bims-meluca 2021-12-19 papers

Proc Natl Acad Sci U S A. 2021 Dec 21. pii: e2110633118. [Epub ahead of print]118(51):

Screening human lung cancer with predictive models of serum magnetic resonance spectroscopy metabolomics.

Tjada A Schult, Mara J Lauer, Yannick Berker, Marcella R Cardoso, Lindsey A Vandergrift, Piet Habbel, Johannes Nowak, Matthias Taupitz, Martin Aryee, Mari A Mino-Kenudson, David C Christiani, Leo L Cheng.

The current high mortality of human lung cancer stems largely from the lack of feasible, early disease detection tools. An effective test with serum metabolomics predictive models able to suggest patients harboring disease could expedite triage patient to specialized imaging assessment. Here, using a training-validation-testing-cohort design, we establish our high-resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS)-based metabolomics predictive models to indicate lung cancer presence and patient survival using serum samples collected prior to their disease diagnoses. Studied serum samples were collected from 79 patients before (within 5.0 y) and at lung cancer diagnosis. Disease predictive models were established by comparing serum metabolomic patterns between our training cohorts: patients with lung cancer at time of diagnosis, and matched healthy controls. These predictive models were then applied to evaluate serum samples of our validation and testing cohorts, all collected from patients before their lung cancer diagnosis. Our study found that the predictive model yielded values for prior-to-detection serum samples to be intermediate between values for patients at time of diagnosis and for healthy controls; these intermediate values significantly differed from both groups, with an F1 score = 0.628 for cancer prediction. Furthermore, values from metabolomics predictive model measured from prior-to-diagnosis sera could significantly predict 5-y survival for patients with localized disease.

Keywords: blood serum; high-resolution magic angle spinning; human lung cancer; magnetic resonance spectroscopy; metabolomics

DOI: https://doi.org/10.1073/pnas.2110633118

Medicine (Baltimore). 2021 Dec 17. 100(50): e28237

A promising prognostic signature for lung adenocarcinoma (LUAD) patients basing on 6 hypoxia-related genes.

Jie Luo, Xiaotian Du.

BACKGROUND: Hypoxia signaling plays a critical role in the development of lung adenocarcinoma (LUAD). We herein aimed to explore the prognostic value of hypoxia-related genes and construct the hypoxia-related prognostic signature for LUAD patients.METHODS: A total of 26 hypoxia-related genes were collected. Five hundred thirteen and 246 LUAD samples were obtained from the Cancer Genome Atlas and Gene Expression Omnibus databases, respectively. Univariate Cox regression and LASSO Cox regression analyses were conducted to screen the hypoxia-related genes associated with the prognosis of LUAD patients, which would be used for constructing prognosis predictive model for LUAD patients. Multivariate Cox regression analysis was done to determine the independent prognostic factors. The Nomogram model was constructed to predict the prognosis of LUAD patients.
RESULTS: Based on 26 hypoxia-related genes, LUAD samples could be divided into 4 clusters with different prognoses. Among which, 6 genes were included to construct the Risk Score and the LUAD patients with higher Risk Score had worse prognosis. Besides, the Nomogram based on all the independent risk factors could relatively reliably predict the survival probability. And 9 types of immune cells' infiltration was significantly differential between high and low risk LUAD patients.
CONCLUSION: The Risk Score model based on the 6 crucial hypoxia-related genes could relatively reliably predict the prognosis of LUAD patients.

DOI: https://doi.org/10.1097/MD.0000000000028237

BMC Pulm Med. 2021 Dec 11. 21(1): 409

Association of the Geriatric Nutritional Risk Index with the survival of patients with non-small-cell lung cancer after platinum-based chemotherapy.

Masato Karayama, Yusuke Inoue, Hideki Yasui, Hironao Hozumi, Yuzo Suzuki, Kazuki Furuhashi, Tomoyuki Fujisawa, Noriyuki Enomoto, Yutaro Nakamura, Naoki Inui, Takafumi Suda.

BACKGROUND: The nutritional status can potentially affect the efficacy of cancer therapy. The Geriatric Nutritional Risk Index (GNRI), a simple index for evaluating nutritional status calculated from body weight and serum albumin levels, has been reported to be associated with the prognosis of various diseases. However, the relationships between GNRI and the efficacy of platinum-based chemotherapy in patients with non-small-cell lung cancer (NSCLC) are unknown.METHODS: The pretreatment levels of GNRI were retrospectively evaluated in 148 chemo-naïve patients with advanced NSCLC who received first-line platinum-based chemotherapy and scored as low or high.
RESULTS: Patients with a high GNRI had a significantly higher overall response rate (ORR; 44.5% [95% confidence interval {CI} = 35.6%-53.9%] vs. 15.8% [95% CI = 7.4%-30.4%, p = 0.002), longer median progression-free survival (PFS; 6.3 months [95% CI = 5.6-7.2 months] vs. 3.8 months [95% CI = 2.5-4.7 months], p < 0.001), and longer median overall survival (OS; 22.8 months [95% CI = 16.7-27.2 months] vs. 8.5 months [95% CI = 5.4-16.0 months], p < 0.001) than those with low GNRI. High GNRI was independently predictive of better ORR in multivariate logistic regression analysis and longer PFS and OS in multivariate Cox proportional hazard analyses. In 71 patients who received second-line non-platinum chemotherapy, patients with high GNRI exhibited significantly longer PFS and OS than those with low GNRI (both p < 0.001).
CONCLUSIONS: GNRI was predictive of prolonged survival in patients with NSCLC who received first-line platinum-based chemotherapy and second-line non-platinum chemotherapy. Assessment of the nutritional status may be useful for predicting the efficacy of chemotherapy.

Keywords: Albumin; Cachexia; Hypoalbuminemia; Malnutrition; Nutrition

DOI: https://doi.org/10.1186/s12890-021-01782-2

Thorac Cancer. 2021 Dec 16.

Differential diagnosis of lung cancer and benign lung lesion using salivary metabolites: A preliminary study.

Satoshi Takamori, Shigeo Ishikawa, Jun Suzuki, Hiroyuki Oizumi, Tetsuro Uchida, Shohei Ueda, Kaoru Edamatsu, Mitsuyoshi Iino, Masahiro Sugimoto.

BACKGROUND: Saliva is often used as a biomarker for the diagnosis of some oral and systematic diseases, owing to the non-invasive attribute of the fluid. In this study, we aimed to identify salivary biomarkers for distinguishing lung cancer (LC) from benign lung lesion (BLL).MATERIALS AND METHODS: Unstimulated saliva samples were collected from 41 patients with LC and 21 with BLL. Salivary metabolites were comprehensively analyzed using capillary electrophoresis mass spectrometry. To differentiate between patients with LCs and BLLs, the discriminatory ability of each biomarker was assessed. Furthermore, a multiple logistic regression (MLR) model was developed for evaluating discriminatory ability of each salivary metabolite.
RESULTS: The profiles of 10 salivary metabolites were remarkably different between the LC and BLL samples. Among them, the concentration of salivary tryptophan was significantly lower in the samples from patients with LC than in those from patients with BLL, and the area under the curve (AUC) for discriminating patients with LC from those with BLL was 0.663 (95% confidence interval [CI] = 0.516-0.810, p = 0.036). Furthermore, from the MLR model developed using these metabolites, diethanolamine, cytosine, lysine, and tyrosine, were selected using the back-selection regression method. The MLR model based on these four metabolites had a high discriminatory ability for patients with LC and those with BLL (AUC = 0.729, 95% CI = 0.598-0.861, p = 0.003).
CONCLUSION: The four salivary metabolites can serve as potential non-invasive biomarkers for distinguishing LC from BLL.

Keywords: benign lung lesion; lung cancer; metabolites; saliva

DOI: https://doi.org/10.1111/1759-7714.14282

Cell Signal. 2021 Dec 14. pii: S0898-6568(21)00304-1. [Epub ahead of print] 110215

AMP-activated protein kinase re-sensitizes A549 to paclitaxel via up-regulating solute carrier organic anion transporter family member 1B3 expression.

Shanshan Jiang, Yunfei Luo, Zhan Zhan, Zhimin Tang, Junrong Zou, Ying Ying, Hui Lin, Deqiang Huang, Lingyu Luo.

Paclitaxel (PTX) is a common antineoplastic drug whose functionality is often restricted by drug resistance. Solute carrier organic anion transporter family member 1B3 (SLCO1B3) is a PTX influx transporter and its low expression has been proved to be relevant with PTX resistance. It has been widely reported that AMP-activated protein kinase (AMPK) could re-sensitize tumor cells to PTX. Our gene array result demonstrates AMPK up-regulated SLCO1B3. In this paper, we have tried to explain the relationships between PTX, SLCO1B3 and AMPK. First, we have verified the proliferative inhibition of PTX on A549 and found that PTX could inhibit A549 cells proliferation. Then, we have explored the relationship between SLCO1B3 and PTX: SLCO1B3 expression significantly decreased when A549 cells were treated with PTX or in A549 PTX resistant cells (A549-PTX) and the intracellular PTX concentration in A549-PTX was also lower. When treated with metformin/LKB1, both SLCO1B3 expression and intracellular PTX concentration have increased. Knockdown of AMPK has induced decreased SLCO1B3 expression. Moreover, in vitro and in vivo experiments have showed that metformin not only obviously inhibited A549-PTX tumor xenograft and A549-PTX proliferation alone, but also enhanced PTX efficacy to A549-PTX and this may be relevant to SLCO1B3. To verify it, we have treated A549 cells with AMPK both activators and an inhibitor, and then found that AMPK activators could weaken the PTX effect in inhibiting SLCO1B3 while its inhibitor has opposite effect. With knockdown of SLCO1B3, the effect of AMPK in re-sensitizing A549 to paclitaxel has decreased. To sum up, activation of AMPK can up-regulate SLCO1B3 expression, enhance the sensitivity of A549 cells to PTX, providing a new way to re-sensitize PTX resistance.

Keywords: AMPK; Metformin; NSCLC; Paclitaxel resistance; SLCO1B3

DOI: https://doi.org/10.1016/j.cellsig.2021.110215