic hydrazone crosslinking chemistry, a poly(ethylene-glycol)-based hydrogel system with brain-mimicking stiffness and tunable stress relaxation is reported to interrogate the role of viscoelasticity on GBM fates in 3D. The hydrogel design allows tuning stress relaxation without changing stiffness, biochemical ligand density, or diffusion. The results reveal that increasing stress relaxation promotes invasive GBM behavior, such as cell spreading, migration, and GBM stem-like cell marker expression. Furthermore, increasing stress relaxation enhances GBM proliferation and drug sensitivity. Stress-relaxation induced changes on GBM fates and drug response are found to be mediated through the cytoskeleton and transient receptor potential vanilloid-type 4. These results highlight the importance of incorporating viscoelasticity into 3D in vitro GBM models and provide novel insights into how viscoelasticity modulates GBM cell fates.

Antifungal Tolerance and Resistance Emerge at Distinct Drug Concentrations and Rely upon Different Aneuploid Chromosomes.

Antifungal drug tolerance is a response distinct from resistance, in which cells grow slowly above the MIC. Here, we found that the majority (69.2%) of 133 Candida albicans clinical isolates, including standard lab strain SC5314, exhibited temperature-enhanced tolerance at 37°C and 39°C, and were not tolerant at 30°C. Other isolates were either always tolerant (23.3%) or never tolerant (7.5%) at these three temperatures, suggesting that tolerance requires different physiological processes in different isolates. At supra-MIC fluconazole concentrations (8 to 128 µg/mL), tolerant colonies emerged rapidly at a frequency of ~10-3. In liquid passages over a broader range of fluconazole concentrations (0.25 to 128 µg/mL), tolerance emerged rapidly (within one passage) at supra-MICs. In contrast, resistance appeared at sub-MICs after 5 or more passages. Of 155 adaptors that evolved higher tolerance, all carried one of several recurrent aneuploid chromosomes, often including chromosome R, alone or in combination with other chromosomes. Furthermore, loss of these recurrent aneuploidies was associated with a loss of acquired tolerance, indicating that specific aneuploidies confer fluconazole tolerance. Thus, genetic background and physiology and the degree of drug stress (above or below the MIC) influence the evolutionary trajectories and dynamics with which antifungal drug resistance or tolerance emerges. IMPORTANCE Antifungal drug tolerance differs from drug resistance: tolerant cells grow slowly in drug, while resistant cells usually grow well, due to mutations in a few known genes. More than half of Candida albicans clinical isolates have higher tolerance at body temperature than they do at the lower temperatures used for most lab experiments. This implies that different isolates achieve drug tolerance via several cellular processes. When we evolved different strains at a range of high drug concentrations above inhibitory levels, tolerance emerged rapidly and at high frequency (one in 1,000 cells) while resistance appeared only later at very low drug concentrations. An extra copy of all or part of chromosome R was associated with tolerance, while point mutations or different aneuploidies were seen with resistance. Thus, genetic background and physiology, temperature, and drug concentration all influence how drug tolerance or resistance evolves.

Integrated proteomic and phosphoproteomic data-independent acquisition data evaluate the personalized drug responses of primary and metastatic tumors in colorectal cancer.

Mass spectrometry (MS)-based proteomics and phosphoproteomics are powerful methods to study the biological mechanisms, diagnostic biomarkers, prognostic analysis, and drug therapy of tumors. Data-independent acquisition (DIA) mode is considered to perform better than data-dependent acquisition (DDA) mode in terms of quantitative reproducibility, specificity, accuracy, and identification of low-abundance proteins. Mini patient derived xenograft (MiniPDX) model is an effective model to assess the response to antineoplastic drugs in vivo and is helpful for the precise treatment of cancer patients. Kinases are favorable spots for tumor-targeted drugs, and their functional completion relies on signaling pathways through phosphorylating downstream substrates. Kinase-phosphorylation networks or edge interactions are considered more credible and permanent for characterizing complex diseases. Here, we provide a workflow for personalized drug response assessment in primary and metastatic colorectal cancer (CRC) tumors using DIA proteomic data, DIA phosphoproteomic data, and MiniPDX models. Three kinase inhibitors, afatinib, gefitinib, and regorafenib, are tested pharmacologically. The process mainly includes the following steps: clinical tissue collection, sample preparation, hybrid spectral libraries establishment, MS data acquisition, kinase-substrate network construction, in vivo drug test, and elastic regression modeling. Our protocol gives a more direct data basis for individual drug responses, and will improve the selection of treatment strategies for patients without the druggable mutation.

Perfusion Air Culture of Precision-Cut Tumor Slices: An Ex Vivo System to Evaluate Individual Drug Response under Controlled Culture Conditions.

Precision-cut tumor slices (PCTS) maintain tissue heterogeneity concerning different cell types and preserve the tumor microenvironment (TME). Typically, PCTS are cultured statically on a filter support at an air-liquid interface, which gives rise to intra-slice gradients during culture. To overcome this problem, we developed a perfusion air culture (PAC) system that can provide a continuous and controlled oxygen medium, and drug supply. This makes it an adaptable ex vivo system for evaluating drug responses in a tissue-specific microenvironment. PCTS from mouse xenografts (MCF-7, H1437) and primary human ovarian tumors (primary OV) cultured in the PAC system maintained the morphology, proliferation, and TME for more than 7 days, and no intra-slice gradients were observed. Cultured PCTS were analyzed for DNA damage, apoptosis, and transcriptional biomarkers for the cellular stress response. For the primary OV slices, cisplatin treatment induced a diverse increase in the cleavage of caspase-3 and PD-L1 expression, indicating a heterogeneous response to drug treatment between patients. Immune cells were preserved throughout the culturing period, indicating that immune therapy can be analyzed. The novel PAC system is suitable for assessing individual drug responses and can thus be used as a preclinical model to predict in vivo therapy responses.

Multifocal Organoid Capturing of Colon Cancer Reveals Pervasive Intratumoral Heterogenous Drug Responses.

Intratumor heterogeneity (ITH) stands as one of the main difficulties in the treatment of colorectal cancer (CRC) as it causes the development of resistant clones and leads to heterogeneous drug responses. Here, 12 sets of patient-derived organoids (PDOs) and cell lines (PDCs) isolated from multiple regions of single tumors from 12 patients, capturing ITH by multiregion sampling of individual tumors, are presented. Whole-exome sequencing and RNA sequencing of the 12 sets are performed. The PDOs and PDCs of the 12 sets are also analyzed with a clinically relevant 24-compound library to assess their drug responses. The results reveal unexpectedly widespread subregional heterogeneity among PDOs and PDCs isolated from a single tumor, which is manifested by genetic and transcriptional heterogeneity and strong variance in drug responses, while each PDO still recapitulates the major histologic, genomic, and transcriptomic characteristics of the primary tumor. The data suggest an imminent drawback of single biopsy-originated PDO-based clinical diagnosis in evaluating CRC patient responses. Instead, the results indicate the importance of targeting common somatic driver mutations positioned in the trunk of all tumor subregional clones in parallel with a comprehensive understanding of the molecular ITH of each tumor.

Enhancer RNA-based modeling of adverse events and objective responses of cancer immunotherapy reveals associated key enhancers and target genes.

Immune checkpoint inhibitors (ICI) targeting PD-1/PD-L1 or CTLA-4 are emerging and effective immunotherapy strategies. However, ICI-treated patients present heterogeneous responses and adverse events, thus demanding effective ways to assess benefit over risk before treatment. Here, by integrating pan-cancer clinical and molecular data, we tried to predict immune-related adverse events (irAEs, risk) and objective response rates (ORRs, benefit) based on enhancer RNAs (eRNAs) expression among patients receiving anti-PD-1/PD-L1 therapies. We built two tri-variate (eRNAs) regression models, one (with ENSR00000326714, ENSR00000148786, and ENSR00000005553) explaining 71% variance (R=0.84) of irAEs and the other (with ENSR00000164478, ENSR00000035913, and ENSR00000167231) explaining 79% (R=0.89) of ORRs. Interestingly, target genes of irAE-related enhancers, including upstream regulators of MYC, were involved in metabolism, inflammation, and immune activation, while ORR-related enhancers target PAK2 and DLG1 which participate in T cell activation. More importantly, we found that ENSR00000148786 probably enhanced TMEM43/LUMA expression mainly in B cells to induce irAEs in ICI-treated patients. Our study provides references for the identification of immunotherapy-related biomarkers and potential therapeutic targets during immunotherapy.

Pan-Cancer Analysis of the Associations of TGFBI Expression With Prognosis and Immune Characteristics.

Transforming growth factor-beta-induced (TGFBI) protein has important roles in tumor growth, metastasis, and immunity. However, there is currently no pan-cancer evidence regarding TGFBI. In this study, we conducted a pan-cancer analysis of TGFBI mRNA and protein expression and prognoses of various cancer types using public databases. We also investigated the associations of TGFBI expression with tumor microenvironment (TME) components, immune cell infiltration, tumor mutational burden (TMB), and microsatellite instability (MSI), along with the TGFBI genetic alteration types. The results showed that TGFBI expression varied among different cancer types, and it was positively or negatively related to prognosis in various cancers. TGFBI expression was also significantly correlated with TME components, TMB, MSI, immune cell infiltration, and immunoinhibitory and immunostimulatory gene subsets. These findings indicate that TGFBI participates in various immune responses and it may function as a prognostic marker in various cancers. The findings may be useful for developing immunotherapies that target TGFBI.

The potential prognostic values of the ADAMTS-like protein family: an integrative pan-cancer analysis.

BACKGROUND: A disintegrin-like and metalloproteinase domain with thrombospondin type 1 motifs (ADAMTS)-like proteins, including ADAMTSL1-6 and papilin, which are part of the mammalian ADAMTS superfamily, appear to be relevant to extracellular matrix function and the regulation of ADAMTS protease activity. Their roles in tumor initiation and progression and regulating the tumor microenvironment (TME) are now recognized. METHODS: In the present study, a comprehensive investigation of the pan-cancer effects of ADAMTSLs and their associations with patient survival, drug responses, and the TME was performed by integrating The Cancer Genome Atlas (TCGA) data and annotated data resources. RESULTS: The expression of ADAMTSL family members was found to be dysregulated in many cancer types. More importantly, their expression was frequently associated with patients' overall survival (OS), drug responses, and the TME. ADAMTSL1, ADAMTSL4, and ADAMTSL5 were primarily associated with aggressive phenotypes, while PAPLN was more frequently associated with a favorable prognosis. In a non-small cell lung cancer (NSCLC) cohort, Thrombospondin Type 1 Domain Containing 4 (THSD4) (ADAMTSL6) and Papilin (PAPLN) were associated with immune checkpoint inhibitor (ICI) sensitivity in samples from the Gene Expression Omnibus repository (GSE135222). Twenty and 30 proteins related to THSD4 and PAPLN, respectively, were identified through a proteomic analysis of 18 Chinese lung adenocarcinoma patients. CONCLUSIONS: Our findings extend understandings of the role of the ADAMTSL family in cancers and are a valuable resource on their clinical utility. This article provides insight into the clinical importance of next-generation sequencing technology to identify novel biomarkers for prognosis and investigate therapeutic strategy for clinical benefit.