Identification of key modules and hub genes for small-cell lung carcinoma and large-cell neuroendocrine lung carcinoma by weighted gene co-expression network analysis of clinical tissue-proteomes

Comparative urine proteomic study involving papillary thyroid carcinoma and benign thyroid nodules

Introduction

Accurately differentiating benign and malignant lesions is essential for treatment. We aimed to determine differences in urine proteomics between papillary thyroid carcinomas (PTCs) and benign thyroid nodules (BTNs) and identify biomarkers for the differential diagnosis of these diseases.

Methods

We collected 155 specimens. In the discovery group, 30 PTC and 31 BTN specimens were quantitatively compared using liquid chromatography-tandem mass spectrometry (MS). The diagnostic value of each significantly altered protein was calculated in the MS validation comprising 11 PTC and 10 BTN samples. Ultimately, 36 BTN and 37 PTC specimens were used for ELISA validation.

Results and discussion

Overall, 2,479 proteins were used for quantitative analysis. Compared with benign nodules, papillary carcinomas showed significant increases and decreases in the levels of 169 and 27 proteins, respectively. Neck and thyroid tumors were enriched in the disease or function category. More than 100 proteins showed good performance in the area under the receiver operating characteristic curve (>0.8) upon MS validation. Semaphorin-6D showed good performance (AUC = 0.763) in ELISA validation. Urine proteomics is an effective diagnostic tool for distinguishing benign and malignant thyroid diseases. Semaphorin-6D may serve as a disease marker for large-scale validation and use. Additionally, this study identified potential biomarkers that warrant further investigation.

Fkbp5 gene deletion: Circadian rhythm profile and brain proteomics in aged mice

FKBP51, also known as FK506-binding protein 51, is a molecular chaperone and scaffolding protein with significant roles in regulating hormone signaling and responding to stress. Genetic variants in FKBP5, which encodes FKBP51, have been implicated in a growing number of neuropsychiatric disorders, which has spurred efforts to target FKBP51 therapeutically. However, the molecular mechanisms and sub-anatomical regions influenced by FKBP51 in these disorders are not fully understood. In this study, we aimed to examine the impact of Fkbp5 ablation using circadian phenotyping and molecular analyses. Our findings revealed that the lack of FKBP51 did not significantly alter circadian rhythms, as detected by wheel-running activity, but did offer protection against stress-mediated disruptions in rhythmicity in a sex-dependent manner. Protein changes in Fkbp5 KO mice, as measured by histology and proteomics, revealed alterations in a brain region- and sex-dependent manner. Notably, regardless of sex, aged Fkbp5 KOs showed elevated MYCBP2, FBXO45, and SPRYD3 levels, which are associated with neuronal-cell adhesion and synaptic integrity. Additionally, pathways such as serotonin receptor signaling and S100 family signaling were differentially regulated in Fkbp5 KO mice. Weighted protein correlation network analysis identified protein networks linked with synaptic transmission and neuroinflammation. The information generated by this work can be used to better understand the molecular changes in the brain during aging and in the absence of Fkbp5, which has implications for the continued development of FKBP51-focused therapeutics for stress-related disorders.

Proteomic Profiling of Small-Cell Lung Cancer: A Systematic Review

The accurate diagnosis of small-cell lung cancer (SCLC) is crucial, as treatment strategies differ from those of other lung cancers. This systematic review aims to identify proteins differentially expressed in SCLC compared to normal lung tissue, evaluating their potential utility in diagnosing and prognosing the disease. Additionally, the study identifies proteins differentially expressed between SCLC and large cell neuroendocrine carcinoma (LCNEC), aiming to discover biomarkers distinguishing between these two subtypes of neuroendocrine lung cancers. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search was conducted across PubMed/MEDLINE, Scopus, Embase, and Web of Science databases. Studies reporting proteomics information and confirming SCLC and/or LCNEC through histopathological and/or cytopathological examination were included, while review articles, non-original articles, and studies based on animal samples or cell lines were excluded. The initial search yielded 1705 articles, and after deduplication and screening, 16 articles were deemed eligible. These studies revealed 117 unique proteins significantly differentially expressed in SCLC compared to normal lung tissue, along with 37 unique proteins differentially expressed between SCLC and LCNEC. In conclusion, this review highlights the potential of proteomics technology in identifying novel biomarkers for diagnosing SCLC, predicting its prognosis, and distinguishing it from LCNEC.

Quantitative proteomic analysis of bronchoalveolar lavage fluids from patients with small cell lung cancers

PURPOSE

Small cell lung cancer (SCLC) is one of the malignant cancers with aggressive progression and poor prognosis. Bronchoalveolar lavage fluid (BALF) has been arising recently as a potential source of biomarkers for lung cancers. In this study, we performed quantitative BALF proteomic analysis to identify potential biomarkers for SCLC.

EXPERIMENTAL DESIGN

BALF were collected from tumor-bearing lungs and non-tumor lungs of five SCLC patients. Then, BALF proteomes were prepared for a TMT-based quantitative mass spectrometry analysis. Differentially expressed proteins (DEP) were identified when considering individual variation. Potential SCLC biomarker candidates were validated by immunohistochemistry (IHC). A public database of multiple SCLC cell lines was used to evaluate the correlation of these markers with SCLC subtypes and chemo-drug responses.

RESULTS

We identified 460 BALF proteins in SCLC patients and observed considerable individual variation among the patients. Immunohistochemical analysis and bioinformatics resulted in the identification of CNDP2 and RNPEP as potential subtype markers for ASCL1 and NEUROD1, respectively. In addition, CNDP2 was found to be positively correlated with responses to etoposide, carboplatin, and irinotecan.

CONCLUSIONS AND CLINICAL RELEVANCE

BALF is an emerging source of biomarkers, making it useful for the diagnosis and prognosis of lung cancers. We characterized the proteomes of paired BALF samples collected from tumor-bearing and non-tumor lungs of SCLC patients. Several proteins were found elevated in tumor-bearing BALF, and especially CNDP2 and RNPEP appeared to be potential indicators for ASLC1-high and NEUROD1-high subtypes of SCLC, respectively. The positive correlation of CNDP2 with chemo-drug responses would help to make decisions for treatment of SCLC patients. These putative biomarkers could be comprehensively investigated for a clinical use towards precision medicine.

Regulation of transcriptome networks that mediate ginsenoside biosynthesis by essential ecological factors

Ginseng, a valuable Chinese medicinal herb, is renowned worldwide for its effectiveness in alleviating certain conditions and promoting overall health. In this study, we performed weighted gene co-expression network analysis (WGCNA) on the accumulation of essential saponins under the influence of 13 essential environmental factors (including air temperature, air bottom temperature, surface mean temperature, soil temperature, surface shortwave radiation, soil moisture, soil water content, rainfall, total precipitation, elevation, soil type, soil pH, and soil water potential). We identified a total of 40 transcript modules associated with typical environmental factors and the accumulation of essential saponins. Among these, 18 modules were closely related to the influence of typical environmental factors, whereas 22 modules were closely related to the accumulation of essential saponins. These results were verified by examining the transcriptome, saponin contents, environmental factor information and the published data and revealed the regulatory basis of saponin accumulation at the transcriptome level under the influence of essential environmental factors. We proposed a working model of saponin accumulation mediated by the transcriptional regulatory network that is affected by typical environmental factors. An isomorphic white-box neural network was constructed based on this model and the predicted results of the white-box neural network correlated with saponin accumulation. The effectiveness of our correlation-directed graph in predicting saponin contents was verified by bioinformatics analysis based on results obtained in this study and transcripts known to affect the biosynthesis of saponin Rb1. The directed graph represents a useful tool for manipulating saponin biosynthesis while considering the influence of essential environmental factors in ginseng and other medicinal plants.

Time-restricted eating alters the 24-hour profile of adipose tissue transcriptome in men with obesity

OBJECTIVE

Time-restricted eating (TRE) restores circadian rhythms in mice, but the evidence to support this in humans is limited. The objective of this study was to investigate the effects of TRE on 24-hour profiles of plasma metabolites, glucoregulatory hormones, and the subcutaneous adipose tissue (SAT) transcriptome in humans.

METHODS

Men (n = 15, age = 63 [4] years, BMI 30.5 [2.4] kg/m2 ) were recruited. A 35-hour metabolic ward stay was conducted at baseline and after 8 weeks of 10-hour TRE. Assessment included 24-hour profiles of plasma glucose, nonesterified fatty acid (NEFA), triglyceride, glucoregulatory hormones, and the SAT transcriptome. Dim light melatonin onset and cortisol area under the curve were calculated.

RESULTS

TRE did not alter dim light melatonin onset but reduced morning cortisol area under the curve. TRE altered 24-hour profiles of insulin, NEFA, triglyceride, and glucose-dependent insulinotropic peptide and increased transcripts of circadian locomotor output cycles protein kaput (CLOCK) and nuclear receptor subfamily 1 group D member 2 (NR1D2) and decreased period circadian regulator 1 (PER1) and nuclear receptor subfamily 1 group D member 1 (NR1D1) at 12:00 am. The rhythmicity of 450 genes was altered by TRE, which enriched in transcripts for transcription corepressor activity, DNA-binding transcription factor binding, regulation of chromatin organization, and small GTPase binding pathways. Weighted gene coexpression network analysis revealed eigengenes that were correlated with BMI, insulin, and NEFA.

CONCLUSIONS

TRE restored 24-hour profiles in hormones, metabolites, and genes controlling transcriptional regulation in SAT, which could underpin its metabolic health benefit.

Clinical features and molecular genetics associated with brain metastasis in suspected early-stage non-small cell lung cancer

Introduction

Regarding whether brain magnetic resonance imaging (MRI) should be routine in patients with suspected early-stage lung cancer, guideline recommendations are inconsistent. Therefore, we performed this study to evaluate the incidence of and risk factors for brain metastasis (BM) in patients with suspected early-stage non-small-cell lung cancer (NSCLC).

Methods

A review of the medical charts of consecutive NSCLC patients diagnosed between January 2006 and May 2020 was performed. We identified 1,382 NSCLC patients with clinical staging of T1/2aN0M0 (excluding BM), and investigated the incidence, clinical predictors, and prognosis of BM in the cohort. We also performed RNA-sequencing differential expression analysis using transcriptome of 8 patients, using DESeq2 package (version 1.32.0) with R (version 4.1.0).

Results

Among 1,382 patients, nine hundred forty-nine patients (68.7%) underwent brain MRI during staging, and 34 patients (3.6%) were shown to have BM. Firth's bias-reduced logistic regression showed that tumor size (OR 1.056; 95% CI 1.009-1.106, p=0.018) was the only predictor of BM, and pathologic type was not a predictor of BM in our cohort (p>0.05). The median overall survival for patients with brain metastasis was 5.5 years, which is better than previously reported in the literature. RNA-sequencing differential expression analysis revealed the top 10 significantly upregulated genes and top 10 significantly downregulated genes. Among the genes involved in BM, Unc-79 homolog, non-selective sodium leak channel (NALCN) channel complex subunit (UNC79) was the most highly expressed gene in the lung adenocarcinoma tissues from the BM group, and an in vitro assay using A549 cells revealed that the NALCN inhibitor suppressed lung cancer cell proliferation and migration.

Conclusions

Given the incidence and favorable outcome of BM in patients with suspected early-stage NSCLC, selective screening with brain MRI may be considered, especially in patients with high-risk features.

Clinic and genetic similarity assessments of atypical carcinoid, neuroendocrine neoplasm with atypical carcinoid morphology and elevated mitotic count and large cell neuroendocrine carcinoma

Background

Pulmonary neuroendocrine neoplasms can be divided into typical carcinoid, atypical carcinoid, large cell neuroendocrine carcinoma, and small cell (lung) carcinoma. According to the World Health Organization, these four neoplasms have different characteristics and morphological traits, mitotic counts, and necrotic status. Importantly, “a grey-zone” neoplasm with an atypical carcinoid-like morphology, where the mitotic rate exceeds the criterion of 10 mitoses per 2 mm², have still not been well classified. In clinical practice, the most controversial area is the limit of 11 mitoses to distinguish between atypical carcinoids and large cell neuroendocrine carcinomas.

Methods

Basic and clinical information was obtained from patient medical records. A series of grey-zone patients (n = 8) were selected for exploring their clinicopathological features. In addition, patients with atypical carcinoids (n = 9) and classical large cell neuroendocrine carcinomas (n = 14) were also included to compare their similarity to these neoplasms with respect to tumour morphology and immunohistochemical staining.

Results

We found that these grey-zone tumour sizes varied and affected mainly middle-aged and older men who smoked. Furthermore, similar gene mutations were found in the grey-zone neoplasms and large cell neuroendocrine carcinomas, for the mutated genes of these two are mainly involved in PI3K-Akt signal pathways and Pathways in cancer, including a biallelic alteration of TP53/RB1 and KEAP1.

Conclusions

Our findings indicate that neuroendocrine neoplasm with atypical carcinoid morphology and elevated mitotic counts is more similar to large cell neuroendocrine carcinoma than atypical carcinoid. Furthermore, this study may help improve diagnosing these special cases in clinical practice to avoid misdiagnosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09391-w.

Identification of co-expression hub genes for ferroptosis in kidney renal clear cell carcinoma based on weighted gene co-expression network analysis and The Cancer Genome Atlas clinical data

Renal clear cell carcinoma (KIRC) is one of the most common tumors worldwide and has a high mortality rate. Ferroptosis is a major mechanism of tumor occurrence and development, as well as important for prognosis and treatment of KIRC. Here, we conducted bioinformatics analysis to identify KIRC hub genes that target ferroptosis. By Weighted gene co-expression network analysis (WGCNA), 11 co-expression-related genes were screened out. According to Kaplan Meier's survival analysis of the data from the gene expression profile interactive analysis database, it was identified that the expression levels of two genes, PROM2 and PLIN2, are respectively related to prognosis. In conclusion, our findings indicate that PROM2 and PLIN2 may be effective new targets for the treatment and prognosis of KIRC.

Protein co-expression network-based profiles revealed from laser-microdissected cancerous cells of lung squamous-cell carcinomas

No therapeutic targets have been identified for lung squamous cell cancer (SqCC) which is the second most prevalent lung cancer because its molecular profiles remain unclear. This study aimed to unveil disease-related protein networks by proteomic and bioinformatic assessment of laser-microdissected cancerous cells from seven SqCCs compared with eight representative lung adenocarcinomas. We identified three network modules significant to lung SqCC using weighted gene co-expression network analysis. One module was intrinsically annotated to keratinization and cell proliferation of SqCC, accompanied by hypoxia-induced aerobic glycolysis, in which key regulators were activated (HIF1A, ROCK2, EFNA1-5) and highly suppressed (KMT2D). The other two modules were significant for translational initiation, nonsense-mediated mRNA decay, inhibited cell death, and interestingly, eIF2 signaling, in which key regulators, MYC and MLXIPL, were highly activated. Another key regulator LARP1, the master regulator in cap-dependent translation, was highly suppressed although upregulations were observed for hub proteins including EIF3F and LARP1 targeted ribosomal proteins, among which PS25 is the key ribosomal protein in IRES-dependent translation. Our results suggest an underlying progression mechanism largely caused by switching to the cap-independent, IRES-dependent translation of mRNA subsets encoding oncogenic proteins. Our findings may help to develop therapeutic strategies to improve patient outcomes.

Identification of hub genes in colorectal cancer based on weighted gene co-expression network analysis and clinical data from The Cancer Genome Atlas

Colorectal cancer (CRC) is one of the most common tumors worldwide and is associated with high mortality. Here we performed bioinformatics analysis, which we validated using immunohistochemistry in order to search for hub genes that might serve as biomarkers or therapeutic targets in CRC. Based on data from The Cancer Genome Atlas (TCGA), we identified 4832 genes differentially expressed between CRC and normal samples (1562 up-regulated and 3270 down-regulated in CRC). Gene ontology (GO) analysis showed that up-regulated genes were enriched mainly in organelle fission, cell cycle regulation, and DNA replication; down-regulated genes were enriched primarily in the regulation of ion transmembrane transport and ion homeostasis. Weighted gene co-expression network analysis (WGCNA) identified eight gene modules that were associated with clinical characteristics of CRC patients, including brown and blue modules that were associated with cancer onset. Analysis of the latter two hub modules revealed the following six hub genes: adhesion G protein-coupled receptor B3 (BAI3, also known as ADGRB3), cyclin F (CCNF), cytoskeleton-associated protein 2 like (CKAP2L), diaphanous-related formin 3 (DIAPH3), oxysterol binding protein-like 3 (OSBPL3), and RERG-like protein (RERGL). Expression levels of these hub genes were associated with prognosis, based on Kaplan–Meier survival analysis of data from the Gene Expression Profiling Interactive Analysis database. Immunohistochemistry of CRC tumor tissues confirmed that OSBPL3 is up-regulated in CRC. Our findings suggest that CCNF, DIAPH3, OSBPL3, and RERGL may be useful as therapeutic targets against CRC. BAI3 and CKAP2L may be novel biomarkers of the disease.

Identification of Liver Cancer Stem Cell Stemness Markers Using a Comparative Analysis of Public Data Sets

Purpose

Comparative reanalysis of single-cell transcriptomics data to gain useful novel insights into cancer stem cells (CSCs), which are a rare subset of cells within tumors, characterized by their capability to self-renew and differentiate, and their role in tumorigenicity.

Patients and Methods

This project utilized publically available liver single-cell RNA-seq datasets of liver cancer and liver progenitor cell types to demonstrate how shared large amounts of data can generate new and valuable information. The data were analyzed using EdgeR differential expression analysis, with focus on a set of 34 known stemness markers.

Results

We showed that the expression of stemness markers SOX9, KRT19, KRT7, and CD24, and Yamanaka factors Oct4 and SOX2 in CSCs was significantly elevated relative to progenitor cell types, potentially explaining their increased differentiation and replication potential.

Conclusion

These results help to further document the complementary expression changes that give CSCs their distinct phenotypic profile. Our findings have potential significance to advance our knowledge of the important genes relevant to CSCs.

Protein co-expression networks identified from HOT lesions of ER+HER2-Ki-67high luminal breast carcinomas

Patients with estrogen receptor-positive/human epidermal growth factor receptor 2-negative/Ki-67-high (ER+HER2-Ki-67high) luminal breast cancer have a worse prognosis and do not respond to hormonal treatment and chemotherapy. This study sought to identify disease-related protein networks significantly associated with this subtype, by assessing in-depth proteomes of 10 lesions of high and low Ki-67 values (HOT, five; COLD, five) microdissected from the five tumors. Weighted correlation network analysis screened by over-representative analysis identified the five modules significantly associated with the HOT lesions. Pathway enrichment analysis, together with causal network analysis, revealed pathways of ribosome-associated quality controls, heat shock response by oxidative stress and hypoxia, angiogenesis, and oxidative phosphorylation. A semi-quantitative correlation of key-protein expressions, protein co-regulation analysis, and multivariate correlation analysis suggested co-regulations via network-network interaction among the four HOT-characteristic modules. Predicted highly activated master and upstream regulators were most characteristic to ER-positive breast cancer and associated with oncogenic transformation, as well as resistance to chemotherapy and endocrine therapy. Interestingly, inhibited intervention causal networks of numerous chemical inhibitors were predicted within the top 10 lists for the WM2 and WM5 modules, suggesting involvement of potential therapeutic targets in those data-driven networks. Our findings may help develop therapeutic strategies to benefit patients.

Co-expression analysis of pancreatic cancer proteome reveals biology and prognostic biomarkers

PURPOSE

Despite extensive biological and clinical studies, including comprehensive genomic and transcriptomic profiling efforts, pancreatic ductal adenocarcinoma (PDAC) remains a devastating disease, with a poor survival and limited therapeutic options. The goal of this study was to assess co-expressed PDAC proteins and their associations with biological pathways and clinical parameters.

METHODS

Correlation network analysis is emerging as a powerful approach to infer tumor biology from omics data and to prioritize candidate genes as biomarkers or drug targets. In this study, we applied a weighted gene co-expression network analysis (WGCNA) to the proteome of 20 surgically resected PDAC specimens (PXD015744) and confirmed its clinical value in 82 independent primary cases.

RESULTS

Using WGCNA, we obtained twelve co-expressed clusters with a distinct biology. Notably, we found that one module enriched for metabolic processes and epithelial-mesenchymal-transition (EMT) was significantly associated with overall survival (p = 0.01) and disease-free survival (p = 0.03). The prognostic value of three proteins (SPTBN1, KHSRP and PYGL) belonging to this module was confirmed using immunohistochemistry in a cohort of 82 independent resected patients. Risk score evaluation of the prognostic signature confirmed its association with overall survival in multivariate analyses. Finally, immunofluorescence analysis confirmed co-expression of SPTBN1 and KHSRP in Hs766t PDAC cells.

CONCLUSIONS

Our WGCNA analysis revealed a PDAC module enriched for metabolic and EMT-associated processes. In addition, we found that three of the proteins involved were associated with PDAC survival.

Identification of Candidate Genes Associated with Charcot-Marie-Tooth Disease by Network and Pathway Analysis

Charcot-Marie-Tooth Disease (CMT) is the most common clinical genetic disease of the peripheral nervous system. Although many studies have focused on elucidating the pathogenesis of CMT, few focuses on achieving a systematic analysis of biology to decode the underlying pathological molecular mechanisms and the mechanism of its disease remains to be elucidated. So our study may provide further useful insights into the molecular mechanisms of CMT based on a systematic bioinformatics analysis. In the current study, by reviewing the literatures deposited in PUBMED, we identified 100 genes genetically related to CMT. Then, the functional features of the CMT-related genes were examined by R software and KOBAS, and the selected biological process crosstalk was visualized with the software Cytoscape. Moreover, CMT specific molecular network analysis was conducted by the Molecular Complex Detection (MCODE) Algorithm. The biological function enrichment analysis suggested that myelin sheath, axon, peripheral nervous system, mitochondrial function, various metabolic processes, and autophagy played important roles in CMT development. Aminoacyl-tRNA biosynthesis, metabolic pathways, and vasopressin-regulated water reabsorption were significantly enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway network, suggesting that these pathways may play key roles in CMT occurrence and development. According to the crosstalk, the biological processes could be roughly divided into a correlative module and two separate modules. MCODE clusters showed that in top 3 clusters, 13 of CMT-related genes were included in the network and 30 candidate genes were discovered which might be potentially related to CMT. The study may help to update the new understanding of the pathogenesis of CMT and expand the potential genes of CMT for further exploration.

Recent developments in PARP14 research

This review aims to reflect upon the major developments in PARP14 research from late 2017 to early 2020. In doing so, this report will focus on the continual elucidation of PARP14's function including an emerging role in viral replication. This is in addition to other functional developments in cancer and inflammation, along with reflecting upon the leads in inhibitor design, including the increased attention toward the macrodomain. This report will also include a brief recap on contemporary poly(ADP-ribose) polymerase inhibitors and reflect upon the development surrounding the other poly(ADP-ribose) polymerases to overall give a succinct update to assist the development of selective PARP14 inhibitors.

Mutant Proteomics of Lung Adenocarcinomas Harboring Different EGFR Mutations

Epidermal growth factor receptor EGFR major driver mutations may affect downstream molecular networks and pathways, which would influence treatment outcomes of non-small cell lung cancer (NSCLC). This study aimed to unveil profiles of mutant proteins expressed in lung adenocarcinomas of 36 patients harboring representative driver EGFR mutations (Ex19del, nine; L858R, nine; no Ex19del/L858R, 18). Surprisingly, the orthogonal partial least squares discriminant analysis performed for identified mutant proteins demonstrated the profound differences in distance among the different EGFR mutation groups, suggesting that cancer cells harboring L858R or Ex19del emerge from cellular origins different from L858R/Ex19del-negative cells. Weighted gene coexpression network analysis, together with over-representative analysis, identified 18 coexpressed modules and their eigen proteins. Pathways enriched differentially for both the L858R and Ex19del mutations included carboxylic acid metabolic process, cell cycle, developmental biology, cellular responses to stress, mitotic prophase, cell proliferation, growth, epithelial to mesenchymal transition (EMT), and immune system. The IPA causal network analysis identified the highly activated networks of PARPBP, HOXA1, and APH1 under the L858R mutation, whereas those of ASGR1, APEX1, BUB1, and MAPK10 were highly activated under the Ex19del mutation. Interestingly, the downregulated causal network of osimertinib intervention showed the highest significance in overlap p-value among most causal networks predicted under the L858R mutation. We also identified the causal network of MAPK interacting serine/threonine kinase 1/2 (MNK1/2) highly activated differentially under the L858R mutation. Tumor-suppressor AMOT, a component of the Hippo pathways, was highly inhibited commonly under both L858R and Ex19del mutations. Our results could identify disease-related protein molecular networks from the landscape of single amino acid variants. Our findings may help identify potential therapeutic targets and develop therapeutic strategies to improve patient outcomes.

A proteogenomic profile of early lung adenocarcinomas by protein co-expression network and genomic alteration analysis

The tumourigenesis of early lung adenocarcinomas, including adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and lepidic predominant invasive adenocarcinoma (LPA), remains unclear. This study aimed to capture disease-related molecular networks characterising each subtype and tumorigenesis by assessing 14 lung adenocarcinomas (AIS, five; MIA, five; LPA, four). Protein-protein interaction networks significant to the three subtypes were elucidated by weighted gene co-expression network analysis and pairwise G-statistics based analysis. Pathway enrichment analysis for AIS involved extracellular matrix proteoglycans and neutrophil degranulation pathway relating to tumour growth and angiogenesis. Whereas no direct networks were found for MIA, proteins significant to MIA were involved in oncogenic transformation, epithelial-mesenchymal transition, and detoxification in the lung. LPA was associated with pathways of HSF1-mediated heat shock response regulation, DNA damage repair, cell cycle regulation, and mitosis. Genomic alteration analysis suggested that LPA had both somatic mutations with loss of function and copy number gains more frequent than MIA. Oncogenic drivers were detected in both MIA and LPA, and also LPA had a higher degree of copy number loss than MIA. Our findings may help identifying potential therapeutic targets and developing therapeutic strategies to improve patient outcomes.

Disease-related cellular protein networks differentially affected under different EGFR mutations in lung adenocarcinoma

It is unclear how epidermal growth factor receptor EGFR major driver mutations (L858R or Ex19del) affect downstream molecular networks and pathways. This study aimed to provide information on the influences of these mutations. The study assessed 36 protein expression profiles of lung adenocarcinoma (Ex19del, nine; L858R, nine; no Ex19del/L858R, 18). Weighted gene co-expression network analysis together with analysis of variance-based screening identified 13 co-expressed modules and their eigen proteins. Pathway enrichment analysis for the Ex19del mutation demonstrated involvement of SUMOylation, epithelial and mesenchymal transition, ERK/mitogen-activated protein kinase signalling via phosphorylation and Hippo signalling. Additionally, analysis for the L858R mutation identified various pathways related to cancer cell survival and death. With regard to the Ex19del mutation, ROCK, RPS6KA1, ARF1, IL2RA and several ErbB pathways were upregulated, whereas AURK and GSKIP were downregulated. With regard to the L858R mutation, RB1, TSC22D3 and DOCK1 were downregulated, whereas various networks, including VEGFA, were moderately upregulated. In all mutation types, CD80/CD86 (B7), MHC, CIITA and IFGN were activated, whereas CD37 and SAFB were inhibited. Costimulatory immune-checkpoint pathways by B7/CD28 were mainly activated, whereas those by PD-1/PD-L1 were inhibited. Our findings may help identify potential therapeutic targets and develop therapeutic strategies to improve patient outcomes.

Translational Control of Secretory Proteins in Health and Disease

Secretory proteins are synthesized in a form of precursors with additional sequences at their N-terminal ends called signal peptides. The signal peptides are recognized co-translationally by signal recognition particle (SRP). This interaction leads to targeting to the endoplasmic reticulum (ER) membrane and translocation of the nascent chains into the ER lumen. It was demonstrated recently that in addition to a targeting function, SRP has a novel role in protection of secretory protein mRNAs from degradation. It was also found that the quality of secretory proteins is controlled by the recently discovered Regulation of Aberrant Protein Production (RAPP) pathway. RAPP monitors interactions of polypeptide nascent chains during their synthesis on the ribosomes and specifically degrades their mRNAs if these interactions are abolished due to mutations in the nascent chains or defects in the targeting factor. It was demonstrated that pathological RAPP activation is one of the molecular mechanisms of human diseases associated with defects in the secretory proteins. In this review, we discuss recent progress in understanding of translational control of secretory protein biogenesis on the ribosome and pathological consequences of its dysregulation in human diseases.

Molecular Signature of Subtypes of Non-Small-Cell Lung Cancer by Large-Scale Transcriptional Profiling: Identification of Key Modules and Genes by Weighted Gene Co-Expression Network Analysis (WGCNA)

Non-small-cell lung cancer (NSCLC) represents a heterogeneous group of malignancies consisting essentially of adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Although the diagnosis and treatment of ADC and SCC have been greatly improved in recent decades, there is still an urgent need to identify accurate transcriptome profile associated with the histological subtypes of NSCLC. The present study aims to identify the key dysregulated pathways and genes involved in the development of lung ADC and SCC and to relate them with the clinical traits. The transcriptional changes between tumour and normal lung tissues were investigated by RNA-seq. Gene ontology (GO), canonical pathways analysis with the prediction of upstream regulators, and weighted gene co-expression network analysis (WGCNA) to identify co-expressed modules and hub genes were used to explore the biological functions of the identified dysregulated genes. It was indicated that specific gene signatures differed significantly between ADC and SCC related to the distinct pathways. Of identified modules, four and two modules were the most related to clinical features in ADC and SCC, respectively. CTLA4, MZB1, NIP7, and BUB1B in ADC, as well as GNG11 and CCNB2 in SCC, are novel top hub genes in modules associated with tumour size, SUVmax, and recurrence-free survival. Our research provides a more effective understanding of the importance of biological pathways and the relationships between major genes in NSCLC in the perspective of searching for new molecular targets.

Current status of clinical proteogenomics in lung cancer

Introduction: Lung cancer is the leading cause of cancer death worldwide. Proteogenomics, a way to integrate genomics, transcriptomics, and proteomics, have emerged as a way to understand molecular causes in cancer tumorigenesis. This understanding will help identify therapeutic targets that are urgently needed to improve individual patient outcomes. Areas covered: To explore underlying molecular mechanisms of lung cancer subtypes, several efforts have used proteogenomic approaches that integrate next generation sequencing (NGS) and mass spectrometry (MS)-based technologies. Expert opinion: A large-scale, MS-based, proteomic analysis, together with both NGS-based genomic data and clinicopathological information, will facilitate establishing extensive databases for lung cancer subtypes that can be used for further proteogenomic analyzes. Proteogenomic strategies will further be understanding of how major driver mutations affect downstream molecular networks, resulting in lung cancer progression and malignancy, and how therapy-resistant cancers resistant are molecularly structured. These strategies require advanced bioinformatics based on a dynamic theory of network systems, rather than statistics, to accurately identify mutant proteins and their affected key networks.