A Description of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) Common Data Analysis Pipeline

Arginine deprivation enriches lung cancer proteomes with cysteine by inducing arginine-to-cysteine substitutants

Many types of human cancers suppress the expression of argininosuccinate synthase 1 (ASS1), a rate-limiting enzyme for arginine production. Although dependency on exogenous arginine can be harnessed by arginine-deprivation therapies, the impact of ASS1 suppression on the quality of the tumor proteome is unknown. We therefore interrogated proteomes of cancer patients for arginine codon reassignments (substitutants) and surprisingly identified a strong enrichment for cysteine (R>C) in lung tumors specifically. Most R>C events did not coincide with genetically encoded R>C mutations but were likely products of tRNA misalignments. The expression of R>C substitutants was highly associated with oncogenic kelch-like epichlorohydrin (ECH)-associated protein 1 (KEAP1)-pathway mutations and suppressed by intact-KEAP1 in KEAP1-mutated cancer cells. Finally, functional interrogation indicated a key role for R>C substitutants in cell survival to cisplatin, suggesting that regulatory codon reassignments endow cancer cells with more resilience to stress. Thus, we present a mechanism for enriching lung cancer proteomes with cysteines that may affect therapeutic decisions.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

NCI Cancer Research Data Commons: Resources to Share Key Cancer Data

Since 2014, the NCI has launched a series of data commons as part of the Cancer Research Data Commons (CRDC) ecosystem housing genomic, proteomic, imaging, and clinical data to support cancer research and promote data sharing of NCI-funded studies. This review describes each data commons (Genomic Data Commons, Proteomic Data Commons, Integrated Canine Data Commons, Cancer Data Service, Imaging Data Commons, and Clinical and Translational Data Commons), including their unique and shared features, accomplishments, and challenges. Also discussed is how the CRDC data commons implement Findable, Accessible, Interoperable, Reusable (FAIR) principles and promote data sharing in support of the new NIH Data Management and Sharing Policy. See related articles by Brady et al., p. 1384, Pot et al., p. 1396, and Kim et al., p. 1404.

Illuminating function of the understudied druggable kinome

The human kinome, with more than 500 proteins, is crucial for cell signaling and disease. Yet, about one-third of kinases lack in-depth study. The Data and Resource Generating Center for Understudied Kinases has developed multiple resources to address this challenge including creation of a heavy amino acid peptide library for parallel reaction monitoring and quantitation of protein kinase expression, use of understudied kinases tagged with a miniTurbo-biotin ligase to determine interaction networks by proximity-dependent protein biotinylation, NanoBRET probe development for screening chemical tool target specificity in live cells, characterization of small molecule chemical tools inhibiting understudied kinases, and computational tools for defining kinome architecture. These resources are available through the Dark Kinase Knowledgebase, supporting further research into these understudied protein kinases.

STMN1 Promotes Tumor Metastasis in Non-small Cell Lung Cancer Through Microtubule-dependent And Nonmicrotubule-dependent Pathways

The relationship between STMN1 and cancer metastasis is controversial. The purpose of this study was to explore the role and mechanism of STMN1 in NSCLC metastasis. In this study, we reported that STMN1 was highly expressed in NSCLC tissues and associated with poor prognosis. Both in vivo and in vitro functional assays confirmed that STMN1 promoted NSCLC metastasis. Further studies confirmed that STMN1 promoted cell migration by regulating microtubule stability. The results of Co-IP and LC‒MS/MS illustrated that STMN1 interacts with HMGA1. HMGA1 decreases microtubule stability by regulating the phosphorylation level of STMN1 at Ser16 and Ser38 after interacting with STMN1. This result suggested that STMN1 could be activated by HMGA1 to further promote NSCLC metastasis. Meanwhile, it has been found that STMN1 could promote cell migration by activating the p38MAPK/STAT1 signaling pathway, which is not dependent on microtubule stability. However, activating p38MAPK can decrease microtubule stability by promoting the dephosphorylation of STMN1 at ser16. A positive feedback loop was formed between STMN1 and p38MAPK to synergistically promote cell migration. In summary, our study demonstrated that STMN1 could promote NSCLC metastasis through microtubule-dependent and nonmicrotubule-dependent mechanisms. STMN1 has the potential to be a therapeutic target to inhibit metastasis.

Mass Spectrometry-Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine

Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.

Integrating single-cell and bulk transcriptomic analyses to develop a cancer-associated fibroblast-derived biomarker for predicting prognosis and therapeutic response in breast cancer

Background

Cancer-associated fibroblasts (CAFs) contribute to the progression and treatment of breast cancer (BRCA); however, risk signatures and molecular targets based on CAFs are limited. This study aims to identify novel CAF-related biomarkers to develop a risk signature for predicting the prognosis and therapeutic response of patients with BRCA.

Methods

CAF-related genes (CAFRGs) and a risk signature based on these genes were comprehensively analyzed using publicly available bulk and single-cell transcriptomic datasets. Modular genes identified from bulk sequencing data were intersected with CAF marker genes identified from single-cell analysis to obtain reliable CAFRGs. Signature CAFRGs were screened via Cox regression and least absolute shrinkage and selection operator (LASSO) analyses. Multiple patient cohorts were used to validate the prognosis and therapeutic responsiveness of high-risk patients stratified based on the CAFRG-based signature. In addition, the relationship between the CAFRG-based signature and clinicopathological factors, tumor immune landscape, functional pathways, chemotherapy sensitivity and immunotherapy sensitivity was examined. External datasets were used and sample experiments were performed to examine the expression pattern of MFAP4, a key CAFRG, in BRCA.

Results

Integrated analyses of single-cell and bulk transcriptomic data as well as prognostic screening revealed a total of 43 prognostic CAFRGs; of which, 14 genes (TLN2, SGCE, SDC1, SAV1, RUNX1, PDLIM4, OSMR, NT5E, MFAP4, IGFBP6, CTSO, COL12A1, CCDC8 and C1S) were identified as signature CAFRGs. The CAFRG-based risk signature exhibited favorable efficiency and accuracy in predicting survival outcomes and clinicopathological progression in multiple BRCA cohorts. Functional enrichment analysis suggested the involvement of the immune system, and the immune infiltration landscape significantly differed between the risk groups. Patients with high CAF-related risk scores (CAFRSs) exhibited tumor immunosuppression, enhanced cancer hallmarks and hyposensitivity to chemotherapy and immunotherapy. Five compounds were identified as promising therapeutic agents for high-CAFRS BRCA. External datasets and sample experiments validated the downregulation of MFAP4 and its strong correlation with CAFs in BRCA.

Conclusions

A novel CAF-derived gene signature with favorable predictive performance was developed in this study. This signature may be used to assess prognosis and guide individualized treatment for patients with BRCA.

iProPhos: A Web-Based Interactive Platform for Integrated Proteome and Phosphoproteome Analysis

Large-scale omics studies have generated a wealth of mass spectrometry-based proteomics data, which provide additional insights into disease biology spanning genomic boundaries. However, there is a notable lack of web-based analysis and visualization tools that facilitate the reutilization of these data. Given this challenge, we present iProPhos, a user-friendly web server to deliver interactive and customizable functionalities. iProPhos incorporates a large number of samples, including 1444 tumor samples and 746 normal samples across 12 cancer types, sourced from the Clinical Proteomic Tumor Analysis Consortium. Additionally, users can also upload their own proteomics/phosphoproteomics data for analysis and visualization. In iProPhos, users can perform profiling plotting and differential expression, patient survival, clinical feature-related, and correlation analyses, including protein-protein, mRNA-protein, and kinase-substrate correlations. Furthermore, functional enrichment, protein-protein interaction network, and kinase-substrate enrichment analyses are accessible. iProPhos displays the analytical results in interactive figures and tables with various selectable parameters. It is freely accessible at http://longlab-zju.cn/iProPhos without login requirement. We present two case studies to demonstrate that iProPhos can identify potential drug targets and upstream kinases contributing to site-specific phosphorylation. Ultimately, iProPhos allows end-users to leverage the value of big data in cancer proteomics more effectively and accelerates the discovery of novel therapeutic targets.

How missing value imputation is confounded with batch effects and what you can do about it

In data-processing pipelines, upstream steps can influence downstream processes because of their sequential nature. Among these data-processing steps, batch effect (BE) correction (BEC) and missing value imputation (MVI) are crucial for ensuring data suitability for advanced modeling and reducing the likelihood of false discoveries. Although BEC-MVI interactions are not well studied, they are ultimately interdependent. Batch sensitization can improve the quality of MVI. Conversely, accounting for missingness also improves proper BE estimation in BEC. Here, we discuss how BEC and MVI are interconnected and interdependent. We show how batch sensitization can improve any MVI and bring attention to the idea of BE-associated missing values (BEAMs). Finally, we discuss how batch-class imbalance problems can be mitigated by borrowing ideas from machine learning.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

A proteome-level view of brain tumors for a better understanding of novel diagnosis, prognosis, and therapy

INTRODUCTION

Brain tumors are complex and heterogeneous malignancies with significant challenges in diagnosis, prognosis, and therapy. Proteomics, the large-scale study of proteins and their functions, has emerged as a powerful tool to comprehensively investigate the molecular mechanisms underlying brain tumor regulation.

AREAS COVERED

This review explores brain tumors from a proteomic standpoint, highlighting recent progress and insights gained through proteomic methods. It delves into the proteomic techniques employed and underscores potential biomarkers for early detection, prognosis, and treatment planning. Recent PubMed Central proteomic studies (2017-present) are discussed, summarizing findings on altered protein expression, post-translational changes, and protein interactions. This sheds light on brain tumor signaling pathways and their significance in innovative therapeutic approaches.

EXPERT OPINION

Proteomics offers immense potential for revolutionizing brain tumor diagnosis and therapy. To unlock its full benefits, further translational research is crucial. Combining proteomics with other omics data enhances our grasp of brain tumors. Validating and translating proteomic biomarkers are vital for better patient results. Challenges include tumor complexity, lack of curated proteomic databases, and the need for collaboration between researchers and clinicians. Overcoming these challenges requires investment in technology, data sharing, and translational research.

CCL5 might be a prognostic biomarker and associated with immuno-therapeutic efficacy in cancers: A pan-cancer analysis

Purpose

Chemokine ligand 5 (CCL5), a vital member of the CC chemokine family, plays diverse roles in tumorigenesis, metastasis, and prognosis in various human tumors. However, no pan-cancer analysis has been conducted to illustrate its distinctive effects on clinical prognosis via underlying mechanisms and biological characteristics.

Methods

Herein, we exploited the existed public bioinformatics database, primarily TCGA database and GTEx data, to comprehensively analyze the value of CCL5 involved in patient prognosis.

Results

This study found that CCL5 was excessively expressed in most tumors and significantly associated with clinical prognosis in 10 out of 33 types of tumors. Notably, CCL5 might be an independent predictive biomarker of clinical outcome in SKCM patients, confirmed by univariate and multivariate Cox regression analysis. Furthermore, we acquired the genetic alteration status of CCL5 in multiple types of tumor tissues from TCGA cohorts. We revealed a potential correlation between the expression level of CCL5 and tumor mutational burden in 33 types of tumors. In addition, data showed that DNA methylation was associated with CCL5 gene expression in THCA, PRAD, LUSC, and BRCA cancers. Immune infiltration and immune checkpoints are fine indexes for evaluating immunotherapy. We uncovered that CCL5 was negatively correlated with the immune infiltration of CD8+ T cell, CD4+ T cell, macrophages, and gamma delta T cells in BRCA-basal and CESC tumors, while a significant positive correlation was observed in BLCA, COAD and other 7 types of tumors. Besides, CCL5 was closely associated with the immune checkpoint molecules in 8 types of tumors. The TIDE score was less in the CCL5 high-expressed group than in the CCL5 low-expressed group in SKCM patients, which indicated that CCL5 might be a fine monitor of immune response for immunotherapy. GO enrichment analysis data uncovered that cytokine-cytokine receptor interaction and chemokine signaling might be involved in the role of CCL5 in regulating tumor pathogenesis and prognosis.

Conclusion

In conclusion, CCL5 was preliminarly identified as a biomarker of immune response and prognosis for tumors patients via our first comprehensive pan-cancer analysis.

Multi-omics Pathways Workflow (MOPAW): An Automated Multi-omics Workflow on the Cancer Genomics Cloud

Introduction

In the era of big data, gene-set pathway analyses derived from multi-omics are exceptionally powerful. When preparing and analyzing high-dimensional multi-omics data, the installation process and programing skills required to use existing tools can be challenging. This is especially the case for those who are not familiar with coding. In addition, implementation with high performance computing solutions is required to run these tools efficiently.

Methods

We introduce an automatic multi-omics pathway workflow, a point and click graphical user interface to Multivariate Single Sample Gene Set Analysis (MOGSA), hosted on the Cancer Genomics Cloud by Seven Bridges Genomics. This workflow leverages the combination of different tools to perform data preparation for each given data types, dimensionality reduction, and MOGSA pathway analysis. The Omics data includes copy number alteration, transcriptomics data, proteomics and phosphoproteomics data. We have also provided an additional workflow to help with downloading data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium and preprocessing these data to be used for this multi-omics pathway workflow.

Results

The main outputs of this workflow are the distinct pathways for subgroups of interest provided by users, which are displayed in heatmaps if identified. In addition to this, graphs and tables are provided to users for reviewing.

Conclusion

Multi-omics Pathway Workflow requires no coding experience. Users can bring their own data or download and preprocess public datasets from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium using our additional workflow based on the samples of interest. Distinct overactivated or deactivated pathways for groups of interest can be found. This useful information is important in effective therapeutic targeting.

Integrated single-cell and bulk characterization of cuproptosis key regulator PDHB and association with tumor microenvironment infiltration in clear cell renal cell carcinoma

Background

Renal clear cell carcinoma (ccRCC) is one of the most prevalent cancers worldwide. Accumulating evidence revealed that copper-induced cell death played a vital role in various tumors. However, the underlying mechanism of cuproptosis with molecular heterogeneity and tumor microenvironment (TME) in ccRCC remains to be elucidated. The present study aimed to discover the biological function of cuproptosis regulators with the potential to guide clinical therapy.

Methods

Using Single-cell RNA-seq, bulk transcriptome and other multi-omics datasets, we identify essential cuproptosis-related hub gene PDHB for further study. The dysregulation of PDHB in ccRCC was characterized, together with survival outcomes, pathway enrichment and immune infiltration among tumor microenvironments. The functional significance and clinical association of PDHB was validated with loss of function experiments and surgical removal specimens.

Results

PDHB mRNA and protein expression level was significantly downregulated in ccRCC tissues compared with normal and paired normal tissues. Clinicopathological parameters and tissue microarray (TMA) indicated that PDHB was identified as a prognostic factor for survival outcomes among ccRCC patients. Additionally, low PDHB was negatively correlated with Treg cells, indicating an immunosuppressive microenvironment. Mechanistically, knockdown PDHB appeared to promote the RCC cells proliferation, migration, and invasion potentials. Subsequent studies showed that copper-induced cell death activation could overcome sunitinib resistance in RCC cells.

Conclusion

This research illustrated a cuproptosis-related hub gene PDHB which could serve as a potential prognostic marker and provide therapeutic benefits for clinical treatment of ccRCC patients.

An Inflection Point in Cancer Protein Biomarkers: What Was and What's Next

Biomarkers remain the highest value proposition in cancer medicine today - especially protein biomarkers. Yet despite decades of evolving regulatory frameworks to facilitate the review of emerging technologies, biomarkers have been mostly about promise with very little to show for improvements in human health. Cancer is an emergent property of a complex system and deconvoluting the integrative and dynamic nature of the overall system through biomarkers is a daunting proposition. The last two decades have seen an explosion of multi-omics profiling and a range of advanced technologies for precision medicine, including the emergence of liquid biopsy, exciting advances in single cell analysis, artificial intelligence (machine and deep learning) for data analysis and many other advanced technologies that promise to transform biomarker discovery. Combining multiple omics modalities to acquire a more comprehensive landscape of the disease state, we are increasingly developing biomarkers to support therapy selection and patient monitoring. Furthering precision medicine, especially in oncology, necessitates moving away from the lens of reductionist thinking towards viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to re-define biomarkers as representations of biological system states at different hierarchical levels of biological order. This definition could include traditional molecular, histologic, radiographic, or physiological characteristics, as well as emerging classes of digital markers and complex algorithms. To succeed in the future, we must move past purely observational individual studies and instead start building a mechanistic framework to enable integrative analysis of new studies within the context of prior studies. Identifying information in complex systems and applying theoretical constructs, such as information theory, to study cancer as a disease of dysregulated communication could prove to be "game changing" for the clinical outcome of cancer patients.

Proteogenomic characterization of ferroptosis regulators reveals therapeutic potential in glioblastoma

BACKGROUND

Ferroptosis is iron-dependent non-apoptotic cell death, that is characterized by the excessive accumulation of lipid peroxides. Ferroptosis-inducing therapy also shows promise in the treatment of cancers. However, ferroptosis-inducing therapy for glioblastoma multiforme (GBM) is still in the exploratory stage.

METHODS

We identified the differentially expressed ferroptosis regulators using Mann-Whitney U test in the proteome data from Clinical Proteomic Tumor Analysis Consortium (CPTAC). We next analyzed the effect of mutation on protein abundance. A multivariate Cox model was constructed to identify the prognostic signature.

RESULTS

In this study, we systemically portrayed the proteogenomic landscape of ferroptosis regulators in GBM. We observed that some mutation-specific ferroptosis regulators, such as down-regulated ACSL4 in EGFR-mutated patients and up-regulated FADS2 in IDH1-mutated patients, were linked to the inhibited ferroptosis activity in GBM. To interrogate the valuable treatment targets, we performed the survival analysis and identified five ferroptosis regulators (ACSL3, HSPB1, ELAVL1, IL33, and GPX4) as the prognostic biomarkers. We also validated their efficiency in external validation cohorts. Notably, we found overexpressed protein and phosphorylation abundances of HSPB1 were poor prognosis markers for overall survival of GBM to inhibit ferroptosis activity. Alternatively, HSPB1 showed a significant association with macrophage infiltration levels. Macrophage-secreted SPP1 could be a potential activator for HSPB1 in glioma cells. Finally, we recognized that ipatasertib, a novel pan-Akt inhibitor, could be a potential drug for suppressing HSPB1 phosphorylation, inducing ferroptosis of glioma cells.

CONCLUSION

In summary, our study characterized the proteogenomic landscape of ferroptosis regulators and identified that HSPB1 could be a candidate target for ferroptosis-inducing therapy strategy for GBM.

Noninvasive Evaluation of the Notch Signaling Pathway via Radiomic Signatures Based on Multiparametric MRI in Association With Biological Functions of Patients With Glioma: A Multi-institutional Study

BACKGROUND

Noninvasive determination of Notch signaling is important for prognostic evaluation and therapeutic intervention in glioma.

PURPOSE

To predict Notch signaling using multiparametric (mp) MRI radiomics and correlate with biological characteristics in gliomas.

STUDY TYPE

Retrospective.

POPULATION

A total of 63 patients for model construction and 47 patients from two public databases for external testing.

FIELD STRENGTH/SEQUENCE

A 1.5 T and 3.0 T, T1-weighted imaging (T1WI), T2WI, T2 fluid attenuated inversion recovery (FLAIR), contrast-enhanced (CE)-T1WI.

ASSESSMENT

Radiomic features were extracted from CE-T1WI, T1WI, T2WI, and T2FLAIR and imaging signatures were selected using a least absolute shrinkage and selection operator. Diagnostic performance was compared between single modality and a combined mpMRI radiomics model. A radiomic-clinical nomogram was constructed incorporating the mpMRI radiomic signature and Karnofsky Performance score. The performance was validated in the test set. The radiomic signatures were correlated with immunohistochemistry (IHC) analysis of downstream Notch pathway components.

STATISTICAL TESTS

Receiver operating characteristic curve, decision curve analysis (DCA), Pearson correlation, and Hosmer-Lemeshow test. A P value < 0.05 was considered statistically significant.

RESULTS

The radiomic signature derived from the combination of all sequences numerically showed highest area under the curve (AUC) in both training and external test sets (AUCs of 0.857 and 0.823). The radiomics nomogram that incorporated the mpMRI radiomic signature and KPS status resulted in AUCs of 0.891 and 0.859 in the training and test sets. The calibration curves showed good agreement between prediction and observation in both sets (P= 0.279 and 0.170, respectively). DCA confirmed the clinical usefulness of the nomogram. IHC identified Notch pathway inactivation and the expression levels of Hes1 correlated with higher combined radiomic scores (r = -0.711) in Notch1 mutant tumors.

DATA CONCLUSION

The mpMRI-based radiomics nomogram may reflect the intratumor heterogeneity associated with downstream biofunction that predicts Notch signaling in a noninvasive manner.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. By leveraging progress in proteomic technologies and network-based methodologies, over the past decade, we developed VESPA-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and used it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogation of tumor-specific enzyme/substrate interactions accurately inferred kinase and phosphatase activity, based on their inferred substrate phosphorylation state, effectively accounting for signal cross-talk and sparse phosphoproteome coverage. The analysis elucidated time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring that was experimentally confirmed by CRISPRko assays, suggesting broad applicability to cancer and other diseases.

BIRCH: An Automated Workflow for Evaluation, Correction, and Visualization of Batch Effect in Bottom-Up Mass Spectrometry-Based Proteomics Data

Recent surges in large-scale mass spectrometry (MS)-based proteomics studies demand a concurrent rise in methods to facilitate reliable and reproducible data analysis. Quantification of proteins in MS analysis can be affected by variations in technical factors such as sample preparation and data acquisition conditions leading to batch effects, which adds to noise in the data set. This may in turn affect the effectiveness of any biological conclusions derived from the data. Here we present Batch-effect Identification, Representation, and Correction of Heterogeneous data (BIRCH), a workflow for analysis and correction of batch effect through an automated, versatile, and easy to use web-based tool with the goal of eliminating technical variation. BIRCH also supports diagnosis of the data to check for the presence of batch effects, feasibility of batch correction, and imputation to deal with missing values in the data set. To illustrate the relevance of the tool, we explore two case studies, including an iPSC-derived cell study and a Covid vaccine study to show different context-specific use cases. Ultimately this tool can be used as an extremely powerful approach for eliminating technical bias while retaining biological bias, toward understanding disease mechanisms and potential therapeutics.

Risk stratification based on DNA damage-repair-related signature reflects the microenvironmental feature, metabolic status and therapeutic response of breast cancer

DNA damage-repair machinery participates in maintaining genomic integrity and affects tumorigenesis. Molecular signatures based on DNA damage-repair-related genes (DRGs) capable of comprehensively indicating the prognosis, tumor immunometabolic profile and therapeutic responsiveness of breast cancer (BRCA) patients are still lacking. Integrating public datasets and bioinformatics algorithms, we developed a robust prognostic signature based on 27 DRGs. Multiple patient cohorts identified significant differences in various types of survival between high- and low-risk patients stratified by the signature. The signature correlated well with clinicopathological factors and could serve as an independent prognostic indicator for BRCA patients. Furthermore, low-risk tumors were characterized by more infiltrated CD8⁺ T cells, follicular helper T cells, M1 macrophages, activated NK cells and resting dendritic cells, and fewer M0 and M2 macrophages. The favorable immune infiltration patterns of low-risk tumors were also accompanied by specific metabolic profiles, decreased DNA replication, and enhanced antitumor immunity. Low-risk patients may respond better to immunotherapy, and experience improved outcomes with conventional chemotherapy or targeted medicine. Real-world immunotherapy and chemotherapy cohorts verified the predictive results. Additionally, four small molecule compounds promising to target high-risk tumors were predicted. In vitro experiments confirmed the high expression of GNPNAT1 and MORF4L2 in BRCA tissues and their association with immune cells, and the knockdown of these two DRGs suppressed the proliferation of human BRCA cells. In summary, this DNA damage-repair-related signature performed well in predicting patient prognosis, immunometabolic profiles and therapeutic sensitivity, hopefully contributing to precision medicine and new target discovery of BRCA.

A novel transfer-learning based physician-level general and subtype classifier for non-small cell lung cancer

Confirming histological patterns of lung carcinoma is important for determining the prognosis and the next steps of treatment for a patient. Confirming the histologic patterns (subtype) of lung adenocarcinoma is important for determining the prognosis and treatment options for a patient. The task is challenging, and often requires the input of experienced pathologists, who by themselves lack interobserver concordance. A computer-aided diagnosis holds the potential to accelerate the time to diagnosis. As many adenocarcinoma tissue samples contain multiple histologic patterns, accurate computer-aided diagnosis requires annotations manually labeled by pathologists. We propose a method that merges weak supervised learning and Integrated Learning using Transfer Learning using two datasets: The Cancer Genome Atlas (TCGA), and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) to reduce the need for manual annotation by a pathologist while maintaining accuracy. Whole-slide images (WSI) are first determined to be either adenocarcinoma or squamous cell carcinoma, then further identify the subtypes by generating weak classifiers for each subtype, then using integrated learning to create a strong classifier. Our model was evaluated with independent datasets from the CPTAC dataset and a dataset from a private hospital. It can achieve AUC values of 0.86, 0.91, 0.82, 0.77, 0.96, 0.98 in Acinar, LPA, Micropapillary, Papillary, Solid, and Normal, respectively.

Extracellular Heparan 6-O-Endosulfatases SULF1 and SULF2 in Head and Neck Squamous Cell Carcinoma and Other Malignancies

Pan-cancer analysis of TCGA and CPTAC (proteomics) data shows that SULF1 and SULF2 are oncogenic in a number of human malignancies and associated with poor survival outcomes. Our studies document a consistent upregulation of SULF1 and SULF2 in HNSC which is associated with poor survival outcomes. These heparan sulfate editing enzymes were considered largely functional redundant but single-cell RNAseq (scRNAseq) shows that SULF1 is secreted by cancer-associated fibroblasts in contrast to the SULF2 derived from tumor cells. Our RNAScope and patient-derived xenograft (PDX) analysis of the HNSC tissues fully confirm the stromal source of SULF1 and explain the uniform impact of this enzyme on the biology of multiple malignancies. In summary, SULF2 expression increases in multiple malignancies but less consistently than SULF1, which uniformly increases in the tumor tissues and negatively impacts survival in several types of cancer even though its expression in cancer cells is low. This paradigm is common to multiple malignancies and suggests a potential for diagnostic and therapeutic targeting of the heparan sulfatases in cancer diseases.

The Ion Channel Gene KCNAB2 Is Associated with Poor Prognosis and Loss of Immune Infiltration in Lung Adenocarcinoma

The malignancy with the greatest global mortality rate is lung cancer. Lung adenocarcinoma (LUAD) is the most common subtype. The evidence demonstrated that voltage-gated potassium channel subunit beta-2 (KCNAB2) significantly participated in the initiation of colorectal cancer and its progression. However, the biological function of KCNAB2 in LUAD and its effect on the tumor immune microenvironment are still unknown. In this study, we found that the expression of KCNAB2 in tissues of patients with LUAD was markedly downregulated, and its downregulation was linked to accelerated cancer growth and poor clinical outcomes. In addition, low KCNAB2 expression was correlated with a deficiency in immune infiltration. The mechanism behind this issue might be that KCNAB2 influenced the immunological process such that the directed migration of immune cells was affected. Furthermore, overexpression of KCNAB2 in cell lines promoted the expression of CCL2, CCL3, CCL4, CCL18, CXCL9, CXCL10, and CXCL12, which are necessary for the recruitment of immune cells. In conclusion, KCNAB2 may play a key function in immune infiltration and can be exploited as a predictive biomarker for evaluating prognosis and a possible immunotherapeutic target.

Nano-omics: nanotechnology-based multidimensional harvesting of the blood-circulating cancerome

Over the past decade, the development of 'simple' blood tests that enable cancer screening, diagnosis or monitoring and facilitate the design of personalized therapies without the need for invasive tumour biopsy sampling has been a core ambition in cancer research. Data emerging from ongoing biomarker development efforts indicate that multiple markers, used individually or as part of a multimodal panel, are required to enhance the sensitivity and specificity of assays for early stage cancer detection. The discovery of cancer-associated molecular alterations that are reflected in blood at multiple dimensions (genome, epigenome, transcriptome, proteome and metabolome) and integration of the resultant multi-omics data have the potential to uncover novel biomarkers as well as to further elucidate the underlying molecular pathways. Herein, we review key advances in multi-omics liquid biopsy approaches and introduce the 'nano-omics' paradigm: the development and utilization of nanotechnology tools for the enrichment and subsequent omics analysis of the blood-circulating cancerome.

Proteomic Discovery and Validation of Novel Fluid Biomarkers for Improved Patient Selection and Prediction of Clinical Outcomes in Alzheimer’s Disease Patient Cohorts

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease characterized by progressive cognitive decline. The two cardinal neuropathological hallmarks of AD include the buildup of cerebral β amyloid (Aβ) plaques and neurofibrillary tangles of hyperphosphorylated tau. The current disease-modifying treatments are still not effective enough to lower the rate of cognitive decline. There is an urgent need to identify early detection and disease progression biomarkers that can facilitate AD drug development. The current established readouts based on the expression levels of amyloid beta, tau, and phospho-tau have shown many discrepancies in patient samples when linked to disease progression. There is an urgent need to identify diagnostic and disease progression biomarkers from blood, cerebrospinal fluid (CSF), or other biofluids that can facilitate the early detection of the disease and provide pharmacodynamic readouts for new drugs being tested in clinical trials. Advances in proteomic approaches using state-of-the-art mass spectrometry are now being increasingly applied to study AD disease mechanisms and identify drug targets and novel disease biomarkers. In this report, we describe the application of quantitative proteomic approaches for understanding AD pathophysiology, summarize the current knowledge gained from proteomic investigations of AD, and discuss the development and validation of new predictive and diagnostic disease biomarkers.

Melanoma RBPome identification reveals PDIA6 as an unconventional RNA-binding protein involved in metastasis

RNA-binding proteins (RBPs) have been relatively overlooked in cancer research despite their contribution to virtually every cancer hallmark. Here, we use RNA interactome capture (RIC) to characterize the melanoma RBPome and uncover novel RBPs involved in melanoma progression. Comparison of RIC profiles of a non-tumoral versus a metastatic cell line revealed prevalent changes in RNA-binding capacities that were not associated with changes in RBP levels. Extensive functional validation of a selected group of 24 RBPs using five different in vitro assays unveiled unanticipated roles of RBPs in melanoma malignancy. As proof-of-principle we focused on PDIA6, an ER-lumen chaperone that displayed a novel RNA-binding activity. We show that PDIA6 is involved in metastatic progression, map its RNA-binding domain, and find that RNA binding is required for PDIA6 tumorigenic properties. These results exemplify how RIC technologies can be harnessed to uncover novel vulnerabilities of cancer cells.

Violacein switches off low molecular weight tyrosine phosphatase and rewires mitochondria in colorectal cancer cells

In the last decade, emerging evidence has shown that low molecular weight protein tyrosine phosphatase (LMWPTP) not only contributes to the progression of cancer but is associated with prostate low survival rate and colorectal cancer metastasis. We report that LMWPTP favors the glycolytic profile in some tumors. Therefore, the focus of the present study was to identify metabolic enzymes that correlate with LMWPTP expression in patient samples. Exploratory data analysis from RNA-seq, proteomics, and histology staining, confirmed the higher expression of LMWPTP in CRC. Our descriptive statistical analyses indicate a positive expression correlation between LMWPTP and energy metabolism enzymes such as acetyl-CoA carboxylase (ACC) and fatty acid synthase (FASN). In addition, we examine the potential of violacein to reprogram energetic metabolism and LMWPTP activity. Violacein treatment induced a shift of glycolytic to oxidative metabolism associated with alteration in mitochondrial efficiency, as indicated by higher oxygen consumption rate. Particularly, violacein treated cells displayed higher proton leak and ATP-linked oxygen consumption rate (OCR) as an indicator of the OXPHOS preference. Notably, violacein is able to bind and inhibit LMWPTP. Since the LMWPTP acts as a hub of signaling pathways that offer tumor cells invasive advantages, such as survival and the ability to migrate, our findings highlight an unexplored potential of violacein in circumventing the metabolic plasticity of tumor cells.

Potential value of PRKDC as a therapeutic target and prognostic biomarker in pan-cancer

BACKGROUND

While protein kinase, DNA-activated, catalytic subunit (PRKDC) plays an important role in double-strand break repair to retain genomic stability, there is still no pan-cancer analysis based on large clinical information on the relationship between PRKDC and different tumors. For the first time, this research used numerous databases to perform a pan-cancer review for PRKDC to explore the possible mechanism of PRKDC in the etiology and outcomes in various tumors.

METHODS

PRKDC's expression profile and prognostic significance in pan-cancer were investigated based on various databases and online platforms, including TIMER2, GEPIA2, cBioPortal, CPTAC, and SangerBox. We applied the TIMER to identified the interlink of PRKDC and the immune infiltration in assorted tumors, and the SangerBox online platform was adopted to find out the relevance between PRKDC and immune checkpoint genes, tumor mutation burden, and microsatellite instability in tumors. GeneMANIA tool was employed to create a protein-protein interaction analysis, gene set enrichment analysis was conducted to performed gene enrichment analysis.

RESULTS

Overall, tumor tissue presented a higher degree of PRKDC expression than adjacent normal tissue. Meanwhile, patients with high PRKDC expression have a worse prognosis. PRKDC mutations were present in almost all The Cancer Genome Atlas tumors and might lead to a better survival prognosis. The PRKDC expression level was shown a positive correlation with tumor-infiltrating immune cells. PRKDC high expression cohorts were enriched in "cell cycle" "oocyte meiosis" and "RNA-degradation" signaling pathways.

CONCLUSIONS

This study revealed the potential value of PRKDC in tumor immunology and as a therapeutic target and prognostic biomarker in pan-cancer.

Long Non-Coding RNA-TMPO-AS1 as ceRNA Binding to let-7c-5p Upregulates STRIP2 Expression and Predicts Poor Prognosis in Lung Adenocarcinoma

Background

Striatin-interacting protein 2 (STRIP2), also called Fam40b, has been reported to regulate tumor cell growth. But the role of STRIP2 in lung adenocarcinoma (LUAD) has not been discovered clearly. Thus, the aim of our study is to explore the function and underlying mechanism of STRIP2 in LUAD.

Methods

Expression of STRIP2 was determined using the Cancer Genome Atlas (TCGA), GTEx, Ualcan, and the Human Protein Altas databases. The Correlation of STRIP2 and survival was detected by PrognoScan and Kaplan-Meier plotter databases. Besides, the correlation between STRIP2 expression and tumor immune infiltration as well as immune checkpoints were analyzed by the ssGSEA method. The biological function of STRIP2 and its co-expression genes was determined by gene ontology (GO) and Genes and Genomes (KEGG), respectively. Finally, the expression level and biological function of STRIP2 in LUAD were determined by qPCR, CCK8, transwell, and wound healing assays.

Results

This manuscript revealed a significantly increased expression of mRNA and protein of STRIP2 in lung adenocarcinoma compared with the adjacent normal tissues. GEO and Kaplan-Meier plotter databases showed higher STRIP2 expression levels were correlated with poor prognosis survival of LUAD. Moreover, Cox regression analysis suggested that a higher STRIP2 level served as an independent risk factor in predicting deteriorative overall survival (OS) for LUAD patients. SsGSEA results showed STRIP2 expression level was positively correlated with infiltrating levels of Th2 cells in LUAD. Lastly, GO analysis indicated the biological processes were enriched in nuclear division and positive regulation of the cell cycle. KEGG signaling pathway analysis showed STRIP2 was correlated with the MAPK signaling pathway and the TNF signaling pathway. The GSEA database showed that STRIP2 was positively associated with the epithelial-mesenchymal transition, cell cycle, and TNF signaling pathway. The QRT-PCR assay showed that STRIP2 was upregulated in LUAD cell lines. Cell proliferation and migration were inhibited in LUAD by knockdown of STRIP2. Moreover, we confirmed that the TMPO-AS1/let-7c-5p/STRIP2 network regulates STRIP2 overexpression in LUAD and is associated with poor prognosis.

Conclusion

Our findings indicated that STRIP2 acted as a crucial oncogene in LUAD and was correlated with unfavorable survival and tumor infiltration inflation.

LncRNA-AL035458.2/hsa-miR-181a-5p Axis-Mediated High Expression of NCAPG2 Correlates With Tumor Immune Infiltration and Non-Small Cell Lung Cancer Progression

Lung adenocarcinoma (LUAD) is the most common histological lung cancer, and it is the leading cause of cancer-related deaths worldwide. NCAPG2 (non-SMC condensin II complex subunit G2) has been shown to be upregulated in various human cancers. Nevertheless, the underlying biological function and potential mechanisms of NCAPG2 driving the progression of LUAD remain unclear. In this study, we investigated the role of NCAPG2 in LUAD and found that the expression of NCAPG2 in LUAD tissues was significantly higher than that of NCAPG2 expression in adjacent normal tissues. Kaplan–Meier survival analysis showed that patients with higher NCAPG2 expression correlated with unfavorable clinical outcomes. Receiver operating characteristic (ROC) curve analysis showed that the AUC value of NCAPG2 was 0.914. Correlation analysis showed that NCAPG2 expression was associated with immune infiltration in LUAD. Finally, we found that AL139385.1 was upregulated in LUAD cancer tissues and cell lines. Knockdown of NCAPG2 inhibited cell proliferation, cell migration, and cell invasion of LUAD in vitro. More importantly, we established the AL035458.2/hsa-miR-181a-5p axis as the most likely upstream ncRNA-related pathway of NCAPG2 in LUAD. In conclusion, our data demonstrated that ncRNA-mediated high expression of NCAPG2 was correlated with progression and immune infiltration, and could serve as a prognostic biomarker for LUAD.

Data integration and mechanistic modelling for breast cancer biology: Current state and future directions

Breast cancer is one of the most common cancers threatening women worldwide. A limited number of available treatment options, frequent recurrence, and drug resistance exacerbate the prognosis of breast cancer patients. Thus, there is an urgent need for methods to investigate novel treatment options, while taking into account the vast molecular heterogeneity of breast cancer. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics and metabolomics data, enable approaching breast cancer biology at multiple levels of omics interaction networks. Systems biology approaches, including computational inference of ‘big data’ and mechanistic modelling of specific pathways, are emerging to identify potential novel combinations of breast cancer subtype signatures and more diverse targeted therapies.

Protein Profiling of Breast Cancer for Treatment Decision-Making

The increasing use of neoadjuvant therapy has resulted in therapeutic decisions being made on the basis of diagnostic needle core biopsy. For many patients, this method might yield the only fragment of tumor available for biomarker analysis, necessitating judicious use. Many multiplex protein analytic methods have been developed that employ fluorescence or other tags to overcome the limitations of immunohistochemistry while still retaining the spatial annotation. Interpretation of the data can be difficult because of the limitations of the human eye. Computational deconvolution of the signals may be necessary for some of these methods to enable identification of cell-specific localization and coexpression of biomarkers. Herein, we present the different methods that are coming of age and their application in cancer research, with a focus on breast cancer. We also discuss the limitations, which include high costs and long turnaround times. The methods are also based on the premise that preanalytical factors will have identical impact on all proteins analyzed. There is a need to establish standards to normalize the data and enable cross-sample comparisons. In spite of these limitations, the multiplex technologies are extremely valuable discovery tools and can provide novel insights into the biology of cancer and mechanisms of drug resistance.

Cancer proteogenomics: current impact and future prospects

Genomic analyses in cancer have been enormously impactful, leading to the identification of driver mutations and development of targeted therapies. But the functions of the vast majority of somatic mutations and copy number variants in tumours remain unknown, and the causes of resistance to targeted therapies and methods to overcome them are poorly defined. Recent improvements in mass spectrometry-based proteomics now enable direct examination of the consequences of genomic aberrations, providing deep and quantitative characterization of tumour tissues. Integration of proteins and their post-translational modifications with genomic, epigenomic and transcriptomic data constitutes the new field of proteogenomics, and is already leading to new biological and diagnostic knowledge with the potential to improve our understanding of malignant transformation and therapeutic outcomes. In this Review we describe recent developments in proteogenomics and key findings from the proteogenomic analysis of a wide range of cancers. Considerations relevant to the selection and use of samples for proteogenomics and the current technologies used to generate, analyse and integrate proteomic with genomic data are described. Applications of proteogenomics in translational studies and immuno-oncology are rapidly emerging, and the prospect for their full integration into therapeutic trials and clinical care seems bright.

Identifying metabolic reprogramming phenotypes with glycolysis-lipid metabolism discoordination and intercellular communication for lung adenocarcinoma metastasis

Tumor metastasis imposes metabolic requirements for escaping from primary tissues, producing vulnerability in treatment. This study aimed to explore the metabolic reprogramming relevant to lung adenocarcinoma (LUAD) metastasis and decode the underlying intercellular alterations. Using the gene expression profiles of 394 LUAD samples derived from The Cancer Genome Atlas (TCGA), we identified 11 metastasis-related metabolic genes involved in glycolysis and lipid metabolism, and defined three metabolic reprogramming phenotypes (MP-I, -II, and -III) using unsupervised clustering. MP-III with the highest glycolytic and lowest lipid metabolic levels exhibited the highest metastatic potency and poorest survival in TCGA and six independent cohorts totaling 1,235 samples. Genomic analyses showed that mutations in TP53 and KEAP1, and deletions in SETD2 and PBRM1 might drive metabolic reprogramming in MP-III. Single-cell RNA-sequencing data from LUAD validated a metabolic evolutionary trajectory from normal to MP-II and MP-III, through MP-I. The further intercellular communications revealed that MP-III interacted uniquely with endothelial cells and fibroblasts in the ANGPTL pathway, and had stronger interactions with endothelial cells in the VEGF pathway. Herein, glycolysis-lipid dysregulation patterns suggested metabolic reprogramming phenotypes relevant to metastasis. Further insights into the oncogenic drivers and microenvironmental interactions would facilitate the treatment of LUAD metastasis in the future.

Transcriptomic analysis from lung adenocarcinoma identified an 11-gene signature that could classify metabolic reprogramming phenotypes in patients.

An overview of technologies for MS-based proteomics-centric multi-omics

INTRODUCTION

Mass spectrometry-based proteomics reveals dynamic molecular signatures underlying phenotypes reflecting normal and perturbed conditions in living systems. Although valuable on its own, the proteome has only one level of moleclar information, with the genome, epigenome, transcriptome, and metabolome, all providing complementary information. Multi-omic analysis integrating information from one or more of these other domains with proteomic information provides a more complete picture of molecular contributors to dynamic biological systems.

AREAS COVERED

Here, we discuss the improvements to mass spectrometry-based technologies, focused on peptide-based, bottom-up approaches that have enabled deep, quantitative characterization of complex proteomes. These advances are facilitating the integration of proteomics data with other 'omic information, providing a more complete picture of living systems. We also describe the current state of bioinformatics software and approaches for integrating proteomics and other 'omics data, critical for enabling new discoveries driven by multi-omics.

EXPERT COMMENTARY

Multi-omics, centered on the integration of proteomics information with other 'omic information, has tremendous promise for biological and biomedical studies. Continued advances in approaches for generating deep, reliable proteomic data and bioinformatics tools aimed at integrating data across 'omic domains will ensure the discoveries offered by these multi-omic studies continue to increase.

Recent Multiomics Approaches in Endometrial Cancer

Endometrial cancer is the most common gynecological cancers in developed countries. Many of the mechanisms involved in its initiation and progression remain unclear. Analysis providing comprehensive data on the genome, transcriptome, proteome, and epigenome could help in selecting molecular markers and targets in endometrial cancer. Multiomics approaches can reveal disturbances in multiple biological systems, giving a broader picture of the problem. However, they provide a large amount of data that require processing and further integration prior to analysis. There are several repositories of multiomics datasets, including endometrial cancer data, as well as portals allowing multiomics data analysis and visualization, including Oncomine, UALCAN, LinkedOmics, and miRDB. Multiomics approaches have also been applied in endometrial cancer research in order to identify novel molecular markers and therapeutic targets. This review describes in detail the latest findings on multiomics approaches in endometrial cancer.

Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported phosphopeptide identification and phosphosite localization results, underscoring a critical need for benchmarking. While efforts have been made to compare performance of computational pipelines using data from synthetic phosphopeptides, evaluations involving real application data have been largely limited to comparing the numbers of phosphopeptide identifications due to the lack of appropriate evaluation metrics. We investigated three deep-learning-derived features as potential evaluation metrics: phosphosite probability, Delta RT, and spectral similarity. Predicted phosphosite probability is computed by MusiteDeep, which provides high accuracy as previously reported; Delta RT is defined as the absolute retention time (RT) difference between RTs observed and predicted by AutoRT; and spectral similarity is defined as the Pearson’s correlation coefficient between spectra observed and predicted by pDeep2. Using a synthetic peptide dataset, we found that both Delta RT and spectral similarity provided excellent discrimination between correct and incorrect peptide-spectrum matches (PSMs) both when incorrect PSMs involved wrong peptide sequences and even when incorrect PSMs were caused by only incorrect phosphosite localization. Based on these results, we used all the three deep-learning-derived features as evaluation metrics to compare different computational pipelines on diverse set of phosphoproteomic datasets and showed their utility in benchmarking performance of the pipelines. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods.

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Computational method selection substantially affects phosphopeptide identification.

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Deep-learning-derived metrics effectively discriminate correct and incorrect PSMs.

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Novel metrics enable computational method comparison on real application data.

An Integrative Pan-Cancer Analysis of the Prognostic and Immunological Role of Casein Kinase 2 Alpha Protein 1 (CSNK2A1) in Human Cancers: A Study Based on Bioinformatics and Immunohistochemical Analysis

Background

Although emerging animal- or cell-based evidence supports the relationship between casein kinase 2 alpha protein 1 (CSNK2A1) and cancers, no pan-cancer analysis is available. Thus, this report aimed to display the prognostic landscape of CSNK2A1 in pan-cancer and investigate the relationship between CSNK2A1 and tumor immunity.

Methods

In the current study, we investigated the expression pattern, genetic alterations and survival analysis of CSNK2A1 in pan-cancer across multiple datasets and online platforms. The correlations between CSNK2A1 expression and tumor immunity were explored and visualized via R language software. Following this, immunohistochemical (IHC) staining and Kaplan–Meier survival analysis were conducted in clinical patients for proving the bioinformatic findings. Analysis of protein–protein interaction and gene functional enrichment was conducted using GeneMANIA platform and gene set enrichment analysis (GSEA), respectively.

Results

In TCGA, tumor tissue had a higher expression level of CSNK2A1 compared with that in corresponding normal tissue. An increased expression level of CSNK2A1 was related to poor clinical prognosis in most types of cancer such as LIHC. The following expression and survival analysis in clinical liver hepatocellular carcinoma (LIHC) patients confirmed these TCGA findings. CSNK2A1 expression had significant positive correlations with pro-tumor-infiltrating immune cells (TIICs) like M1-macrophages and fibroblasts, and significant negative correlations with anti-tumor-TIICs like activated CD8+ T cells and NK cells, suggesting specific interactions between CSNK2A1 and certain TIICs subtypes. Furthermore, CSNK2A1 expression had the most significant positive correlations with common markers of immune checkpoint including programmed death ligand-1 (PDL1) in LIHC. These findings were validated by an IHC analysis. GSEA analysis demonstrated that high expression of CSNK2A1 was related to cell signaling pathways and immunity-related activities.

Conclusion

These findings suggested that CSNK2A1 was not only related to poor clinical prognosis in cancer like LIHC but also a novel immunotherapy-related biomarker in cancers, especially in LIHC, shedding new light on anti-tumor strategy.

Protein-gene Expression Nexus: Comprehensive characterization of human cancer cell lines with proteogenomic analysis

Researchers have gained new therapeutic insights using multi-omics platform approaches to study DNA, RNA, and proteins of comprehensively characterized human cancer cell lines. To improve our understanding of the molecular features associated with oncogenic modulation in cancer, we proposed a proteogenomic database for human cancer cell lines, called Protein-gene Expression Nexus (PEN). We have expanded the characterization of cancer cell lines to include genetic, mRNA, and protein data of 145 cancer cell lines from various public studies. PEN contains proteomic and phosphoproteomic data on 4,129,728 peptides, 13,862 proteins, 7,138 phosphorylation site-associated genomic variations, 117 studies, and 12 cancer. We analyzed functional characterizations along with the integrated datasets, such as cis/trans association for copy number alteration (CNA), single amino acid variation for coding genes, post-translation modification site variation for Single Amino Acid Variation, and novel peptide expression for noncoding regions and fusion genes. PEN provides a user-friendly interface for searching, browsing, and downloading data and also supports the visualization of genome-wide association between CNA and expression, novel peptide landscape, mRNA-protein abundance, and functional annotation. Together, this dataset and PEN data portal provide a resource to accelerate cancer research using model cancer cell lines. PEN is freely accessible at http://combio.snu.ac.kr/pen.

The Combiome Hypothesis: Selecting Optimal Treatment for Cancer Patients

Existing approaches for cancer diagnosis are inefficient in the use of diagnostic tissue, and decision-making is often sequential, typically resulting in delayed treatment initiation. Future diagnostic testing needs to be faster and optimize increasingly complex treatment decisions. We envision a future where comprehensive testing is routine. Our approach, termed the "combiome," combines holistic information from the tumor, and the patient's immune system. The combiome model proposed here advocates synchronized up-front testing with a panel of sensitive assays, revealing a more complete understanding of the patient phenotype and improved targeting and sequencing of treatments. Development and eventual adoption of the combiome model for diagnostic testing may provide better outcomes for all cancer patients, but will require significant changes in workflows, technology, regulations, and administration. In this review, we discuss the current and future testing landscape, targeting of personalized treatments, and technological and regulatory advances necessary to achieve the combiome.

The Correlation Between SPP1 and Immune Escape of EGFR Mutant Lung Adenocarcinoma Was Explored by Bioinformatics Analysis

Background

Immune checkpoint inhibitors have achieved breakthrough efficacy in treating lung adenocarcinoma (LUAD) with wild-type epidermal growth factor receptor (EGFR), leading to the revision of the treatment guidelines. However, most patients with EGFR mutation are resistant to immunotherapy. It is particularly important to study the differences in tumor microenvironment (TME) between patients with and without EGFR mutation. However, relevant research has not been reported. Our previous study showed that secreted phosphoprotein 1 (SPP1) promotes macrophage M2 polarization and PD-L1 expression in LUAD, which may influence response to immunotherapy. Here, we assessed the role of SPP1 in different populations and its effects on the TME.

Methods

We compared the expression of SPP1 in LUAD tumor and normal tissues, and in samples with wild-type and mutant EGFR. We also evaluated the influence of SPP1 on survival. The LUAD data sets were downloaded from TCGA and CPTAC databases. Clinicopathologic characteristics associated with overall survival in TCGA were assessed using Cox regression analysis. GSEA revealed that several fundamental signaling pathways were enriched in the high SPP1 expression group. We applied CIBERSORT and xCell to calculate the proportion and abundance of tumor-infiltrating immune cells (TICs) in LUAD, and compared the differences in patients with high or low SPP1 expression and wild-type or mutant EGFR. In addition, we explored the correlation between SPP1 and CD276 for different groups.

Results

SPP1 expression was higher in LUAD tumor tissues and in people with EGFR mutation. High SPP1 expression was associated with poor prognosis. Univariate and multivariate cox analysis revealed that up-regulated SPP1 expression was independent indicator of poor prognosis. GSEA showed that the SPP1 high expression group was mainly enriched in immunosuppressed pathways. In the SPP1 high expression group, the infiltration of CD8+ T cells was lower and M2-type macrophages was higher. These results were also observed in patients with EGFR mutation. Furthermore, we found that the SPP1 expression was positively correlated with CD276, especially in patients with EGFR mutation.

Conclusion

SPP1 levels might be a useful marker of immunosuppression in patients with EGFR mutation, and could offer insight for therapeutics.

hsa_circ_0008234 inhibits the progression of lung adenocarcinoma by sponging miR-574-5p

circRNAs are a novel type of noncoding RNA (ncRNA) that have been identified as an important regulator of gene expression and play a part in the progression of various diseases. However, the function of circ_0008234 in lung adenocarcinoma (LUAC) remains unknown. Through the GEO (Gene Expression Omnibus) database, circ_0008234 was first found to be downregulated in LUAC tissues. It could inhibit cell growth and accelerate apoptosis in vitro and in vivo. In terms of its possible mechanism, circ_0008234 mainly was present in the cytoplasm and competed with miR-574-5p to regulate RND3 (Rho family GTPase 3). Our results revealed that circ_0008234 inhibited the progression of LUAC through a competing endogenous RNA (ceRNA)-based mechanism and provided potential biomarkers and therapeutic targets for LUAC treatment.

Prognostic Signature for Lung Adenocarcinoma Patients Based on Cell-Cycle-Related Genes

Objective

To screen lung adenocarcinoma (LUAC)-specific cell-cycle-related genes (CCRGs) and develop a prognostic signature for patients with LUAC.

Methods

The GSE68465, GSE42127, and GSE30219 data sets were downloaded from the GEO database. Single-sample gene set enrichment analysis was used to calculate the cell cycle enrichment of each sample in GSE68465 to identify CCRGs in LUAC. The differential CCRGs compared with LUAC data from The Cancer Genome Atlas were determined. The genetic data from GSE68465 were divided into an internal training group and a test group at a ratio of 1:1, and GSE42127 and GSE30219 were defined as external test groups. In addition, we combined LASSO (least absolute shrinkage and selection operator) and Cox regression analysis with the clinical information of the internal training group to construct a CCRG risk scoring model. Samples were divided into high- and low-risk groups according to the resulting risk values, and internal and external test sets were used to prove the validity of the signature. A nomogram evaluation model was used to predict prognosis. The CPTAC and HPA databases were chosen to verify the protein expression of CCRGs.

Results

We identified 10 LUAC-specific CCRGs (PKMYT1, ETF1, ECT2, BUB1B, RECQL4, TFRC, COCH, TUBB2B, PITX1, and CDC6) and constructed a model using the internal training group. Based on this model, LUAC patients were divided into high- and low-risk groups for further validation. Time-dependent receiver operating characteristic and Cox regression analyses suggested that the signature could precisely predict the prognosis of LUAC patients. Results obtained with CPTAC, HPA, and IHC supported significant dysregulation of these CCRGs in LUAC tissues.

Conclusion

This prognostic prediction signature based on CCRGs could help to evaluate the prognosis of LUAC patients. The 10 LUAC-specific CCRGs could be used as prognostic markers of LUAC.

MRPL42 is activated by YY1 to promote lung adenocarcinoma progression

Mammalian mitochondrial ribosomal proteins are a group of protein factors encoded by nuclear genes, responsible for the synthesis of proteins in mitochondria. As a member of mitochondrial ribosomal proteins, MRPL42 (mitochondrial ribosomal protein L42) belongs to 28S and 39S subunits. The current literature showed that its role in lung adenocarcinoma (LUAD) was not clear. We found that MRPL42 was highly expressed in early-stage LUAD tissues and cell lines, and remarkably related to the prognosis of patients. Knockdown of MRPL42 could reduce the proliferation and colonization, promote cell cycle arrest in G1/S phase, and weaken the migration and invasion ability of LUAD cells in vitro. Moreover, depletion of MRPL42 also inhibited tumor growth in vivo. Bioinformatics analysis found that YY1 may bind to the promoter region upstream of the MRPL42 gene to promote the transcription of MRPL42, which was verified by the ChIP and Dual luciferase reporter assay. QRT-PCR confirmed that knocking down YY1 could attenuate the expression of MRPL42. In summary, MRPL42 acts as an oncogene in LUAD, and its expression level is regulated by YY1.

Multi-Omic Biomarkers as Potential Tools for the Characterisation of Pancreatic Cystic Lesions and Cancer: Innovative Patient Data Integration

Pancreatic cancer (PC) is regarded as one of the most lethal malignant diseases in the world, with GLOBOCAN 2020 estimates indicating that PC was responsible for almost half a million deaths worldwide in 2020. Pancreatic cystic lesions (PCLs) are fluid-filled structures found within or on the surface of the pancreas, which can either be pre-malignant or have no malignant potential. While some PCLs are found in symptomatic patients, nowadays many PCLs are found incidentally in patients undergoing cross-sectional imaging for other reasons-so called 'incidentalomas'. Current methods of characterising PCLs are imperfect and vary hugely between institutions and countries. As such, there is a profound need for improved diagnostic algorithms. This could facilitate more accurate risk stratification of those PCLs that have malignant potential and reduce unnecessary surveillance. As PC continues to have such a poor prognosis, earlier recognition and risk stratification of PCLs may lead to better treatment protocols. This review will focus on the importance of biomarkers in the context of PCLs and PCand outline how current 'omics'-related work could contribute to the identification of a novel integrated biomarker profile for the risk stratification of patients with PCLs and PC.

Role of Multiomics Data to Understand Host-Pathogen Interactions in COVID-19 Pathogenesis

Human infectious diseases are contributed equally by the host immune system's efficiency and any pathogens' infectivity. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the coronavirus strain causing the respiratory pandemic coronavirus disease 2019 (COVID-19). To understand the pathobiology of SARS-CoV-2, one needs to unravel the intricacies of host immune response to the virus, the viral pathogen's mode of transmission, and alterations in specific biological pathways in the host allowing viral survival. This review critically analyzes recent research using high-throughput "omics" technologies (including proteomics and metabolomics) on various biospecimens that allow an increased understanding of the pathobiology of SARS-CoV-2 in humans. The altered biomolecule profile facilitates an understanding of altered biological pathways. Further, we have performed a meta-analysis of significantly altered biomolecular profiles in COVID-19 patients using bioinformatics tools. Our analysis deciphered alterations in the immune response, fatty acid, and amino acid metabolism and other pathways that cumulatively result in COVID-19 disease, including symptoms such as hyperglycemic and hypoxic sequelae.

PINA 3.0: mining cancer interactome

Protein–protein interactions (PPIs) are crucial to mediate biological functions, and understanding PPIs in cancer type-specific context could help decipher the underlying molecular mechanisms of tumorigenesis and identify potential therapeutic options. Therefore, we update the Protein Interaction Network Analysis (PINA) platform to version 3.0, to integrate the unified human interactome with RNA-seq transcriptomes and mass spectrometry-based proteomes across tens of cancer types. A number of new analytical utilities were developed to help characterize the cancer context for a PPI network, which includes inferring proteins with expression specificity and identifying candidate prognosis biomarkers, putative cancer drivers, and therapeutic targets for a specific cancer type; as well as identifying pairs of co-expressing interacting proteins across cancer types. Furthermore, a brand-new web interface has been designed to integrate these new utilities within an interactive network visualization environment, which allows users to quickly and comprehensively investigate the roles of human interacting proteins in a cancer type-specific context. PINA is freely available at https://omics.bjcancer.org/pina/.

Clinical value and potential mechanisms of LINC00221 in hepatocellular carcinoma based on integrated analysis

Aims:This study aimed to unveil the functional roles of LINC00221 in hepatocellular carcinoma (HCC). Materials and methods:A discovery cohort and a validation cohort were respectively used to identify and verify the clinical value of LINC00221 in HCC. Bioinformatics analysis was performed to explore its potential mechanisms. Results:LINC00221 was upregulated in HCC tissues and serum samples. Survival analysis and receiver operating characteristic curve further revealed its prognostic and diagnostic roles. Exploration of the mechanism showed that LINC00221 might exert a pro-cancer role via the lncRNA-miRNA-mRNA network.Conclusions: Our study reveals that upregulated LINC00221 can serve as a potential diagnostic and prognostic biomarker and provides novel clues as to the role of LINC00221 in HCC.