Background: Papillary thyroid cancer (PTC) is the most common subtype of thyroid cancer, and inflammation relates significantly to its initiation and prognosis. Systematic exploration of the immunogenomic landscape therein to assist in PTC prognosis is therefore urgent. The Cancer Genome Atlas (TCGA) project provides a large number of genetic PTC samples that enable a comprehensive and reliable immunogenomic study.

Methods: We integrated the expression profiles of immune-related genes (IRGs) and progression-free intervals (PFIs) in survival in 493 PTC patients based on the TCGA dataset. Differentially-expressed and survival-associated IRGs in PTC patients were estimated a computational difference algorithm and COX regression analysis. The potential molecular mechanisms and properties of these PTC-specific IRGs were also explored with the help of computational biology. A new prognostic index based on immune-related genes was developed by using multivariable COX analysis.

Results: A total of 46 differentially expressed immune-related genes were significantly correlated with clinical outcome of PTC patients. Functional enrichment analysis revealed that these genes were actively involved in a cytokine-cytokine receptor interaction KEGG pathway. A prognostic signature based on RGs (AGTR1, CTGF, FAM3B, IL11, IL17C, PTH2R and SPAG11A) performed moderately in prognostic predictions and correlated with age, tumor stage, metastasis, number of lesions, and tumor burden. Intriguingly, the prognostic index based on IRGs reflected infiltration by several types of immune cells.

Conclusions: Together, our results screened several IRGs of clinical significance, revealed drivers of the immune repertoire, and demonstrated the importance of a personalized, IRG-based immune signature in the recognition, surveillance, and prognosis of PTC.

/pdf/development-of-a-prognostic-index-based-on-an-immunogenomic-1bv8crsss0.pdf

Development of a prognostic index based on an immunogenomic landscape analysis of papillary thyroid cancer

To evaluate whether germline variants in genes encoding pancreatic secretory enzymes contribute to pancreatic cancer susceptibility, we sequenced the coding regions of CPB1 and other genes encoding pancreatic secretory enzymes and known pancreatitis susceptibility genes (PRSS1, CPA1, CTRC, and SPINK1) in a hospital series of pancreatic cancer cases and controls. Variants in CPB1, CPA1 (encoding carboxypeptidase B1 and A1), and CTRC were evaluated in a second set of cases with familial pancreatic cancer and controls. More deleterious CPB1 variants, defined as having impaired protein secretion and induction of endoplasmic reticulum (ER) stress in transfected HEK 293T cells, were found in the hospital series of pancreatic cancer cases (5/986, 0.5%) than in controls (0/1,045, P = 0.027). Among familial pancreatic cancer cases, ER stress-inducing CPB1 variants were found in 4 of 593 (0.67%) vs. 0 of 967 additional controls (P = 0.020), with a combined prevalence in pancreatic cancer cases of 9/1,579 vs. 0/2,012 controls (P < 0.01). More ER stress-inducing CPA1 variants were also found in the combined set of hospital and familial cases with pancreatic cancer than in controls [7/1,546 vs. 1/2,012; P = 0.025; odds ratio, 9.36 (95% CI, 1.15-76.02)]. Overall, 16 (1%) of 1,579 pancreatic cancer cases had an ER stress-inducing CPA1 or CPB1 variant, compared with 1 of 2,068 controls (P < 0.00001). No other candidate genes had statistically significant differences in variant prevalence between cases and controls. Our study indicates ER stress-inducing variants in CPB1 and CPA1 are associated with pancreatic cancer susceptibility and implicate ER stress in pancreatic acinar cells in pancreatic cancer development.

https://www.pnas.org/content/pnas/115/18/4767.full.pdf

Mutations in the pancreatic secretory enzymes CPA1 and CPB1 are associated with pancreatic cancer

The limitation of current biomarker of early stage ovarian cancer and the anatomical location of ovarian (depths of the pelvic) make ovarian cancer difficult to be detected in early stage. Growing evidence shows exosomes as key information transmitters, it carried molecules, such as miRNAs, proteins, lipids, double-stranded DNA have been reported as promising biomarkers in many diseases. However, little is known about the protein and lipid composition of ovarian cancer. Here, we report proteomic and lipidomic analysis of exosomes derived from ovarian cancer cells (SKOV-3) and ovarian surface epithelial cells (HOSEPiC). A total of 1433 proteins and 1227 lipid species were identified from two cell line derived exosomes. Several lipid species and proteins significantly differ in SKOV-3 derived exosomes compared to those from HOSEPiC. For example, we noted that ChE and ZyE species were in general more abundant in exosomes from SKOV-3 than from HOSEPiC; Collagen type V alpha 2 chain (COL5A2) and lipoprotein lipase (LPL) were significantly higher in SKOV-3 derived exosomes than HOSEpic (p < 0.05). Our research indicates the promising role of exosomal proteins and lipids in the early diagnosis of ovarian cancer.

https://link.springer.com/content/pdf/10.1186/s13048-020-0609-y.pdf

Proteomic and lipidomic analysis of exosomes derived from ovarian cancer cells and ovarian surface epithelial cells.

Lung cancer remains a leading cause of cancer-associated mortality worldwide, however, molecular mechanisms underlying lung cancer tumorigenesis and progression remain unknown. Here, we report evidence showing that one member of the mammalian methyltransferase-like family (METTL), METTL7B, is a potential molecular target for treatment of non-small cell lung cancer (NSCLC). METTL7B expression was elevated in the majority of NSCLC comparing to normal tissues. Increased expression of METTL7B contributed to advanced stages of tumor development and poor survival in NSCLC patients. Lentivirus-mediated shRNA silencing of METTL7B suppressed proliferation and tumorigenesis of cancer cells in vitro and in vivo. Investigation on gene expression profiles of NSCLC cells revealed that abundant cell cycle related genes were downregulated in the absence of METTL7B. Pathway enrichment analysis indicated that METTL7B participated in cell cycle regulation. Notably, CCND1, a key regulator for G1/S transition, was significantly decreased with the depletion of METTL7B, resulting in G0/G1 arrest, indicating that METTL7B is critical for cell cycle progression. Taken together, our findings implicate that METTL7B is essential for NSCLC development and progression. METTL7B might serve as a potential therapeutic target for NSCLC.

/pdf/mettl7b-is-required-for-cancer-cell-proliferation-and-49vp0sb618.pdf

METTL7B Is Required for Cancer Cell Proliferation and Tumorigenesis in Non-Small Cell Lung Cancer.

The human family of Potassium (K+) Channel Tetramerization Domain (KCTD) proteins counts 25 members, and a significant number of them are still only partially characterized. While some of the KCTDs have been linked to neurological disorders or obesity, a growing tally of KCTDs are being associated with cancer hallmarks or involved in the modulation of specific oncogenic pathways. Indeed, the potential relevance of the variegate KCTD family in cancer warrants an updated picture of the current knowledge and highlights the need for further research on KCTD members as either putative therapeutic targets, or diagnostic/prognostic markers. Homology between family members, capability to participate in ubiquitination and degradation of different protein targets, ability to heterodimerize between members, role played in the main signalling pathways involved in development and cancer, are all factors that need to be considered in the search for new key players in tumorigenesis. In this review we summarize the recent published evidence on KCTD members' involvement in cancer. Furthermore, by integrating this information with data extrapolated from public databases that suggest new potential associations with cancers, we hypothesize that the number of KCTD family members involved in tumorigenesis (either as positive or negative modulator) may be bigger than so far demonstrated. Video abstract.

/pdf/the-emerging-role-of-the-kctd-proteins-in-cancer-5egw4j8m1o.pdf

The emerging role of the KCTD proteins in cancer.

The hunt for the molecular markers with specificity and sensitivity has been a hot area for the tumor treatment. Due to the poor diagnosis and prognosis of pancreatic cancer (PC), the excision rate is often low, which makes it more urgent to find the ideal tumor markers. Robust Rank Aggreg (RRA) methods was firstly applied to identify the differentially expressed genes (DEGs) between PC tissues and normal tissues from GSE28735, GSE15471, GSE16515, and GSE101448. Among these DEGs, the highly correlated genes were clustered using WGCNA analysis. The co-expression networks and molecular complex detection (MCODE) Cytoscape app were then performed to find the sub-clusters and confirm 35 candidate genes. For these genes, least absolute shrinkage and selection operator (lasso) regression model was applied and validated to build a diagnostic risk score model. Cox proportional hazard regression analysis was used and validated to build a prognostic model. Based on integrated transcriptomic analysis, we identified a 19 gene module (SYCN, PNLIPRP1, CAP2, GNMT, MAT1A, ABAT, GPT2, ADHFE1, PHGDH, PSAT1, ERP27, PDIA2, MT1H, COMP, COL5A2, FN1, COL1A2, FAP and POSTN) as a specific predictive signature for the diagnosis of PC. Based on the two consideration, accuracy and feasibility, we simplified the diagnostic risk model as a four-gene model: 0.3034*log2(MAT1A)-0.1526*log2(MT1H) + 0.4645*log2(FN1) -0.2244*log2(FAP), log2(gene count). Besides, a four-hub gene module was also identified as prognostic model = − 1.400*log2(CEL) + 1.321*log2(CPA1) + 0.454*log2(POSTN) + 1.011*log2(PM20D1), log2(gene count). Integrated transcriptomic analysis identifies two four-hub gene modules as specific predictive signatures for the diagnosis and prognosis of PC, which may bring new sight for the clinical practice of PC.

/pdf/integrated-transcriptomic-analysis-reveals-hub-genes-c1m48oc3sk.pdf

Integrated transcriptomic analysis reveals hub genes involved in diagnosis and prognosis of pancreatic cancer

Comparison of three lymph node staging schemes for predicting the outcome in patients with small bowel adenocarcinoma: A population-based cohort and international multicentre cohort study.

Currently, the combination of ultrasonography and fine-needle aspiration biopsy (FNAB) can not discriminate between benign and malignant tumor of thyroid in some cases. The main issue in assessing the patients with thyroid nodules is to distinguish thyroid cancer from benign nodules, and reduce diagnostic surgery. To identify potential molecular biomarkers for patients with indeterminate FNAB, we explored the differentially expressed genes (DEGs) and differentially expressed long non-coding RNAs (DElncRNAs) in TCGA database between 318 papillary thyroid carcinoma (PTC) tissues and 35 normal thyroid gland tissues by DESeq R. Furthermore, DEGs were verified by gene expression profile GSE33630. Ten top DEGs and DElncRNAs were identified as candidate biomarkers for diagnosis and Lasso (Least Absolute Shrinkage and Selection Operator) logistic regression analysis were performed to improve the diagnostic accuracy of them. Besides, partial molecular biomarkers of top DEGs and DElncRNAs were closely related to the tumor stage (T), lymph node metastasis (N), metastasis (M) and pathological stage of PTC, which could reflect behavior of tumor progression. According to multivariate Cox analysis, the combination of two DEGs (METTL7B and KCTD16) and two DElncRNAs (LINC02454 and LINC02471) could predict the outcome in a more exact way. In conclusion, top DEGs and DElncRNAs could raise diagnosis of PTC in indeterminate FNAB specimens, and some could function as molecule biomarkers for tumor progression and prognosis.

Role of differentially expressed genes and long non-coding RNAs in papillary thyroid carcinoma diagnosis, progression, and prognosis.

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Integrated transcriptomic analysis reveals hub genes involved in diagnosis and prognosis of pancreatic cancer

Comparison of three lymph node staging schemes for predicting the outcome in patients with small bowel adenocarcinoma: A population-based cohort and international multicentre cohort study.

Role of differentially expressed genes and long non-coding RNAs in papillary thyroid carcinoma diagnosis, progression, and prognosis.