Associations of circulating mediators of inflammation, cell regulation and immune response with esophageal squamous cell carcinoma

Targeted plasma proteomic analysis uncovers a high-performance biomarker panel for early diagnosis of gastric cancer

BACKGROUND

Gastric cancer (GC) is characterized by high morbidity, high mortality and low early diagnosis rate. Early diagnosis plays a crucial role in radically treating GC. The aim of this study was to identify plasma biomarkers for GC and early GC diagnosis.

METHODS

We quantified 369 protein levels with plasma samples from discovery cohort (n = 88) and validation cohort (n = 50) via high-throughput proximity extension assay (PEA) utilizing the Olink-Explore-384-Cardiometabolic panel. The multi-protein signatures were derived from LASSO and Ridge regression models.

RESULTS

In the discovery cohort, 13 proteins (GDF15, ITIH3, BOC, DPP7, EGFR, AMY2A, CCDC80, CD163, GPNMB, LTBP2, CTSZ, CCL18 and NECTIN2) were identified to distinguish GC (Stage I-IV) and early GC (HGIN-I) groups from control group with AUC of 0.994 and AUC of 0.998, severally. The validation cohort yielded AUC of 0.930 and AUC of 0.818 for GC and early GC, respectively.

CONCLUSIONS

This study identified a multi-protein signature with the potential to benefit clinical GC diagnosis, especially for Asian and early GC patients, which may contribute to the development of a less-invasive, convenient, and efficient early screening tool, promoting early diagnosis and treatment of GC and ultimately improving patient survival.

A pathway-based computational framework for identification of a new modal of multi-omics biomarkers and its application in esophageal cancer

BACKGROUND

The pathway-based strategy has been recently proposed for identifying biomarkers with the advantages of higher biological interpretability and cross-data robustness than the conventional gene-based strategy. However, its utility in clinical applications has been limited due to the high computational complexity and ill-defined performance.

OBJECTIVE

The current study presents a machine learning-based computational framework using multi-omics data for identifying a new modal of biomarkers, called pathway-derived core biomarkers, which have the advantages of both gene-based and pathway-based biomarkers.

METHODS

Machine-learning methods and gene-pathway network were integrated to select the pathway-derived core biomarkers. Multiple machine-learning algorithms were used to construct and validate the diagnostic models of the biomarkers based on more than 1400 multi-omics clinical samples of esophageal squamous cell carcinoma (ESCC).

RESULTS

The results showed that the classifier models based on the new modal biomarkers achieved superior performance in the training datasets with an average AUC/accuracy of 0.98/0.95 and 0.89/0.81 for mRNAs and miRNA, respectively, higher than the currently known classifier models based on the conventional gene-based strategy and pathway-based strategy. In the testing cohorts, the AUC/accuracy increased by 6.1 %/7.3 % than the models based on the native gene-based biomarkers. The improved performance was further confirmed in independent validation cohorts. Specifically, the sensitivity/specificity increased by ∼3 % and the variance significantly decreased by ∼69 % compared with that of the native gene-based biomarkers. Importantly, the pathway-derived core biomarkers also recovered 45 % more previously reported biomarkers than the gene-based biomarkers and are more functionally relevant to the ESCC etiology (involved in 14 versus 7 pathways related with ESCC or other cancer), highlighting the cross-data robustness of this new modal of biomarkers via enhanced functional relevance.

CONCLUSIONS

The results demonstrated that the new modal of biomarkers not only have improved predicting performance and robustness, but also exhibit higher functional interpretability thus leading to the potential application in cancer diagnosis.

The impact of circulating protein levels identified by affinity proteomics on short-term, overall breast cancer risk

OBJECTIVE

Current breast cancer risk prediction scores and algorithms can potentially be further improved by including molecular markers. To this end, we studied the association of circulating plasma proteins using Proximity Extension Assay (PEA) with incident breast cancer risk.

SUBJECTS

In this study, we included 1577 women participating in the prospective KARMA mammographic screening cohort.

RESULTS

In a targeted panel of 164 proteins, we found 8 candidates nominally significantly associated with short-term breast cancer risk (P < 0.05). Similarly, in an exploratory panel consisting of 2204 proteins, 115 were found nominally significantly associated (P < 0.05). However, none of the identified protein levels remained significant after adjustment for multiple testing. This lack of statistically significant findings was not due to limited power, but attributable to the small effect sizes observed even for nominally significant proteins. Similarly, adding plasma protein levels to established risk factors did not improve breast cancer risk prediction accuracy.

CONCLUSIONS

Our results indicate that the levels of the studied plasma proteins captured by the PEA method are unlikely to offer additional benefits for risk prediction of short-term overall breast cancer risk but could provide interesting insights into the biological basis of breast cancer in the future.

Immunoprofiling bronchoalveolar lavage cells reveals multifaceted smoking-associated immune dysfunction

Background

Bronchoalveolar lavage (BAL) is an underutilised tool in the search for pulmonary disease biomarkers. While leukocytes with effector and suppressor function play important roles in airway immunity and tumours, it remains unclear if frequencies and phenotypes of BAL leukocytes can be useful parameters in lung cancer studies and clinical trials. We therefore explored the utility of BAL leukocytes as a source of biomarkers interrogating the impact of smoking, a major lung cancer risk determinant, on pulmonary immunity.

Methods

In this "test case" observational study, BAL samples from 119 donors undergoing lung cancer screening and biopsy procedures were evaluated by conventional and spectral flow cytometry to exemplify the comprehensive immune analyses possible with this biospecimen. Proportions of major leukocyte populations and phenotypic markers levels were found. Multivariate linear rank sum analysis considering age, sex, cancer diagnosis and smoking status was performed.

Results

Significantly increased frequencies of myeloid-derived suppressor cells and PD-L1-expressing macrophages were found in current and former smokers compared to never-smokers. While cytotoxic CD8 T-cells and conventional CD4 helper T-cell frequencies were significantly reduced in current and former smokers, expression of immune checkpoints PD-1 and LAG-3 as well as Tregs proportions were increased. Lastly, the cellularity, viability and stability of several immune readouts under cryostorage suggested BAL samples are useful for correlative end-points in clinical trials.

Conclusions

Smoking is associated with heightened markers of immune dysfunction, readily assayable in BAL, that may reflect a permissive environment for cancer development and progression in the airway.

Dietary Inflammatory Nutrients and Esophageal Squamous Cell Carcinoma Risk: A Case-Control Study

We conducted a case-control study (532 cases and 532 control) in Chinese adults to investigate the independent and interactive effects of dietary nutrients (pro- or anti-inflammation) on Esophageal Squamous Cell Carcinoma (ESCC) risk. Dietary data were collected using a food questionnaire survey that included 171 items. Two algorithms, the Least Absolute Shrinkage and Selector Operation (LASSO) and Bayesian Kernel Machine Regression (BKMR) were employed to select indicators and evaluate the interactive effect of nutrients' mixture on ESCC risk. Thirteen nutrients were selected, including three pro-inflammatory nutrients (protein, fat and carbohydrate) and ten anti-inflammatory nutrients (fiber, Vitamin A, riboflavin, niacin, Vitamin C, Fe, Se, MUFA, n-3 PUFA and n-6 PUFA). Single-exposure effects of fat, carbohydrate and fiber significantly contributed to ESCC risk. The pro-inflammatory nutrients' submodel discovered that the combined effect was statistically associated with increased ESCC risk. In addition, a higher fat level was significantly associated with ESCC risk. On the contrary, for fiber and riboflavin, the anti-inflammatory nutrients' submodel delineated a significant negative effect on the risk of ESCC. Our result implies that dietary nutrients and their inflammatory traits significantly impacted ESCC occurrence. Additional studies are warranted to verify our findings.

The Role and Interactions of Programmed Cell Death 4 and its Regulation by microRNA in Transformed Cells of the Gastrointestinal Tract

Data from GLOBOCAN 2020 estimates that there were 19.3 million new cases of cancer and 10.0 million cancer-related deaths in 2020 and that this is predicted to increase by 47% in 2040. The combined burden of cancers of the gastrointestinal (GI) tract, including oesophageal-, gastric- and colorectal cancers, resulted in 22.6% of the cancer-related deaths in 2020 and 18.7% of new diagnosed cases. Understanding the aetiology of GI tract cancers should have a major impact on future therapies and lessen this substantial burden of disease. Many cancers of the GI tract have suppression of the tumour suppressor Programmed Cell Death 4 (PDCD4) and this has been linked to the expression of microRNAs which bind to the untranslated region of PDCD4 mRNA and either inhibit translation or target the mRNA for degradation. This review highlights the properties of PDCD4 and documents the evidence for the regulation of PDCD4 expression by microRNAs in cancers of the GI tract.

Serum Proteomics in Patients with Head and Neck Cancer: Peripheral Blood Immune Response to Treatment

In this real-world study, the aims were to prospectively evaluate the expression of inflammatory proteins in serum collected from head and neck cancer patients before and after treatment, and to assess whether there were differences in expression associated with treatment modalities. The mixed study cohort consisted of 180 patients with head and neck cancer. The most common tumor sites were the oropharynx (n = 81), the oral cavity (n = 53), and the larynx (n = 22). Blood tests for proteomics analysis were carried out before treatment, 7 weeks after the start of treatment, and 3 and 12 months after the termination of treatment. Sera were analyzed for 83 proteins using an immuno-oncology biomarker panel (Olink, Uppsala, Sweden). Patients were divided into four treatment groups: surgery alone (Surg group, n = 24), radiotherapy with or without surgery (RT group, n = 94), radiotherapy with concomitant cisplatin (CRT group, n = 47), and radiotherapy with concomitant targeted therapy (RT Cetux group, n = 15). For the overall cohort, the expression levels of 15 of the 83 proteins changed significantly between the pretreatment sample and the sample taken 7 weeks after the start of treatment. At 7 weeks after the start of treatment, 13 proteins showed lower expression in the CRT group compared to the RT group. The majority of the inflammatory proteins had returned to their pretreatment levels after 12 months. It was clearly demonstrated that cisplatin-based chemoradiation has immunological effects in patients with head and neck cancer. This analysis draws attention to several inflammatory proteins that are of interest for further studies.

Circulating immune- and inflammation-related biomarkers and early-stage noncardia gastric cancer risk

BACKGROUND

In Helicobacter pylori-driven gastric cancer, mucosal colonization induces chronic inflammation that may variably progress to cancer. Prospective studies of circulating inflammation-related proteins have suggested weak associations with gastric cancer risk. To assess potential utility as a screening tool in clinical settings, we examined circulating levels of a wide range of key inflammation molecules for associations with early-stage gastric cancer.

METHODS

We used pretreatment EDTA plasma from 239 individuals with early-stage noncardia gastric cancer (203 stage I and 36 stage II) and 256 age-frequency-matched H. pylori-seropositive cancer-free controls within the Hospital-based Epidemiologic Research Program at Aichi Cancer Center. Levels of 92 biomarkers were measured by proximity extension assays using Olink's Proseek Immuno-oncology Panel. Odds ratios (ORs) for association with gastric cancer risk were calculated for quantiles (two to four categories) of each biomarker from unconditional logistic regression models, adjusted for age, sex, smoking and alcohol consumption. Two-sided P values <0.05 were considered as significant. The false discovery rate (FDR) was used to correct for multiple comparisons.

RESULTS

Of 83 evaluable biomarkers, lower levels of TNFRSF12A (per quartile OR, 0.82; nominal P-trend = 0.02) and ADGRG1 (per quartile OR, 0.84; nominal P-trend = 0.03) were associated with early-stage gastric cancer but were not statistically significant after FDR correction.

CONCLUSION

Our study did not identify any inflammation-related biomarkers that may be useful for early disease detection. To date, this is the first assessment of circulating inflammation-related proteins in early-stage gastric cancer. Given the complex inflammation processes preceding malignant transformation, further investigation of other biomarkers is warranted.

Machine-Learning Analysis of Serum Proteomics in Neuropathic Pain after Nerve Injury in Breast Cancer Surgery Points at Chemokine Signaling via SIRT2 Regulation

BACKGROUND

Persistent postsurgical neuropathic pain (PPSNP) can occur after intraoperative damage to somatosensory nerves, with a prevalence of 29-57% in breast cancer surgery. Proteomics is an active research field in neuropathic pain and the first results support its utility for establishing diagnoses or finding therapy strategies.

METHODS

57 women (30 non-PPSNP/27 PPSNP) who had experienced a surgeon-verified intercostobrachial nerve injury during breast cancer surgery, were examined for patterns in 74 serum proteomic markers that allowed discrimination between subgroups with or without PPSNP. Serum samples were obtained both before and after surgery.

RESULTS

Unsupervised data analyses, including principal component analysis and self-organizing maps of artificial neurons, revealed patterns that supported a data structure consistent with pain-related subgroup (non-PPSPN vs. PPSNP) separation. Subsequent supervised machine learning-based analyses revealed 19 proteins (CD244, SIRT2, CCL28, CXCL9, CCL20, CCL3, IL.10RA, MCP.1, TRAIL, CCL25, IL10, uPA, CCL4, DNER, STAMPB, CCL23, CST5, CCL11, FGF.23) that were informative for subgroup separation. In cross-validated training and testing of six different machine-learned algorithms, subgroup assignment was significantly better than chance, whereas this was not possible when training the algorithms with randomly permuted data or with the protein markers not selected. In particular, sirtuin 2 emerged as a key protein, presenting both before and after breast cancer treatments in the PPSNP compared with the non-PPSNP subgroup.

CONCLUSIONS

The identified proteins play important roles in immune processes such as cell migration, chemotaxis, and cytokine-signaling. They also have considerable overlap with currently known targets of approved or investigational drugs. Taken together, several lines of unsupervised and supervised analyses pointed to structures in serum proteomics data, obtained before and after breast cancer surgery, that relate to neuroinflammatory processes associated with the development of neuropathic pain after an intraoperative nerve lesion.

Transcriptome Profiling Reveals B-Lineage Cells Contribute to the Poor Prognosis and Metastasis of Clear Cell Renal Cell Carcinoma

Although immune therapy can improve the treatment of clear cell renal cell carcinoma (ccRCC) significantly, there are still a large proportion of ccRCC patients who progress to metastasis. Targeting the pro-metastatic immune cell in the ccRCC microenvironment could provide a solution to this problem. In this study, B cells in ccRCC biopsies were identified by using scRNA-seq and flow cytometry. The findings indicated the presence of a pro-metastatic B cell type which could be further classified into 3 subpopulations, MARCH3, B2M and DTWD1, based on their large-scaled genetic profiles, rather than traditional Immature/Mature ones. Although all of the 3 subpopulations appeared to contribute to distant metastasis, B cell (B2M) was deemed to be the most essential. Moreover, STX16, CLASRP, ATIC, ACIN1 and SEMA4B, were genes found to be commonly up-regulated in the 3 subpopulations and this was correlated to a poor prognosis of ccRCC. Furthermore, the heterogeneity of plasma cells in ccRCC was also found to contribute to metastasis of the disease. This study offers potential novel therapeutic targets against distant metastasis of cancers, and can help to improve the therapeutic efficiency of ccRCC patients.