Research Article Clinical End-Points Associated with ...downloads.hindawi.com/journals/bmri/2015/827829.pdfResearch Article Clinical End-Points Associated with Mycobacterium tuberculosis

Research ArticleClinical End-Points Associated with Mycobacteriumtuberculosis and Lung Cancer Implications into Host-PathogenInteraction and Coevolution

Yansheng Tian1 Tong Hao2 Bin Cao1 Wei Zhang1 Yan Ma3 Qiang Lin4 and Xiaomin Li2

1Central Laboratory North China Oilfield Hospital of Hebei Medical University Renqiu Hebei 062552 China2Department of Pathology North China Oilfield Hospital of Hebei Medical University Renqiu Hebei 062552 China3Department of Pharmacy North China Oilfield Hospital of Hebei Medical University Renqiu Hebei 062552 China4Oncology Department North China Oilfield Hospital of Hebei Medical University Renqiu Hebei 062552 China

Correspondence should be addressed to Tong Hao haotong2000sinacom

Received 15 March 2015 Revised 29 June 2015 Accepted 6 August 2015

Academic Editor Song Yi

Copyright copy 2015 Yansheng Tian et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

There is a recent emerging theory that suggests a cross-link between pathogens and cancer In this context we examined theassociation between the Mycobacterium tuberculosis (MTB) with its L-forms (MTB-L) and lung cancer In the present study wehave optimized and applied a highly sensitive assay to detect the presence of MTB and MTB-L in 187 lung cancer samples and 39samples of other cancer origins By carefully controlling confounding factors we have found that 62 of the lung cancer samples areMTB-L positive while only 51 of the other cancer samples are MTB-L positive Through generalized linear models and randomforest models we have further identified a set of clinical end-points that are strongly associated with MTB-L presence Our findingprovides the basis for future studies to investigate the underlyingmechanism linkingMTB-L infection to lung cancer development

1 Introduction

Lung cancer is a leading cause of death with an annualmortality rate of 159 million people accounting for 193of all cancer mortality worldwide [1] Of notable concern inChina lung cancer mortality rate has continuously risen forthe past three decades at an alarming annual rate of 464This has now replaced liver cancer as the leading cause ofdeath from malignant tumors [2] Therefore understandingthe causes of lung cancer is of great importance

It has been speculated that Mycobacterium tuberculosis(MTB) primarily as a pathogen of the mammalian respi-ratory system is closely linked to the occurrence of lungcancer [3] Approximately one-third of the people in theworld have been infected by MTB [4] with 1 of theworld population becoming newly infected each year [5]Of particular interest 90ndash95 of MTB carriers have nosymptoms [6]Mycobacterium tuberculosis L forms (MTB-L)are a wall-defective and pleomorphic form of MTB and havemany characteristics different fromMTBrsquos original vegetative

forms [7] While investigating the incidence of lung cancerresearchers noticed thatMTB-L which have no cell wall mayact like some carcinogenic viruses that can induce cancer byDNA integration [7]

Generally MTB-L have low pathogenicity and activityTherefore MTB-L have often gone undiagnosed and havenot been identified as the cause of disease [7] For examplethe clinical presentations of MTB-L may be misclassified asldquochronic lymphadenitis of unknown originrdquo because inflam-mation may be the only detectable sign This often happensbecause of other viruses or mycoplasma infections [7] Con-sequently to avoid failing to detect ormisdiagnosingMTB-Lpathologists have adopted three sensitive methods (1) acid-fast (2) immunohistochemical staining and (3) isolation Ithas been reported that among acid-fast staining methodsIntensified Kinyounrsquos (IK) method has higher sensitivity thanthe Ziehl-Neelsen (ZN) method [7] and has emerged as themain acid-fast staining method However the sensitivity ofthe IK method to detect MTB is still improvable As a resultprobe-based methods with increasing performance are being

Hindawi Publishing CorporationBioMed Research InternationalVolume 2015 Article ID 827829 9 pageshttpdxdoiorg1011552015827829

2 BioMed Research International

Table 1 Patient characteristics

Characteristic Values Lung cancer TB non-lung-cancer Non-TB non-lung-cancerEligible patients Total number 187 31 39CategoryWHO (lung cancerhistologicalclassification) of 2004

Squamous cell carcinoma (SCC) 63 NA NALung adenocarcinoma (LAC) 77 NA NA

Bronchioloalveolar carcinoma (BAC) 27 NA NASmall cell lung carcinoma (SCLC) 20 NA NA

Clinical stage (UCC1997)

I II 61 NA NAIII IV 70 NA NA

Tumor size le3 cm 56 NA NAgt3 cm 75 NA NA

Lymph nodemetastasis

Yes 89 NA NANo 42 NA NA

History of TB Yes 19 31 NANo 112 0 NA

Gender Male 80 26 14Female 107 5 27

Pathology gradeI 22 NA NAII 60 NA NAIII 63 NA NA

Age 593 (mean) 103 (sd) 534 (mean) 91 (sd) 499 (mean) 76 (sd)

rapidly developed for MTB (including MTB-L) detection Inthis paper in order to improve the sensitivity of MTB andMTB-L detection probes targeting gene mpb64 are used inin situ hybridization Gene mpb64 is in the MTB genomeand its MPB64 protein is identified from MTB strain H37Rv[8 9] Although MTB-L cannot grow well in media forroutine examination of tuberculosis media culture remainsthe standard way to detect and isolate the infection However92-3TB-L media were reported to be suitable for the growthof both MTB vegetative forms and L-forms [7] In our labwe developed a standardized protocol of using 92-3TB and92-3TB-L media to maximize the isolation rate of L forms[10 11]

Currently there have been limited investigations on therelationship between lung cancer and MTB-L Our lab is akey pioneer in this field and published two primary studies Inthese two studies we showed thatMTB-L exist in lung cancerpatients as the primary type of MTB Unfortunately we wereunable to explain any of the tumorigenic mechanisms yet [1011]

In this study we examined 187 lung cancer tissues forMTBMTB-L presence by combining IK acid-fast stainingand in situ hybridization using mpb64 probes We comparedthe rate ofMTBMTB-L positive cases in lung cancer with therate among other cancer types By computational methodswe were able to extract key clinical features that can char-acterize a subpopulation of lung cancer samples with higherprevalence of MTBMTB-L

2 Materials and Methods

21 Samples Table 1 was the summary of clinical charac-teristics of all samples used in this study There were threemain groups of samples (1) lung cancer samples which wereused to study the association betweenMTBMTB-L and lung

cancer (2) tuberculosis group without cancer (TB non-lung-cancer) which was used as the positive control to estimatethe sensitivity of the MTBMTB-L detection assay and (3)a group of samples without TB (non-TB non-lung cancer)including hepatic gastric and breast cancers was used as thenegative control to estimate the specificity for theMTBMTB-L detection assay

The lung cancer samples were from 187 patients whowere diagnosed with lung cancer at the North China OilfieldHospital of Hebei Medical University Here they had surgeryto remove the cancer from the lung between 2003 and 2012A consensus cancer diagnosis was reached by pathology For131 out of 187 patients complete pathological records wereavailable For 47 out of 131 patients fresh surgery tissues wereavailable Within the 19 patients with history of TB sixteenpatients had ipsilateral tuberculosis and cancer while anotherthree had contralateral disease

For the TB non-lung-cancer group samples were from31 patients who had surgery from the North China OilfieldHospital of Hebei Medical University between 2004 and 2012(14 out of 31 patients had fresh surgery tissues) Amongthe 31 patients 19 had tuberculosis and 12 had tuberculouslymphadenitis

For the non-TB non-lung-cancer control group sampleswere from 39 hospitalized surgical patients in the NorthChina Oilfield Hospital of Hebei Medical University between2004 and 2012 Each patient went through a chest X-rayCT and OT tests that excluded the possibility of patients forhaving tuberculosis or lung cancer 17 out of 39 patients hadfresh surgery tissues Among the 39 patients 6 had hepaticcarcinoma 14 had gastric cancer and 19 had breast cancer

The research protocol was approved by ethic committeesof the North China Oilfield Hospital of Hebei MedicalUniversity where the samples were collected and all patientsprovided informed consent All specimens were handled

BioMed Research International 3

and made anonymous according to the ethical and legalguidelines

22 Reagents Targeted fragments My250-224 and My310-283 were chosen from the mpb64 gene (X75361 826 bp)from the standard strain Mycobacterium tuberculosis H37RThe probes for the two fragments were made and biotin-labeled by Tianjin Haoyang Biotechnology Company (Tian-jin China) The probe sequences were as follows

(1) 51015840-CGGTATCGGTGCCTTTCAACTCCTCGC-31015840(2) 51015840-GGGCAGGCTGATGTTGATGTTGTAGGC-31015840

The probes can be used separately or mixed together Inorder to increase the MTB detection rate these two probeswere mixed for this experiment The in situ hybridizationkit was purchased from Tianjin Haoyang BiotechnologyCompany Other reagents such as 92-3TB 92-3TBL PNB-MTB and PNB-MTB-L TCH-MTB and TCH-MTB-L andother liquid media were prepared by our lab

92-3TBL liquid media were made by first dissolving 2 g ofglucose 5 g of amino succinamic acid 25 g of monopotas-sium phosphate 1 g of monometallic sodium orthophos-phate 25 g of sodium citrate 05 g of magnesium sulfate 4 gof gelatin 30 g of sodium chloride and 20mL of glycerol intodistilledwater to a final volumeof 900mL (Allmaterials werepurchased from Beijing Chemical Works Beijing China)4 NaOH was added to the solution to adjust its pH to70 1mL of 1 malachite green (Ziyi Reagent CompanyShanghai China) that was added to the solution which wasthen split into vaccine bottles with 90mL in each bottleBottles were placed under eight pounds of pressure for 15minutes After cooling to room temperature 10mL of sterilebloodplasmawas added into each bottle (Fresh bloodplasmawas kept under 56 degrees Celsius for one hour followed by 4degrees Celsius overnight Blood plasma was brought up thenext day by centrifuging at 1500timesg for 30 minutes) Finallyaseptic technique was used to split the mixture into smalltest tubes with two to three mL in each tube Samples werecultured in an incubator for 48 hours to ensure no bacterialcontamination

92-3TB liquid media was made following the same pro-cedure as that for 92-3TBL liquid media with the exceptionthat no sodium chloride was added

p-Nitrobenzoic acid- (PNB-)MTBorMTBL liquidmediawere made by adding PNB (Shanghai Chemical WorksShanghai China) into 92-3TB or 92-3TBL liquid media tomake the final PNB concentration to 025mgmL

2-Thiophenecarboxylic acid hydrazide- (TCH-) MTB orMTBL liquid media were made by adding TCH (ShanghaiChemical Works Shanghai China) into 92-3TB or 92-3TBL liquid media to make the final TCH concentration to025mgmL

23 Methods Slide preparation tissue was harvested andfixed immediately with 4 paraformaldehyde (BeijingCellChip Biotechnology Co Ltd Beijing China) and 01MPBS (PH 70 to 76 in 11000 DEPC (Sigma-Aldrich St LouisMO USA)) followed by dehydration paraffin embedding

and sectioning of a series of 6 slides with a thickness rangingfrom 4 to 6 120583m

Intensified Kinyounrsquos (IK) acid-fast staining to detectMTB-L as used here this method has previously beendescribed in the literature [10] Briefly 3 g of Schiff stain (ZiyiReagent Company Shanghai China) was dissolved in 20mLof 95 ethanol (Ziyi Reagent Company Shanghai China)8mL of phenol (Beijing Chemical Works Beijing China)was added into 80mL of distilled water The final solutionwas filtered for storage Before each use 01mL of Tween 80(Beijing ChemicalWorks Beijing China) was added into themixture of the previous two solutions Decolorant was madeby dissolving 05muriatic acid in ethanol solution followedby adding 05mL of concentrated hydrochloric acid (BeijingChemical Works Beijing China) into 100mL of 95 ethanol(Ziyi Reagent Company Shanghai China) After staining01 g of methylene blue (Ziyi Reagent Company ShanghaiChina) was dissolved in 100mL of 02 acetic acid (BeijingChemical Works Beijing China) Detection of MTB-L wasbased on the frequency of finding a stained cell in a field of amicroscope If 0 out of 300 fields of a microscope was foundit was marked as MTB-L negative (minus) if 1 to 2 out of 300fields were found it was marked as MTB-L inconclusive (plusmn)otherwise if a range of 3 ormore out of 300 fields were foundit was marked as MTB-L positive (+)

The Ziehl-Neelsen staining to detect MTB-bacteria typethe slides were dewaxed and went through the follow-ing series of ethanol dehydration (Ziyi Reagent CompanyShanghai China) First carbol fuchsin solution was madeby dissolving 4 g of Schiff stain (Ziyi Reagent CompanyShanghai China) in 100mL of 95 ethanol (Ziyi ReagentCompany Shanghai China) to make 100mL of saturatedsolution Second 10mL of saturated solution was mixedwith 90mL of 5 phenol (Beijing Chemical Works BeijingChina) Third 3 muriatic acid ethanol solution was madeby adding 3mL of concentrated hydrochloric acid (BeijingChemical Works Beijing China) into 97mL of 95 ethanol(Ziyi Reagent Company Shanghai China) Lastly Lofflerrsquosalkaline methylene blue solution was made by dissolving2 g of methylene blue (Ziyi Reagent Company ShanghaiChina) into 100mL of 95 ethanol (Ziyi Reagent CompanyShanghai China) to make 100mL of saturated solution Thefinal solution wasmade by taking 30mL of saturated solutionand mixing it with 100mL of distilled water and 01mL of10 caustic potash (BeijingChemicalWorks Beijing China)These methods strictly followed TB Diagnostic BacteriologyTesting Protocols developed by the China Tuberculosis Asso-ciation

In situ hybridization to detect mpb64 gene expressionfollowing the procedure from the kit manual the probe washybridized to a lung cancer tissue slice and used as thepositive control while a lung cancer tissue without probehybridization was used as the negative control The detectionmethod was NBTBCIP method If the hybridized cancercell nucleus showed brown particles it was deemed mpb64positive To determine thempb64 expression status per slicefive high magnification views (400x) from each slice wereselected at random A mpb64-positive-cancer-cell rate foreach view was determined by counting the percentage of


Table 2 IK lowastMPB64 TCN ISH as response 99 samples positive negative = 69 30

Clinical endpoints 119875 value Values MTBL positive MTBL negative

Category 00832

Squamous cell carcinoma (SCC) 29 6Lung adenocarcinoma (LAC) 28 13

Bronchioloalveolar carcinoma (BAC) 8 6Small cell lung carcinoma (SCLC) 4 5

Clinical stage 04328 I II 27 15III IV 42 15

Tumor size 09732 le3 cm 28 13gt3 cm 41 17

Lymph node metastasis 01409 Yes 49 16No 20 14

Gender 03929 Male 31 10Female 38 20

Pathology grade 04674I 13 6II 34 11III 22 13

Age 09579 5986 (mean) 953 (sd) 5973 (mean) 1089 (sd)

mpb64-positive cancer cells in all cancer cells under eachviewThe final positive rate for each slice was then calculatedas an average of the rates from the five views The overallsamplersquos mpb64 expression was deemed positive if the per-slice positive rate is greater than or equal to 5 otherwise itwas labeled asmpb64 negative

Use of bacterial culture to detect MTB and MTB-Lpart of the tissue from each resecting surgery was placedonto a sterile plate cut by sterile scissors and ground Eachhalf gram of ground tissue was then inoculated into 92-3TB and 92-3TBL liquid media mixed and incubated at37 degrees Celsius for one to three weeks The sample wasobserved once each week by taking the precipitate ontoslides for the IK acid-fast staining and the Ziehl-Neelsenstaining before microscopy observation Bacteria samplesthat were grown on 92-3TB liquid media were transferredto PNB-MTB and TCH-MTB liquid media to identify theMTB type Bacteria samples that were grown on 92-3TBLliquid media were passed to the same media (92-3TBL) fromgeneration to generation Each generation was cultured forone week until the fifth generation Meanwhile samples wereinoculated ontoPNB-MTL-L andTCH-MTB-L liquidmediaThe identification method for MTB-L was the same as forMTB above

24 Classification Model of MTB-L with Clinical End-Points

241 Generalized Linear Model (GLM) The response of thedata was calculated with following equation

Response = IK lowastMPB64 (1)

Samples with missing values were filtered out The finaldataset contained seven covariates and 99 samples (Table 2)69 of which had a positive response

All covariates were put into an initial model Nextstepwise Akaikersquos Information Criterion (AIC) was applied

to pick the strongest predictive features as well as to avoidoverfitting This procedure was repeated 1000 times andthe frequency of each variable appearing in the best modelwas used to determine the ldquoimportantrdquo features Due tothe unbalanced group sizes in each iteration we utilizedthe bootstrap resampling technique to generate 100 positivesamples and 100 negative samples as the training data Pseudo119903 square was calculated for investigating goodness-of-fit Thetraining data was predicted by the model and AUC wascalculated as a measure of model accuracy All statisticsanalyses were done with the R programming language [12]

242 Random Forest Model Random forest is an ensembleof classification trees that are calculated on random subsetsof the data using a subset of randomly restricted andselected predictors for each split in each classification treeIn this study we used the ldquopartyrdquo R package [13] to performrandom forest modeling Again as the samples were notevenly distributed we utilized bootstrap resampling to createa balanced dataset with 100 positive samples and 100 negativesamples which was then used for random forest modelingWe repeated this procedure five times to get average accuracyestimation The ldquopartyrdquo R package also provides a per-mutation accuracy variable importance measure Variableswere considered informative and important if their variableimportance values were above the absolute value of the lowestnegative-scoring variableThe rationale for this rule of thumbis that the importance of irrelevant variables varies randomlyaround zero In order to model the random feature we madetwo features by scrambling the response vector and thenrunning random forest with the two random features

3 Results

31 Development of a Reliable Detection Assay for MTBMTB-L Originally we sought to establish a reliable assay for


(a) (b)

(c) (d)

(e) (f)

Figure 1 Sample histology imagesThe scale bar equals 10 120583m in each figure (a and b)The IK staining figures with 1000 times zoomed in (a)Showing MTB-L in the nucleus of squamous cell carcinoma (see black arrows) (b) Showing MTB-L in the nucleus of lung adenocarcinoma(see black arrows) (c and d) 400 times zoomed in in situ hybridization figures (c) Showing mpb64 positive expression in the nucleus ofsquamous cell carcinoma (d) Showing mpb64 positive expression in the nucleus of lung adenocarcinoma (e and f) 200 times zoomed in insitu hybridization figures (e) Positive control showingmpb64 in tuberculosis (f) Negative control where nompb64 probes are added in lungadenocarcinoma

detecting MTB-L by assessing and optimizing existing stan-dard assays The Ziehl-Neelsen (ZN) staining had tradition-ally been used as a standard method for detecting MTB (allforms) in TB samples Recently reports have leveraged theemergence of Intensified Kinyounrsquos (IK) acid-fast stainingtechnique to detectMTB in general andmore specifically theL forms [10 14]

In the current report we first assessed our ability to detectMTB-L by the IKmethod Discriminatory detection ofMTB-L by this method has a significant histology component (seeMethods) and depends on the experience of the histologists

(Figure 1) A sample set containing 31 TB non-lung-cancersamples and 39 non-TB non-lung-cancer samples was testedNon-lung-cancer samples were used to eliminate lung canceras a potential factor affecting the detection of MTB-L intissues As shown in Figure 2(a) 2931 (935) TB non-lung-cancer samples were correctly identified as IK-positive (forMTB-L) while 3739 (949) non-TB non-lung cancer sam-ples were correctly identified as IK-negatives For referencethe ZN staining was also performed on a subset of thesesamples for confirmatory purposes with 100 agreement(results not shown) To further confirm the diagnosis of


37

2

2

29

0

25

50

75

100

Non-TB TB

IKminusIK+

Sam

ples

()

= 83e minus 13P value

(a)

36

3

0

31

(IKMPB64)minus(IKMPB64)+

Non-TB TB

0

25

50

75

100

Sam

ples

()

10e minus 13P value =

(b)

Figure 2 Assessment and optimization of MTBMTB-L detection assay 31 TB non-lung-cancer samples and 39 non-TB non-lung-cancersamples were used as the test set As the cause of TB MTB-L was expected to be present in the TB non-lung-cancer but absent in the non-TBnon-lung-cancer samples (a) Detection of MTB-L by the IK method alone (b) Detection of MTBMTB-L by the IK method and thempb64marker

MTB-L 31 TB samples were used to isolate and culturebacteria under the appropriate selection media conditionsfor MTB-L [11] All 31 TB samples were confirmed to haveMTB-L by the culture methods Conversely culture resultsfrom 16 non-TB samples that were IK-negative had a 100confirmation rate as being negative for MTB-L

We next explored novel ways to improve positive detec-tions from TB non-lung-cancer samples We reasoned thatsince the leftover samples contained MTBMTB-L in a waythat was hard to be stained by the IK method we couldmake use of genomic information from MTB to improvesensitivity Meanwhile we hypothesized that these hard-to-stain MTB samples might have unknown forms of MTBwhich had deviations from the textbook characteristics andwere worth further study As a result we complementedthe IK method with a genetic component by testing for thepresence of the mpb64 gene in the cell nucleus a markerfor MTB [11] The combination of the IK method and thempb64marker improved the true positives rate in TB samplesfor MTBMTB-L from 935 to 100 while only slightlydecreasing the true negatives rates in non-TB samples from949 to 917 (Figure 2(b)) Based on these results weadopted the combination of the IK method and the mpb64marker as the extended criteria for classifying MTBMTB-Lpresence for the remaining studies in this report while theIK method alone was the strict criteria for classifying MTB-Lpresence

32 In Patients with No TB History MTB-L Is More Prevalentin Lung Cancer Samples Compared to Other Cancer TypesA total of 187 lung cancer samples were collected for thepresent studyOf these samples 19 patients had a documented

prior medical history of TB and 113 had no reported TBhistory while the remaining 55 patients were missing thisinformation We selected the 113 samples with no reportedTB history to compare with 39 samples of other cancer typeswithout prior TB medical history As shown in Figure 3MTB-L was detected from 70 of the 113 lung cancer samples(619) a statistically significant (119875 value = 22119890 minus 16)contrast with the samples from other cancer types (2 of 3951) Among other cancer types there were one out of 14gastric cancer samples (71) and one out of six breast cancersamples (167) detected as MTB-L positive which wasstill a statistically significant contrast To increase sensitivity(Figure 4) MTBMTB-L was detected from 73 of the 113 lungcancer samples (646) which was still significantly (119875 value= 26119890 minus 15) higher than 3 of 39 samples from other cancertypes (77) Among other cancer types there were one outof 14 gastric cancer samples (71) and two out of six breastcancer samples (333) detected as MTB-L positive

To investigate the relation between MTB and breastcancer we conducted a breast cancer study Out of 115 breastcancer patients we found that 53 samples (461) wereMTB-L positive which is statistically significantly (119875 value = 0016)lower than 619 in lung cancer For MTBMTB-L 60 out or115 samples (522) were positive which was still lower than646 in lung cancer (119875 value = 0057) [15]

33The Presence of MTB-L Is Associated with a Specific Subsetof the Lung Cancer Population We hypothesized that withinthe lung cancer patients the MTB-L positive populationrepresents a homogenous subset that shares a common setof measurable clinical end-points No apparent connection


43

70

37

2

Lung cancer0

25

50

75

100

Sam

ples

()

Breast cancerOther cancer types Gastric cancer

5

1 1

13

No TB No TB No TB No TB

MTB-Lminus

MTB-L+

Figure 3 Higher presence of MTB-L in the lung cancer samples compared to samples of other cancer types by the IK method alone

40

73

36

3

4

2

1

13

Lung cancer Breast cancerOther cancer types Gastric cancerNo TB No TB No TB No TB

MTB-Lminus

MTB-L+

0

25

50

75

100

Sam

ples

()

Figure 4 Higher presence of MTBMTB-L in the lung cancer samples compared to samples of other cancer types by both the IK methodand thempb64marker

however could be found between the presence ofMTB-L andindividual clinical end-points in isolation (Table 3)

We next applied a generalized linear regression modelas a representative of linear models to examine potentialcombinatorial effects of all clinical end-points The finalmodel identified category and lymph node metastasis ashaving the strongest combined effects (Table 4) The strongpredictive power of the model (AUC = 070) elucidated thenecessity of examining the combinatorial effect of the fourclinical features The suboptimal 119903-squared value (01) how-ever indicated that a linear model alone is likely insufficientespecially without explicit inclusion of critical interactionterms

We then removed the assumption that the associationis strictly linear and derived a random forest model as arepresentative of nonlinear models to reexamine the asso-ciation Again random forest model was run on a balanceddataset through bootstrapping We repeated the procedurefive times and the average AUC was 085 (0850 0845 08500885 and 0830) We used a standard R package to runrandom forest model which should be replicable Figure 5was one example of the tree structureThe feature importance

ranking agreed with our linear model and selected the mostdominant features (category pathology grade lymph nodemetastasis and clinical stage) Age was significant as well Bycomparing the linear and nonlinear models we found thatnonlinear models performed better This was consistent withour hypothesis that features are nonlinearly associated witheach other in terms of predicting outcomes

4 Discussion

The relatively high rates of lung cancer and TB cases inChina have recently sparked new theories that there might becross-associations between the two diseases Recent reportsof colocalization ofMTBMTB-L in tumor samples althoughlimited in sample size and coincidental in nature underlinethe need for thorough confirmation studies Towards thisgoal we set up the present study with three key componentsin mind (1) accurate detection assay (2) large sample sizeand (3) full medical history and clinical diagnosis to controlfor confounding factors as well as linkage to clinical end-points


Table 3 IK as response 112 samples positive negative = 77 35


Category 02246









Metastasisp lt 0001

1

0 1Stage

p = 0451

2

1 0Genderp = 0403

3

Female Malen = 14

y = (0643 0357)

4Categoryp = 0245

5

LAC SCLC SCCn = 11

y = (0909 0091)

6n = 11

y = (0636 0364)

7

Categoryp lt 0001

8

BAC LAC SCLC SCC

n = 21y = (0952 0048)

9n = 15

y = (02 08)

10

Gradep = 0048

11

3 1 2Genderp = 0013

12

Male FemaleSize

p = 0009

13

1 0n = 13

y = (0615 0385)

14n = 7

y = (0 1)

15

n = 13y = (0846 0154)

16

Categoryp = 0006

17

LAC SCLC BAC SCCCategoryp = 0188

18

SCLC LACn = 9

y = (0667 0333)

19Size

p = 0107

20

0 1n = 9

y = (0667 0333)

21Age

p = 0094

22

le16 gt16n = 8

y = (0 1)

23Age

p = 093

24

le23 gt23n = 19

y = (0526 0474)

25n = 11

y = (0364 0636)

26

Agep = 0047

27

le8 gt8n = 9

y = (0444 0556)

28Genderp = 0078

29

Female Malen = 18

y = (0 1)

30n = 12

y = (0167 0833)

31

Figure 5 One example of decision trees from the random forest model In the annotation metastasis means lymph node metastasis grademeans pathology grade stage means clinical stage size means tumor size

Table 4 Feature frequency table of generalized linear model

Covariable name Frequency ()Category 986Gender 396Pathology grade 621Lymph node metastasis 939Tumor size 658Clinical stage 543

Acid-fast staining methods are the traditional way forMTB andMTB-L detection and isolation is the gold standardfor validation However we noticed that staining methodsfailed to detect MTB or MTB-L in some MTB-L positivesamples that were validated by isolation We hypothesizedthat there might be a group of MTB which is hard to bestained because of unknown characteristics In this situationbecause this group should still contain the main componentsof MTB we could make use of an important MTB proteincoding gene (mpb64) for detection In this study we provedthat the IK method is better than the ZN method but thempb64 in situ hybridization method can further increasesensitivity By combining the IK method and the mpb64 in

situhybridizationmethod we could find out samples that hadMTB but in a hard-to-stain form which is worth future study

We performedMTBMTB-L detection on all 187 samplesisolated from lung cancer patients but decided to concentrateon the 113 samples that had no history of TB It was critical toobtain samples from other cancer types that were also free ofprior TB history By leveraging the available medical recordswe were able to remove TB history as a potential confoundingfactormdashan important step in the subsequent interpretationof MTB-L presence Central to our key findings 619 ofthe lung cancer samples were found to be MTB-L positivea statistically significant higher percentage than that of theother cancer samples (51) or that of gastric cancer sample(71) see Figure 3 For breast cancer we had only 6 sampleswhich were not enough to make statistical conclusion Thetwo positive samples were both validated to have MTB-L byisolation method This fact implied that MTBMTB-L mightalso have associationwith breast cancer whichwas confirmedby our breast cancer study [15]

This finding suggested that the presence of MTB-L mightbe strongly associated with lung cancer but the causalrelationship was underdetermined Because MTB-L are wall-defective and pleomorphic they have the potential to actlike carcinogenic viruses They can get into human alveolarepithelial cells remain dormant and induce cancer by DNA


integration because DNA integration can potentially activateoncogenes andor repress tumor suppressor genes In mousemodel researchers reported that MTB-L infected mousehad significant higher chance to develop neoplasia than thecontrol group [16 17] There are well-known viruses thatcan lead to cancer for example human papilloma viruseshepatitis B and C viruses and so forth However evenfor these viruses host-pathogen adaptation and biologicalmechanism of tumor causality are still unclear Similarly theinteraction between host and MTB-L is not fully understoodyet

While the biological mechanism of causality is stillunclear we acknowledged that preclinical experimentsbeyond the scope of the present report were needed tofully investigate the question of causality Nevertheless wereasoned that a causal relationship would also reflect anassociation with clinical disease severity

Towards this goal we focused on finding end-pointdifferences betweenMTB-L positive and negative lung cancersamples None of the individual end-points in isolation wassufficient to distinguish between the positive and negativesamples However upon applying computational classifica-tion algorithms we were able to identify a set of clinical end-points that could be used to stratify between the samplesTo our knowledge this was the first report linking clinicalend-points to the presence and absence of MTB-L As ourrule-based model indicated MTB-L was especially prevalentin patients with non-lymph-node-metastatic small-cell lungand squamous cell carcinoma Our findings should lead tofurther studies focusing on this subpopulation as well asmaking a strong case for the advantage of utilizing primarycells from this subpopulation as an experimental modelsystem

Key Points

A reliable detection assay forMTBMTB-Lwas devel-opedMTB-L is more prevalent in lung cancer samplescompared to other cancer typesA set of clinical end-points which are strongly asso-ciated with MTB-L presence was identified

Conflict of Interests

The authors declare that there is no conflict of interests

References

[1] WHO ldquoCancerrdquo 2014[2] J She P Yang Q Hong and C Bai ldquoLung cancer in China

challenges and interventionsrdquo Chest vol 143 no 4 pp 1117ndash1126 2013

[3] K J Ryan andCGRay SherrisMedicalMicrobiologyMcGraw-Hill 2004

[4] J-C Camus M J Pryor C Medigue and S T Cole ldquoRe-annotation of the genome sequence ofMycobacterium tubercu-losisH37RvrdquoMicrobiology vol 148 no 10 pp 2967ndash2973 2002

[5] P R Murray K S Rosenthal and M A Pfaller MedicalMicrobiology Mosby Elsevier 2005

[6] R Skolnik Global Health 101 Jones amp Bartlett LearningBurlington Mass USA 2011

[7] G Huang and T Lin ldquoMycobacterium tuberculosis L-formsrdquoMicrobial Ecology inHealth andDisease vol 10 no 3-4 pp 129ndash133 1999

[8] Y Tamada S Kanda A Yoshidome I Hayashi MMiyake andT Nishiyama ldquoDiagnosis of active tuberculosis using MPB64a specific antigen of Mycobacterium bovisrdquo Microbiology andImmunology vol 56 no 11 pp 740ndash747 2012

[9] M Harboe S Nagai M E Patarroyo M L Torres C Ramirezand N Cruz ldquoProperties of proteins MPB64 MPB70 andMPB80 ofMycobacterium bovis BCGrdquo Infection and Immunityvol 52 no 1 pp 293ndash302 1986

[10] Y Tian S Zhang X Cui and J Wang ldquoDetection of Mycobac-terium tuberculosis and its L-forms in peripheral blood fromtuberculosis and lung cancer patientsrdquo Chinese Journal OfLaboratory Medicine vol 31 no 12 pp R5ndashR73 2008

[11] Y S Tian X K Cui T Hao H G Li W Zhang and J Y WangldquoStudy on the relationship betweenMycobacterium tuberculosisL infection and lung cancerrdquo Tumor vol 29 pp 1085ndash10892009

[12] R Development Core Team R A Language and Environmentfor Statistical Computing vol 1 R Foundation for StatisticalComputing 2011 httpwwwr-projectorg

[13] T Hothorn K Hornik and A Zeileis ldquoparty A Laboratoryfor Recursive Part (y) itioning R Packag version 09-0rdquo 2006httpscranr-projectorgwebpackagespartyvignettespartypdf

[14] D Zhao X-M Yang Q-Y Chen X-S Zhang C-J Guoand X-Y Che ldquoA modified acid-fast staining method forrapid detection of Mycobacterium tuberculosisrdquo Journal ofMicrobiological Methods vol 91 no 1 pp 128ndash132 2012

[15] C Bao T Yansheng C Xingkun et al ldquoDetection ofMycobac-terium tuberculosis L-forms and MPB64 in breast cancer tis-suesrdquoThe Journal of Practical Medicine vol 29 no 15 2013

[16] D Yunhai L Tefu Y Min H Jie and Z Shifu ldquoStudyon pathogenicity and carcinogenicity using C57BL6N mouseinfected by Mycobacterium tuberculosis L-formsrdquo LaboratoryMedicine no z1 pp 26ndash27 2001

[17] Z Mingli W Chaofu Z Shifu et al ldquoStudy onMycobacteriumtuberculosis L-formsrsquo vertical transmission and its impact onoffspring in mouse modelrdquo Chinese Journal of Zoonoses no 1pp 39ndash42 2004

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of


Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology


Molecular Biology International

GenomicsInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


BioinformaticsAdvances in

Marine BiologyJournal of



Signal TransductionJournal of


BioMed Research International

Evolutionary BiologyInternational Journal of



Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Genetics Research International


Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational



Enzyme Research



Microbiology


Table 1 Patient characteristics

Characteristic Values Lung cancer TB non-lung-cancer Non-TB non-lung-cancerEligible patients Total number 187 31 39CategoryWHO (lung cancerhistologicalclassification) of 2004

Squamous cell carcinoma (SCC) 63 NA NALung adenocarcinoma (LAC) 77 NA NA

Bronchioloalveolar carcinoma (BAC) 27 NA NASmall cell lung carcinoma (SCLC) 20 NA NA

Clinical stage (UCC1997)

I II 61 NA NAIII IV 70 NA NA

Tumor size le3 cm 56 NA NAgt3 cm 75 NA NA

Lymph nodemetastasis

Yes 89 NA NANo 42 NA NA

History of TB Yes 19 31 NANo 112 0 NA

Gender Male 80 26 14Female 107 5 27

Pathology gradeI 22 NA NAII 60 NA NAIII 63 NA NA

Age 593 (mean) 103 (sd) 534 (mean) 91 (sd) 499 (mean) 76 (sd)

rapidly developed for MTB (including MTB-L) detection Inthis paper in order to improve the sensitivity of MTB andMTB-L detection probes targeting gene mpb64 are used inin situ hybridization Gene mpb64 is in the MTB genomeand its MPB64 protein is identified from MTB strain H37Rv[8 9] Although MTB-L cannot grow well in media forroutine examination of tuberculosis media culture remainsthe standard way to detect and isolate the infection However92-3TB-L media were reported to be suitable for the growthof both MTB vegetative forms and L-forms [7] In our labwe developed a standardized protocol of using 92-3TB and92-3TB-L media to maximize the isolation rate of L forms[10 11]

Currently there have been limited investigations on therelationship between lung cancer and MTB-L Our lab is akey pioneer in this field and published two primary studies Inthese two studies we showed thatMTB-L exist in lung cancerpatients as the primary type of MTB Unfortunately we wereunable to explain any of the tumorigenic mechanisms yet [1011]

In this study we examined 187 lung cancer tissues forMTBMTB-L presence by combining IK acid-fast stainingand in situ hybridization using mpb64 probes We comparedthe rate ofMTBMTB-L positive cases in lung cancer with therate among other cancer types By computational methodswe were able to extract key clinical features that can char-acterize a subpopulation of lung cancer samples with higherprevalence of MTBMTB-L

2 Materials and Methods

21 Samples Table 1 was the summary of clinical charac-teristics of all samples used in this study There were threemain groups of samples (1) lung cancer samples which wereused to study the association betweenMTBMTB-L and lung

cancer (2) tuberculosis group without cancer (TB non-lung-cancer) which was used as the positive control to estimatethe sensitivity of the MTBMTB-L detection assay and (3)a group of samples without TB (non-TB non-lung cancer)including hepatic gastric and breast cancers was used as thenegative control to estimate the specificity for theMTBMTB-L detection assay

The lung cancer samples were from 187 patients whowere diagnosed with lung cancer at the North China OilfieldHospital of Hebei Medical University Here they had surgeryto remove the cancer from the lung between 2003 and 2012A consensus cancer diagnosis was reached by pathology For131 out of 187 patients complete pathological records wereavailable For 47 out of 131 patients fresh surgery tissues wereavailable Within the 19 patients with history of TB sixteenpatients had ipsilateral tuberculosis and cancer while anotherthree had contralateral disease

For the TB non-lung-cancer group samples were from31 patients who had surgery from the North China OilfieldHospital of Hebei Medical University between 2004 and 2012(14 out of 31 patients had fresh surgery tissues) Amongthe 31 patients 19 had tuberculosis and 12 had tuberculouslymphadenitis

For the non-TB non-lung-cancer control group sampleswere from 39 hospitalized surgical patients in the NorthChina Oilfield Hospital of Hebei Medical University between2004 and 2012 Each patient went through a chest X-rayCT and OT tests that excluded the possibility of patients forhaving tuberculosis or lung cancer 17 out of 39 patients hadfresh surgery tissues Among the 39 patients 6 had hepaticcarcinoma 14 had gastric cancer and 19 had breast cancer

The research protocol was approved by ethic committeesof the North China Oilfield Hospital of Hebei MedicalUniversity where the samples were collected and all patientsprovided informed consent All specimens were handled


















Category 00832


















3 Results



(a) (b)

(c) (d)

(e) (f)






37

2

2

29

0

25

50

75

100

Non-TB TB

IKminusIK+

Sam

ples

()


(a)

36

3

0

31


Non-TB TB

0

25

50

75

100

Sam

ples

()


(b)









43

70

37

2

Lung cancer0

25

50

75

100

Sam

ples

()


5

1 1

13


MTB-Lminus

MTB-L+


40

73

36

3

4

2

1

13


MTB-Lminus

MTB-L+

0

25

50

75

100

Sam

ples

()






4 Discussion





Category 02246









Metastasisp lt 0001

1

0 1Stage

p = 0451

2

1 0Genderp = 0403

3

Female Malen = 14

y = (0643 0357)

4Categoryp = 0245

5

LAC SCLC SCCn = 11

y = (0909 0091)

6n = 11

y = (0636 0364)

7

Categoryp lt 0001

8

BAC LAC SCLC SCC

n = 21y = (0952 0048)

9n = 15

y = (02 08)

10

Gradep = 0048

11

3 1 2Genderp = 0013

12

Male FemaleSize

p = 0009

13

1 0n = 13

y = (0615 0385)

14n = 7

y = (0 1)

15

n = 13y = (0846 0154)

16

Categoryp = 0006

17


18

SCLC LACn = 9

y = (0667 0333)

19Size

p = 0107

20

0 1n = 9

y = (0667 0333)

21Age

p = 0094

22

le16 gt16n = 8

y = (0 1)

23Age

p = 093

24

le23 gt23n = 19

y = (0526 0474)

25n = 11

y = (0364 0636)

26

Agep = 0047

27

le8 gt8n = 9

y = (0444 0556)

28Genderp = 0078

29

Female Malen = 18

y = (0 1)

30n = 12

y = (0167 0833)

31












Key Points




References

























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


















Category 00832


















3 Results



(a) (b)

(c) (d)

(e) (f)






37

2

2

29

0

25

50

75

100

Non-TB TB

IKminusIK+

Sam

ples

()


(a)

36

3

0

31


Non-TB TB

0

25

50

75

100

Sam

ples

()


(b)









43

70

37

2

Lung cancer0

25

50

75

100

Sam

ples

()


5

1 1

13


MTB-Lminus

MTB-L+


40

73

36

3

4

2

1

13


MTB-Lminus

MTB-L+

0

25

50

75

100

Sam

ples

()






4 Discussion





Category 02246









Metastasisp lt 0001

1

0 1Stage

p = 0451

2

1 0Genderp = 0403

3

Female Malen = 14

y = (0643 0357)

4Categoryp = 0245

5

LAC SCLC SCCn = 11

y = (0909 0091)

6n = 11

y = (0636 0364)

7

Categoryp lt 0001

8

BAC LAC SCLC SCC

n = 21y = (0952 0048)

9n = 15

y = (02 08)

10

Gradep = 0048

11

3 1 2Genderp = 0013

12

Male FemaleSize

p = 0009

13

1 0n = 13

y = (0615 0385)

14n = 7

y = (0 1)

15

n = 13y = (0846 0154)

16

Categoryp = 0006

17


18

SCLC LACn = 9

y = (0667 0333)

19Size

p = 0107

20

0 1n = 9

y = (0667 0333)

21Age

p = 0094

22

le16 gt16n = 8

y = (0 1)

23Age

p = 093

24

le23 gt23n = 19

y = (0526 0474)

25n = 11

y = (0364 0636)

26

Agep = 0047

27

le8 gt8n = 9

y = (0444 0556)

28Genderp = 0078

29

Female Malen = 18

y = (0 1)

30n = 12

y = (0167 0833)

31












Key Points




References

























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology




Category 00832


















3 Results



(a) (b)

(c) (d)

(e) (f)






37

2

2

29

0

25

50

75

100

Non-TB TB

IKminusIK+

Sam

ples

()


(a)

36

3

0

31


Non-TB TB

0

25

50

75

100

Sam

ples

()


(b)









43

70

37

2

Lung cancer0

25

50

75

100

Sam

ples

()


5

1 1

13


MTB-Lminus

MTB-L+


40

73

36

3

4

2

1

13


MTB-Lminus

MTB-L+

0

25

50

75

100

Sam

ples

()






4 Discussion





Category 02246









Metastasisp lt 0001

1

0 1Stage

p = 0451

2

1 0Genderp = 0403

3

Female Malen = 14

y = (0643 0357)

4Categoryp = 0245

5

LAC SCLC SCCn = 11

y = (0909 0091)

6n = 11

y = (0636 0364)

7

Categoryp lt 0001

8

BAC LAC SCLC SCC

n = 21y = (0952 0048)

9n = 15

y = (02 08)

10

Gradep = 0048

11

3 1 2Genderp = 0013

12

Male FemaleSize

p = 0009

13

1 0n = 13

y = (0615 0385)

14n = 7

y = (0 1)

15

n = 13y = (0846 0154)

16

Categoryp = 0006

17


18

SCLC LACn = 9

y = (0667 0333)

19Size

p = 0107

20

0 1n = 9

y = (0667 0333)

21Age

p = 0094

22

le16 gt16n = 8

y = (0 1)

23Age

p = 093

24

le23 gt23n = 19

y = (0526 0474)

25n = 11

y = (0364 0636)

26

Agep = 0047

27

le8 gt8n = 9

y = (0444 0556)

28Genderp = 0078

29

Female Malen = 18

y = (0 1)

30n = 12

y = (0167 0833)

31












Key Points




References

























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


(a) (b)

(c) (d)

(e) (f)






37

2

2

29

0

25

50

75

100

Non-TB TB

IKminusIK+

Sam

ples

()


(a)

36

3

0

31


Non-TB TB

0

25

50

75

100

Sam

ples

()


(b)









43

70

37

2

Lung cancer0

25

50

75

100

Sam

ples

()


5

1 1

13


MTB-Lminus

MTB-L+


40

73

36

3

4

2

1

13


MTB-Lminus

MTB-L+

0

25

50

75

100

Sam

ples

()






4 Discussion





Category 02246









Metastasisp lt 0001

1

0 1Stage

p = 0451

2

1 0Genderp = 0403

3

Female Malen = 14

y = (0643 0357)

4Categoryp = 0245

5

LAC SCLC SCCn = 11

y = (0909 0091)

6n = 11

y = (0636 0364)

7

Categoryp lt 0001

8

BAC LAC SCLC SCC

n = 21y = (0952 0048)

9n = 15

y = (02 08)

10

Gradep = 0048

11

3 1 2Genderp = 0013

12

Male FemaleSize

p = 0009

13

1 0n = 13

y = (0615 0385)

14n = 7

y = (0 1)

15

n = 13y = (0846 0154)

16

Categoryp = 0006

17


18

SCLC LACn = 9

y = (0667 0333)

19Size

p = 0107

20

0 1n = 9

y = (0667 0333)

21Age

p = 0094

22

le16 gt16n = 8

y = (0 1)

23Age

p = 093

24

le23 gt23n = 19

y = (0526 0474)

25n = 11

y = (0364 0636)

26

Agep = 0047

27

le8 gt8n = 9

y = (0444 0556)

28Genderp = 0078

29

Female Malen = 18

y = (0 1)

30n = 12

y = (0167 0833)

31












Key Points




References

























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


37

2

2

29

0

25

50

75

100

Non-TB TB

IKminusIK+

Sam

ples

()


(a)

36

3

0

31


Non-TB TB

0

25

50

75

100

Sam

ples

()


(b)









43

70

37

2

Lung cancer0

25

50

75

100

Sam

ples

()


5

1 1

13


MTB-Lminus

MTB-L+


40

73

36

3

4

2

1

13


MTB-Lminus

MTB-L+

0

25

50

75

100

Sam

ples

()






4 Discussion





Category 02246









Metastasisp lt 0001

1

0 1Stage

p = 0451

2

1 0Genderp = 0403

3

Female Malen = 14

y = (0643 0357)

4Categoryp = 0245

5

LAC SCLC SCCn = 11

y = (0909 0091)

6n = 11

y = (0636 0364)

7

Categoryp lt 0001

8

BAC LAC SCLC SCC

n = 21y = (0952 0048)

9n = 15

y = (02 08)

10

Gradep = 0048

11

3 1 2Genderp = 0013

12

Male FemaleSize

p = 0009

13

1 0n = 13

y = (0615 0385)

14n = 7

y = (0 1)

15

n = 13y = (0846 0154)

16

Categoryp = 0006

17


18

SCLC LACn = 9

y = (0667 0333)

19Size

p = 0107

20

0 1n = 9

y = (0667 0333)

21Age

p = 0094

22

le16 gt16n = 8

y = (0 1)

23Age

p = 093

24

le23 gt23n = 19

y = (0526 0474)

25n = 11

y = (0364 0636)

26

Agep = 0047

27

le8 gt8n = 9

y = (0444 0556)

28Genderp = 0078

29

Female Malen = 18

y = (0 1)

30n = 12

y = (0167 0833)

31












Key Points




References

























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


43

70

37

2

Lung cancer0

25

50

75

100

Sam

ples

()


5

1 1

13


MTB-Lminus

MTB-L+


40

73

36

3

4

2

1

13


MTB-Lminus

MTB-L+

0

25

50

75

100

Sam

ples

()






4 Discussion





Category 02246









Metastasisp lt 0001

1

0 1Stage

p = 0451

2

1 0Genderp = 0403

3

Female Malen = 14

y = (0643 0357)

4Categoryp = 0245

5

LAC SCLC SCCn = 11

y = (0909 0091)

6n = 11

y = (0636 0364)

7

Categoryp lt 0001

8

BAC LAC SCLC SCC

n = 21y = (0952 0048)

9n = 15

y = (02 08)

10

Gradep = 0048

11

3 1 2Genderp = 0013

12

Male FemaleSize

p = 0009

13

1 0n = 13

y = (0615 0385)

14n = 7

y = (0 1)

15

n = 13y = (0846 0154)

16

Categoryp = 0006

17


18

SCLC LACn = 9

y = (0667 0333)

19Size

p = 0107

20

0 1n = 9

y = (0667 0333)

21Age

p = 0094

22

le16 gt16n = 8

y = (0 1)

23Age

p = 093

24

le23 gt23n = 19

y = (0526 0474)

25n = 11

y = (0364 0636)

26

Agep = 0047

27

le8 gt8n = 9

y = (0444 0556)

28Genderp = 0078

29

Female Malen = 18

y = (0 1)

30n = 12

y = (0167 0833)

31












Key Points




References

























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology




Category 02246









Metastasisp lt 0001

1

0 1Stage

p = 0451

2

1 0Genderp = 0403

3

Female Malen = 14

y = (0643 0357)

4Categoryp = 0245

5

LAC SCLC SCCn = 11

y = (0909 0091)

6n = 11

y = (0636 0364)

7

Categoryp lt 0001

8

BAC LAC SCLC SCC

n = 21y = (0952 0048)

9n = 15

y = (02 08)

10

Gradep = 0048

11

3 1 2Genderp = 0013

12

Male FemaleSize

p = 0009

13

1 0n = 13

y = (0615 0385)

14n = 7

y = (0 1)

15

n = 13y = (0846 0154)

16

Categoryp = 0006

17


18

SCLC LACn = 9

y = (0667 0333)

19Size

p = 0107

20

0 1n = 9

y = (0667 0333)

21Age

p = 0094

22

le16 gt16n = 8

y = (0 1)

23Age

p = 093

24

le23 gt23n = 19

y = (0526 0474)

25n = 11

y = (0364 0636)

26

Agep = 0047

27

le8 gt8n = 9

y = (0444 0556)

28Genderp = 0078

29

Female Malen = 18

y = (0 1)

30n = 12

y = (0167 0833)

31












Key Points




References

























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology





Key Points




References

























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology








Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

Documents

Research Article Clinical End-Points Associated with ...downloads.hindawi.com/journals/bmri/2015/827829.pdfResearch Article Clinical End-Points Associated with Mycobacterium tuberculosis